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ChatGPT vs. Claude vs. Google Gemini Models: Full Report and Comparison of Features, Capabilities, Pricing, and more (September 2025 Update)

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As of September 2025, the generative AI ecosystem has entered a new phase of maturity, defined by larger context windows, more adaptive reasoning, multimodal capabilities, and highly specialized tiered models. OpenAI, Anthropic, and Google now lead the market with fundamentally different philosophies on performance, personalization, safety, and pricing. Below is a comprehensive introduction summarizing the key updates and differentiators for each model family and aspect.



1. OpenAI’s GPT series (GPT-5, GPT-4.1, GPT-4o, GPT-3.5)

OpenAI now runs a multi-tiered model ecosystem serving both casual users and advanced developers.

  • GPT-5 (flagship)

    • Unified architecture: First OpenAI model with a dual-mode reasoning router. It automatically decides whether to use a lightweight core model or a deep “Thinking” mode for complex tasks.

    • Performance leap: SOTA on coding, math, science, and multimodal benchmarks, delivering expert-level reasoning and better factual accuracy.

    • Custom personalities: First OpenAI model with built-in tone presets and enhanced persistent instructions for personalized interaction.

    • Multimodality: Full support for text, vision, and audio; seamless switching between written, spoken, and image-based tasks.

    • GPT-5 Pro tier: Offers even deeper reasoning chains, 22% fewer major errors, and is now the highest-scoring OpenAI model for scientific and technical tasks.

  • GPT-4.1

    • Designed for developer integration and professional workflows.

    • 1M-token context window for entire books or repositories in one prompt.

    • Exceptional in multi-file coding and agentic tool use, scoring 54.6% SWE-Bench Verified, far above GPT-4o’s 33%.

    • Includes Mini and Nano variants optimized for low latency and cost while maintaining GPT-4-level intelligence.

  • GPT-4o ("Omni")

    • First fully multimodal GPT trained end-to-end on text, images, and audio.

    • Known for its conversational warmth and human-like creativity, it introduced real-time voice interaction (~0.32s latency).

    • While benchmark performance trails GPT-4.1, GPT-4o remains highly valued by users for role-play, emotional engagement, and artistic writing.

  • GPT-3.5 (“o3”)

    • Now mostly legacy, powering the free ChatGPT tier.

    • Fast and cost-efficient but limited in reasoning, coding, and long-context processing (4K–16K context vs. GPT-5’s dynamic scaling).



2. Anthropic’s Claude family (Claude 4 and Claude 4.1)

Anthropic continues to distinguish itself with agent-oriented models, optimized for long-horizon tasks, tool integration, and safety.

  • Claude 4 (Opus & Sonnet)

    • Opus 4: Flagship model excelling in multi-file coding, topping the SWE-Bench Verified leaderboard at 72.5%. Designed for extended autonomous tasks, Claude can sustain multi-hour reasoning loops without drifting off-track.

    • Sonnet 4: Faster, more efficient, and widely accessible on Claude.ai, offering near-Opus capabilities at a fraction of the cost.

    • Built-in “memory files” allow Claude to create and reuse structured notes during long reasoning sessions, effectively extending beyond its 100K+ context.

  • Claude 4.1 (Opus 4.1)

    • Incremental upgrade delivering 74.5% SWE-Bench, the highest coding score among all publicly benchmarked models in 2025.

    • Significantly better state-tracking for agent workflows, making it reliable in managing multi-step, tool-driven tasks.

    • Enhanced safety: Claude 4.1 achieves a 98.76% safe-response rate and reduces risky compliance by ~25%, reinforcing Anthropic’s positioning for enterprise deployments.

    • Primarily aimed at developers, research assistants, and AI agent platforms requiring precision and stability.



3. Google Gemini 2.5 (Flash and Pro)

Google has doubled down on multimodality, context scale, and tight integration with its cloud ecosystem, making Gemini a strategic pillar across Google products.

  • Gemini 2.5 Flash

    • Built for speed and affordability, Flash is Google’s first “thinking-enabled” Gemini, offering visible chain-of-thought reasoning for transparency.

    • Multimodal inputs: Accepts text, images, audio, video, and code in a single query. Includes Flash Image (generation & editing) and Flash Audio (natural expressive voice interaction).

    • Performance optimized for real-time tasks: Flash tops the WebDev Arena coding leaderboard and achieves high accuracy with significantly lower latency than Pro.

    • Cost-effective at $0.30/M input and $2.50/M output, making it a strong choice for high-volume deployments.

  • Gemini 2.5 Pro

    • Google’s flagship reasoning powerhouse, optimized for enterprise-level analysis, coding, and multimodal understanding.

    • SOTA on math and science benchmarks: #1 on LMArena human preference rankings and 18.8% on Humanity’s Last Exam, outperforming GPT-4-class peers.

    • 1M+ token context window, extendable to 2M, enabling entire datasets or books to be processed in one pass.

    • Excels at agentic coding tasks with 63.8% SWE-Bench and integrates tightly with Vertex AI, Google Workspace, and its knowledge graph.

    • Slightly more expensive than GPT-4.1 but positioned as a developer-first, cloud-native model with superior integration and retrieval capabilities.



4. Key takeaways across the ecosystem

  • Multimodality leadership:

    • GPT-5 inherits GPT-4o’s native audio+vision pipeline.

    • Gemini Flash & Pro push full-spectrum multimodal integration, extending to image editing, video processing, and expressive voice outputs.

    • Claude remains text-first, relying on external APIs for vision/audio.

  • Reasoning & coding performance:

    • Claude 4.1 leads raw coding accuracy (74.5% SWE-Bench).

    • GPT-5 Pro dominates broader math, science, and mixed reasoning domains, with strong full-stack coding support.

    • Gemini 2.5 Pro specializes in structured agentic reasoning, aided by Google’s retrieval systems, while Flash balances speed and everyday coding capability.

  • Context scale & memory:

    • Gemini Pro & Flash: Up to 1M tokens, planned 2M support.

    • GPT-4.1: 1M tokens via API; GPT-5 expected similar or better.

    • Claude 4 & 4.1: ~100K tokens but enhanced with “memory files” that effectively extend usable recall far beyond window size.

  • Pricing & accessibility:

    • Gemini Flash dominates in price-performance for high-volume apps.

    • GPT-5 Plus and Gemini Advanced compete at consumer level; Claude Sonnet offers a free tier for casual use.

    • Enterprise pricing diverges: GPT-5 Pro > Gemini Pro > Claude Opus ≈ Claude 4.1 > GPT-4.1 > Flash.

  • Enterprise integration:

    • OpenAI consolidates ChatGPT + plugins + agents under GPT-5 with seamless routing.

    • Gemini models integrate deeply with Google Workspace, Vertex AI, and Android.

    • Claude focuses on autonomous workflows and multi-hour agent tasks, especially via Claude Code IDE plugins and Bedrock/Vertex AI deployment options.


Summary of Key Differences

Below is a high-level comparison of the latest OpenAI GPT series, Anthropic Claude models, and Google Gemini models as of September 2025:

Feature

GPT‑5 (OpenAI)

GPT‑4.1 (OpenAI)

GPT‑4o (OpenAI)

GPT‑3.5 (o3, OpenAI)

Claude 4 (Anthropic)

Claude 4.1 (Anthropic)

Gemini 2.5 Flash (Google)

Gemini 2.5 Pro (Google)

General Capabilities

Unified dual-mode reasoning (fast vs. “Thinking” mode) with expert-level answers. Top-tier in coding, math, writing & health. Significantly reduced hallucinations and more “safe-completion” style responses.

Strong instruction-following and coding specialist. Huge 1M-token context window for long documents. Outperforms GPT-4o in coding & reasoning tasks. API-only model family (Standard, Mini, Nano) with focus on professional use cases.

Omnimodal GPT-4 model (“4o” for “omni”) trained on text, vision & audio together. Extremely conversational with nuanced emotional understanding; set the standard for creative, warm dialogue. 128K context; fluid real-time voice chat (~0.32s latency). Default ChatGPT model (May 2024–Aug 2025) beloved for its personality.

Legacy GPT-3.5 model powering early ChatGPT. Good general conversational ability but limited complex reasoning. ~4K–16K context. Much lower knowledge and reasoning depth (e.g. ~70% MMLU vs ~86% for GPT-4). Very fast and cheap; backbone of ChatGPT free tier.

Claude Opus 4 – Anthropic’s flagship with sustained long reasoning. Excels at complex coding and multi-hour tasks. Can use tools (web search, code exec) in “extended thinking” mode. Huge context (100K+ tokens) and improved long-term memory via “memory files” when allowed. Sonnet 4 variant offers faster, instant responses for everyday use.

Claude Opus 4.1 – Iterative upgrade with better multi-file coding reliability and reasoning continuity. Further improved refactoring and state-tracking on long agent sessions. Leads in harmlessness (98.76% safe-response rate) and reduced misuse cooperation. Minor performance bump (~+2% on coding benchmark) over Claude 4.

Gemini 2.5 Flash – Google’s fast, cost-effective “thinking” model for everyday tasks. First Gemini Flash model with visible step-by-step reasoning (“thinking tokens”). Multimodal inputs (text, code, images, audio, video) with 1M-token context. Variants: standard Flash, Flash Image (generative image & editing), and Flash Audio (voice dialog) for rich media responses. Optimized for low latency and high throughput.

Gemini 2.5 Pro – Google’s most advanced reasoning model. Excels at complex problem-solving, large data analysis, and coding agents. Multimodal understanding across text, images, audio, video, and entire codebases. Uses extensive chain-of-thought reasoning; tops human preference leaderboard (LMArena) by a wide margin. 1M+ token context (2M planned) for deep analyses.

Benchmark Performance

State-of-the-art on most benchmarks: e.g. near-record MMLU (~90%+ general knowledge) and highest GPQA science QA score. Best-in-class on complex math (AIME) and coding challenges. External evals show GPT-5 answers preferred over GPT-4 series ~68% of the time on hard prompts.

Excellent performance: 90.2% MMLU, 66.3% GPQA (science). Coding: 54.6% on SWE-Bench Verified (real software tasks) – a large jump over GPT-4o’s 33.2%. Supports up to 1e6-token inputs without quality drop. GPT-4.1 Mini/Nano match GPT-4o-level intelligence at far lower latency.

Strong generalist but now surpassed by newer models. ~85.7% MMLU. Not tuned for coding to the same degree (33% on SWE-Bench), but excelled in creative writing and high-context conversations. Many users found GPT-4o’s answers more “human-like” despite lower raw scores.

Moderately capable. ~70% MMLU (far below GPT-4), modest coding ability (e.g. ~48% HumanEval in Python). Effective for casual Q&A and writing assistance, but struggles with complex tasks (often requiring multiple prompts). Serves as a baseline for fine-tuning and as ChatGPT’s free model until 2025.

Cutting-edge coding leader: Claude 4 scored 72.5% on SWE-Bench (best at launch). Also strong on knowledge (topping many academic benchmarks at time of release). Claude models known for long-form quality – able to maintain coherence over very long essays or dialogues. Emphasizes reliable follow-through on multi-step tasks (e.g. ran 7-hour autonomous coding job successfully).

New high in coding: 74.5% SWE-Bench (slightly above Claude 4). Robust on reasoning: internal evals equated Claude 4.1’s jump as one std dev improvement over Claude 4. Continues to excel at long-horizon tasks and complex tool use (TAU benchmark for agents). Safety gains: ~25% less likely to follow harmful requests than Claude 4.

Very strong overall. In coding, Flash is competitive: e.g. tops WebDev Arena coding leaderboard (a popular web development benchmark). It balances speed with good accuracy on academic tests. Flash model can use “thinking budgets” to trade off accuracy vs. cost. Generally trails the larger Pro model on difficult benchmarks, but far outperforms smaller LLMs, with state-of-the-art results in its class.

State-of-the-art on many evals. Leads in advanced reasoning benchmarks: e.g. #1 on LMArena human preference ranking; 18.8% on the extremely hard Humanity’s Last Exam (best among peers without tools). Excels in math & science (SOTA on AIME 2025, GPQA). Coding: 63.8% on SWE-Bench with agentic approach – not quite Claude’s level, but a major leap over Gemini 2.0.

Multimodal I/O

Yes – GPT-5 handles text, code, and images natively (vision is improved over GPT-4). It inherits GPT-4o’s end-to-end voice and audio understanding, enabling fluid spoken dialogue. (Outputs are primarily text/voice; no image generation from GPT-5 itself, aside from describing images.)

Limited – GPT-4.1 accepts images (and possibly other modalities) as input, but outputs text only. It was optimized for textual tasks (coding, writing) rather than rich media. Multimodal capabilities were de-emphasized in favor of long text context and reliability.

Yes – Fully multimodal (the first ChatGPT model with native vision & voice integration). It can see and analyze images and respond with speech or detailed descriptions. GPT-4o unified what were separate models for speech & vision into one network, greatly improving latency and context-sharing across modalities.

No – Textual interface only. (ChatGPT’s initial version could handle images or audio only via separate plugins or modules, not via the GPT-3.5 model itself.) GPT-3.5 focuses on conversational text.

Partial – Primarily a text-based model, but can interpret some images or files if provided (e.g. extracting text or reasoning about an image if described). Not a native image generation model. The focus is on text, coding, and agent tool use. (Claude can interface with vision or other tools via its tool-use API, but it’s not inherently vision-trained like GPT-4o.)

Partial – Same modality limitations as Claude 4. Main improvements were in coding and reasoning rather than new modalities. (Anthropic has not highlighted image or audio support in Claude 4.1; the model remains chiefly a text/dialogue model, albeit one that can be connected to external vision or voice tools if developers enable them.)

Yes – Multimodal input by design. Flash can ingest images (for description or editing), audio (transcription or analysis), video, and text in one prompt. Also features Flash Image mode for image generation/editing and a native audio/voice response mode (Flash with Live API) with expressive speech output. All outputs ultimately return as text or media files (e.g. generated image or audio stream).

Yes – Fully multimodal understanding across text, images, audio, video. Like Flash, Gemini Pro does not directly produce images in text chat, but it deeply comprehends visual and auditory content and can integrate that understanding in its textual responses. (Google also offers specialized Gemini image and audio models in the ecosystem.) Gemini 2.5 Pro is used in apps that require analyzing mixed media inputs with a textual reasoning powerhouse.

Memory & Context

Context: Supports very long conversations. ChatGPT with GPT-5 dynamically uses a smaller or bigger model as needed to stay fast; if usage limits hit, it falls back to a smaller GPT-5-mini for remaining queries. While exact token limit isn’t public, it can leverage at least 128K tokens (and likely more, given OpenAI’s 1M-token advances in GPT-4.1). Long-term memory: GPT-5 introduced user profile customization and remembers user instructions better (Plus/Pro users can set persistent preferences). No built-in long-term memory beyond session context, but plugins or retrieval can be used.

Context: Up to 1,000,000 tokens (via API) – effectively the entire contents of a book or codebase in one go. This model is explicitly tuned to utilize extremely large contexts (improved comprehension over long inputs). Memory: No intrinsic long-term memory storage; however, developers can feed relevant docs into the context (and OpenAI’s “Responses API” helps manage retrieval). Not personalized to a user – it’s a generic model instance in the API, though it follows instructions well.

Context: 128K tokens window – a huge jump from earlier GPT-4’s 32K. Enough to hold lengthy conversations or multiple images. Enabled fluid continuous dialogue without forgetting recent history. Memory/personality: GPT-4o became known for its conversational “personality” that users felt was consistent and creative. It did not have long-term memory of users between chats, but within a single session it excelled at maintaining context and characters.

Context: Initially ~4K tokens, later up to 16K with 2023 updates. Often requires summarizing earlier conversation to handle very long chats. Memory: No built-in long-term memory (stateless between sessions). Users could use conversation history in the prompt for continuity. GPT-3.5 is not easily steerable to a persistent persona without user-provided system prompts (which Plus users could supply).

Context: Extremely large (Claude 2 already had 100K; Claude 4 likely similar or more). Claude is designed to handle book-length inputs or thousands of lines of code. Memory: When given a files workspace, Claude can write and refer to “memory files” – effectively notes it creates to remember facts during long tool-using sessions. It doesn’t retain memory across separate chats by default (unless an external system does that), but it’s optimized for long single-session memory and coherence.

Context: Same as Claude 4’s – very large. Additionally, Claude 4.1 introduced “thinking summaries,” where if its chain-of-thought gets too lengthy, it uses a smaller model to summarize it and keeps going. This prevents context overflow during extended reasoning. Memory: Still session-scoped, but even better at maintaining state over thousands of turns. Claude 4.1 was tested on multi-hour dialogues and agent tasks to ensure it “remembers” goals and facts provided earlier.

Context: 1,048,576 tokens (1M) input, with ~65K output limit – very large, enabling ingestion of long documents or even multiple files at once. Google plans to expand to 2M tokens for Pro. Memory: Via “context caching” and Vertex AI RAG (Retrieval-Augmented Generation) integration, Gemini can efficiently recall earlier info within a session or from a vector store. No built-in cross-session memory (developers must implement persistence). Flash can output a rationale (thought process) which may help external systems track its state.

Context: 1M+ tokens (with potential extension to 2M). This huge window allows Gemini Pro to analyze very large datasets or lengthy transcripts in one go. Memory: Focus is on accurate processing of all that context rather than on persistent persona memory. Enterprise users can use tools like context caching (to reuse computed embeddings) and fine-tuning to instill organizational knowledge. There’s no permanent memory of the user, but Pro can be integrated with Google’s ecosystem (Docs, etc.) to fetch relevant user data when solving tasks.

Tool Use & Integration

Natively agentic – GPT-5 will decide when to use tools (e.g. code execution or web browsing) if available, guided by the new real-time router. In ChatGPT, GPT-5 supports the plugin ecosystem and function calling from day one (continuing from GPT-4). OpenAI’s platform allows developers to define functions that the model can call (e.g. for database queries), which GPT-5 does reliably. Developer tools: OpenAI introduced a Codex CLI for GPT-5 coding assistance. However, as of Sep 2025 GPT-5 was not yet offered via open API to third-parties (beyond limited partners); integration is mainly through ChatGPT or OpenAI’s managed services.

Built for developers: function calling API, Tools via OpenAI’s function interface, and integration with the “Responses API” to manage agent dialogues. GPT-4.1 can power autonomous agents more effectively than GPT-4o, thanks to improved instruction-following (fewer off-track outputs). It’s available in the OpenAI API for direct integration into apps, and supports fine-tuning for custom use (limited fine-tune capability introduced for GPT-4 series in 2025).

In ChatGPT, GPT-4o gained tool use features like browsing and code execution (via plugins) during its tenure. It can follow user-provided tools or APIs but does not have the self-directed tool-use mechanism that GPT-5’s router employs. No public “GPT-4o API” by that name – developers used the GPT-4 API (which was text-only). The multimodal and voice capabilities of GPT-4o were primarily accessible through ChatGPT’s app and partnership integrations (e.g. Whisper for speech).

Minimal native tool use. GPT-3.5 can call functions via the API (OpenAI enabled function calling for gpt-3.5-turbo in mid-2023), but its reliability is lower than GPT-4 on complex multi-step tool use. Typically used in simple chatbot integrations or with external logic orchestrating any tools.

Claude 4 can act as an AI assistant/agent out-of-the-box. Anthropic provides an API and even IDE plugins (VS Code, JetBrains) via Claude Code to let Claude execute code or run actions. New API features include a code execution sandbox, a browser/search connector, and a file system API – enabling Claude to perform extended tool-using “thought loops” for up to an hour. These make Claude 4 a strong foundation for building AI agents or assistants that carry out tasks autonomously.

Continuation of Claude 4’s approach. Claude 4.1 is even more robust in agentic tasks (scoring high on TAU bench for autonomous agents). It handles tools in parallel and follows developer-set tool-use policies strictly. Anthropic positions Claude 4.1 as enterprise-ready for workflows like managing marketing campaigns or analyzing databases automatically. Integration: Claude is accessible via API, and through cloud platforms like AWS Bedrock and GCP Vertex AI, making it easy to plug into business applications.

Gemini Flash supports an extensive tool ecosystem on Google Cloud. It has built-in support for Google Search grounding (the model can fetch live search results) and code execution via a Python sandbox. It also supports function calling in a manner similar to OpenAI’s, and even can handle API chaining with structured output. Flash is available through Vertex AI (for API calls, including streaming results) and Google AI Studio (interactive playground) – allowing developers to integrate it into apps or even Google Workspace (via APIs for Docs, etc.). Its “Live API” feature with audio means it can power voice agents on telephony or Assistant-like platforms.

Gemini Pro has the same integration points as Flash, but geared towards heavy-duty tasks. It’s available in Google AI Studio and as an endpoint on Vertex AI with high-rate throughput. Developers can fine-tune Pro on custom data (Google supports fine-tuning for Gemini models) and use Enterprise Search connectors so that Pro can query internal data securely. Use-cases include advanced autonomous agents (Pro was built to reason over multi-hop problems, and partners use it for things like complex workflow orchestration). Google’s ecosystem allows Gemini Pro to be incorporated into everything from chatbots in Google Cloud to experimental features in Gmail/Docs (for instance, helping with spreadsheet formula generation or data analysis via the API).

Pricing (API & Plans)

ChatGPT Access: Available to all ChatGPT users (including Free) as of Aug 2025. Free users have limited GPT-5 queries (then it may use a smaller model). Plus ($20/mo) users get it as default with higher limits, Pro users (new higher tier) get unlimited GPT-5 and exclusive GPT-5 Pro mode. API: Not yet generally offered; expected to be expensive given its size. (OpenAI’s previous GPT-4.5 preview was $75/1M in and $150/1M out, so GPT-5 would likely be in that range or lower if commoditized).

API Pricing: $2.00 per 1M input tokens, $8.00 per 1M output tokens (standard GPT-4.1) – about 26% cheaper than GPT-4o was. The smaller GPT-4.1 Mini and Nano are far cheaper (e.g. Nano $0.10 in / $0.40 out per 1M). ChatGPT: Many improvements of 4.1 were merged into ChatGPT’s GPT-4o latest for Plus users in early 2025, but 4.1 as a distinct model was API-only.

ChatGPT Plus: (Prior to GPT-5) GPT-4o was the default for paid users, and it wasn’t separately metered. API: Equivalent to GPT-4 pricing; OpenAI didn’t sell a model labeled “4o” directly, but GPT-4 32k context via API cost ~$0.06/1K tokens output in 2024. By late 2024, cost had been reduced as usage scaled. Note: On Aug 2025 GPT-4o was temporarily removed from ChatGPT when GPT-5 launched, causing backlash; OpenAI restored it for Plus users due to popular demand.

Free (ChatGPT basic) model. OpenAI’s API price for gpt-3.5-turbo was extremely low (around $0.40 per 1M tokens in, $0.80 out). It’s the most accessible model cost-wise. Many third-party apps use GPT-3.5 via API for affordability, reserving GPT-4 for tougher cases.

Claude Pricing: Opus 4 costs $15 per 1M input tokens and $75 per 1M output. Sonnet 4 (the smaller model) is $3 in / $15 out per 1M – on par with other 34B-70B models. Claude also offers generous free tiers on its own chat (Claude.ai) for exploration, with limits. Enterprise plans (Claude Pro, Claude Max) bundle both models and allow extended thinking mode usage without per-call charges.

Claude 4.1 Pricing: Same base prices as Claude 4 (Opus 4.1 at ~$15/75 per 1M tokens), with discounts for prompt caching (up to 90% off repeated content) and batch requests. Claude 4.1 is available to Anthropic’s paying customers (Claude Max, Team, Enterprise) on their platform – free-tier users on Claude.ai continue with Claude 4 (Sonnet 4).

Gemini Flash Pricing: Pay-as-you-go (Vertex AI): $0.30 per 1M input tokens (text) and $2.50 per 1M output tokens. This is for the standard interactive mode including “thinking” overhead. Discounts: 50% off in batch mode processing. Flash Image generation has separate pricing (roughly $3 per image output at this stage). Access: Flash and Flash-Lite are GA (general availability) – developers can use them in production. Google also offers a free testing tier with limited rate for AI Studio and up to 500 requests/day on the API.

Gemini Pro Pricing: Paid API: $1.25 per 1M input tokens (up to 200K context; $2.50 beyond that), and $10 per 1M output tokens (up to 200K; $15 beyond). This makes it Google’s priciest model (reflecting its high compute usage). However, it undercuts some competitors: e.g. cheaper output than OpenAI’s GPT-4.5 was and Claude 3.7. Free access: Google AI Studio allows limited free use of 2.5 Pro (with strict rate limits) for developers. Offerings: Gemini 2.5 Pro Experimental was launched first; by Sep 2025 it’s generally available via API (in Vertex AI Model Garden). Google is also introducing Gemini Ultra (future larger model) for specialized needs, but 2.5 Pro remains the flagship in broad availability.


OpenAI: GPT-3.5, GPT-4o, GPT-4.1, and GPT-5

OpenAI’s ChatGPT models have evolved from the GPT-3.5 series through GPT-4 and now GPT-5, bringing major improvements in reasoning, multimodality, and tool use. Below we detail each model’s capabilities, performance, and usage as of late 2025.


GPT-3.5 (ChatGPT “o3”)

Overview: GPT-3.5 was the initial model behind ChatGPT’s launch (Nov 2022). It’s a finetuned version of GPT-3, optimized for dialogue. This model (code-named “o3”) became the default assistant for millions of users, known for its friendly style and fast responses. However, it has considerably lower raw capabilities than the GPT-4 series on challenging tasks.

  • Capabilities: Primarily a text-only model for conversational AI. GPT-3.5 can follow user instructions and answer questions on a wide range of topics, but its complex reasoning and math skills are limited. It often relies on superficial patterns; for example, GPT-3.5 scored around 70% on MMLU (multi-task knowledge exam), far behind GPT-4’s performance. It can write code at a basic level and solve simple problems, but struggles with more complex coding or multi-step logic that larger models handle. On coding benchmarks like HumanEval (Python problems), GPT-3.5’s success rate was substantially lower than GPT-4’s (roughly half of GPT-4’s score).

  • Usage & Integration: GPT-3.5 (branded as gpt-3.5-turbo in the API) became widely used due to its speed and low cost. It has a shorter context window (initially ~4K tokens, later up to 16K in an expanded version) which means it can’t ingest extremely long prompts without losing earlier context. It does not natively support images or audio input. Nonetheless, it powered countless chatbots and applications as the economical choice for conversational AI. OpenAI enabled function calling with GPT-3.5 in mid-2023, allowing developers to have the model call specified APIs (though GPT-3.5 is less reliable in tool use than GPT-4). This model does not have the “intelligence” to decide on tools autonomously – the developer must prompt it to use functions.

  • Limitations: Tends to hallucinate facts more often and can be easily confused by complex instructions. It may also exhibit repetitive or generic answers for creative tasks. OpenAI’s alignment tuning curbed the most inappropriate outputs, but GPT-3.5 will still occasionally produce tangential or subtly incorrect answers that GPT-4 would catch. By late 2025, GPT-3.5 was largely considered a legacy model; in fact, OpenAI’s August 2025 release notes signaled the retirement of “o3” and “o3-pro” models from ChatGPT (users would be migrated to GPT-5 or GPT-5 Pro). OpenAI did keep GPT-3.5 available via API and as an optional model (particularly for developers or users who explicitly prefer its style or need its speed).


GPT-4o (GPT-4 “Omni”)

Overview: GPT-4o is the enhanced multimodal version of GPT-4 that debuted in May 2024. The “o” stands for “omni,” reflecting that it was trained to handle text, vision, and voice in a single model. This was a departure from the original GPT-4 (March 2023), which handled text (and had a separate vision model in limited beta). GPT-4o became the default ChatGPT model for Plus users throughout late 2024 and the first half of 2025. It gained a reputation for very natural conversations and creativity.

  • Multimodal Capabilities: GPT-4o can see and speak. It accepts image inputs and can generate detailed descriptions or analyses of images. For example, users could upload a diagram or photo and GPT-4o would explain it or answer questions. It also powers ChatGPT’s voice conversations: GPT-4o was trained end-to-end on spoken dialogues, eliminating the stitched-together pipeline (speech-to-text + text model + text-to-speech) used previously. This end-to-end training yielded huge latency improvements – voice responses dropped from ~5.4 seconds (with GPT-4) to ~0.32 seconds with GPT-4o. The model can detect nuances in audio input (tone, emotion) and respond with a more human-like cadence, including laughter or intonation when appropriate. Overall, GPT-4o made voice interactions feel much more seamless and “real time,” enabling near-human conversational speed.

  • Personality and Use Cases: GPT-4o became beloved for its conversational warmth and creativity. Many users noted it had a more engaging, story-like style in long-form chats. It was excellent at creative writing, role-playing, and emotionally nuanced responses, often better than later stricter models in these areas. This model set an “industry-defining standard” for accessible, natural AI interaction. It prioritized a good user experience and graceful dialogue over maximizing benchmark scores. For instance, GPT-4o would willingly engage in longer, imaginative storytelling or supportive counseling-like dialogue. As a generalist, it handled everyday questions with low latency and sufficient accuracy.

  • Performance: On academic or coding benchmarks, GPT-4o was strong but not state-of-the-art by mid-2025. It had about 85.7% on MMLU (knowledge test) and around 33% on SWE-Bench coding (when tested in late 2024). These are good results (comparable to the original GPT-4), but GPT-4o was overtaken by the more specialized GPT-4.1 in those areas. Nonetheless, GPT-4o’s multimodal competency and fast responses made it the model of choice for user-facing applications where response time and conversational quality mattered more than absolute precision on niche benchmarks.

  • Retirement and User Backlash: When OpenAI launched GPT-5 in Aug 2025, they initially retired GPT-4o from the consumer app (ChatGPT) with no overlap period. Many users were upset to lose GPT-4o’s unique qualities, describing GPT-5 as more “sterile” in personality. Creative writers and those doing role-play or emotionally rich chats felt GPT-5 didn’t match the old model’s tone. In response, OpenAI brought GPT-4o back for paid users a day later, stating they’d monitor usage to decide how long to keep it. As of September 2025, GPT-4o remains available in ChatGPT for Plus/Pro users (with a toggle in settings to show legacy models). OpenAI affirmed that if they ever do fully deprecate it, they will give plenty of notice – a recognition of GPT-4o’s special place for a segment of users. The GPT-4o model is still accessible via the API as well (there were no immediate plans to remove it from the API by Aug 2025).

  • Pricing: OpenAI did not sell GPT-4o under that name in the API, but essentially it is equivalent to GPT-4 32k with multimodal. API pricing for GPT-4 (32k) was on the order of $0.06 per 1K output tokens initially. By April 2025, efficiency improvements allowed GPT-4.1 to be cheaper, implying GPT-4o’s costs likely dropped as well. In ChatGPT, GPT-4o was included under the $20/month Plus plan. When it was briefly removed, Plus users demanded it back despite having GPT-5 – indicating that for some, the experience mattered more than raw power.


GPT-4.1

Overview: GPT-4.1, released in April 2025, is a series of specialized GPT-4-based models focusing on coding proficiency, long context, and reliability. It marked a strategic shift by OpenAI: instead of one model for everything, they offered a family of models tuned for specific needs (professional and enterprise tasks). The GPT-4.1 family includes the main model (often just called “GPT-4.1”), a 4.1 Mini, and a 4.1 Nano variant. These correspond to smaller parameter versions that trade off some raw capability for huge gains in speed and cost.

  • Strengths: GPT-4.1 made major advances in coding and long-form reasoning. It outperforms GPT-4o on coding benchmarks by a large margin, scoring 54.6% on SWE-Bench Verified (vs 33.2% for GPT-4o). This benchmark measures real-world code generation in the context of a codebase – GPT-4.1 was specifically trained to handle tasks like reading multiple files, following diff instructions, and producing correct patches. Likewise, on instruction-following tests like MultiChallenge, GPT-4.1 is about 10.5% (absolute) better than GPT-4o, reflecting improved compliance with user requests.

  • 1 Million Token Context: A headline feature of GPT-4.1 is its 1,000,000-token context window. This is eight times larger than GPT-4o’s 128k, and enables entirely new use cases. For example, GPT-4.1 can ingest hundreds of pages of text or entire code repositories and reason about them in one go. OpenAI demonstrated it can analyze a lengthy video transcript (using Video-MME benchmark) with state-of-the-art understanding. The model is trained to make use of long context – it was specifically optimized for long-context comprehension so that it doesn’t lose track or become confused even with very large inputs. Notably, OpenAI does not charge extra for long context use on these models (no premium for using 1M tokens versus a smaller context), which encourages developers to experiment with feeding huge documents.

  • GPT-4.1 Mini and Nano: OpenAI scaled the GPT-4.1 architecture down to smaller sizes that are incredibly efficient. GPT-4.1 Mini delivers roughly GPT-4o-level performance on many tasks with half the latency and 1/6th the cost. In fact, 4.1 Mini “matches or exceeds” GPT-4o on intelligence evaluations. GPT-4.1 Nano is even smaller – it’s the fastest model OpenAI has, while still scoring 80.1% on MMLU and outperforming the older GPT-4o Mini on many benchmarks. Nano is ideal for tasks like rapid classification or autocompletion where speed and scale matter more than the absolute best reasoning. Essentially, OpenAI used insights from GPT-4 to push the performance frontier at every latency tier: developers can choose from the nano, mini, or full model based on their needs.

  • Use Cases: GPT-4.1 models shine in enterprise and developer applications. They are better at “powering agents” – e.g., an AI agent that reads a large knowledge base and handles customer requests autonomously. One reason is improved reliability: the model is less likely to go off track, and it handles tools and function calls in a predictable way. For instance, GPT-4.1 was trained to output diffs reliably, so it can make targeted code edits without regressing other parts. It also supports long outputs up to 32,768 tokens, doubling the prior limit, which is helpful when producing lengthy documents or full code files. OpenAI mentioned that many improvements from GPT-4.1 were gradually being incorporated into the “latest” ChatGPT GPT-4 model (which was GPT-4o) over time. However, GPT-4.1 itself was only available via the API (ChatGPT’s UI did not have a GPT-4.1 toggle for end-users).

  • Safety and Alignment: GPT-4.1 continued OpenAI’s alignment work. One notable aspect was the plan to deprecate the GPT-4.5 Research Preview after GPT-4.1’s launch. GPT-4.5 was an experimental, very large model that some developers tested in 2024; GPT-4.1 offered similar or better performance at much lower cost, so OpenAI decided to retire 4.5 by July 2025. The safe and desirable behaviors from 4.5 (like humor, nuance in writing) were to be carried into future models. This indicates GPT-4.1 struck a balance between capability and cost-effectiveness that OpenAI deemed the future path.

  • Pricing: Thanks to system optimizations, GPT-4.1 is cheaper than GPT-4o was. The full 4.1 model costs $2 per million tokens in and $8 per million out, which OpenAI noted is ~26% less expensive for typical queries than GPT-4o. The Mini and Nano are even more affordable: GPT-4.1 Nano is only $0.10 per million input and $0.40 per million output tokens (with additional discounting for cached prompt reuse). These aggressive prices (e.g. $0.0004 per 1K output tokens on Nano) show OpenAI’s push to commoditize basic language tasks while still offering top-end performance with larger models. The cost drop also pressures rivals – GPT-4.1 made high-end AI more accessible.


GPT-5

Overview: GPT-5 is OpenAI’s newest flagship model, launched on August 7, 2025. It represents a significant leap in intelligence over previous models and a new philosophy in model design. Instead of a single monolithic model, GPT-5 is presented as one unified system composed of multiple specialized components that are orchestrated automatically. It’s described as OpenAI’s “smartest, fastest, most useful model yet”, with state-of-the-art performance across a broad range of domains.

  • Unified “Thinking” Architecture: GPT-5’s hallmark feature is its ability to route queries in real-time between different internal models to balance speed and complexity. Specifically, GPT-5 consists of: (1) a fast, efficient main model for most queries (often dubbed GPT-5 Main), and (2) a deeper, more powerful model called GPT-5 Thinking for particularly hard or nuanced problems. A learned router model sits on top, analyzing each user prompt and deciding whether to give a quick answer or engage the “think harder” mode. Users can also explicitly signal if they want deep reasoning (e.g. by saying “think hard about this” in the prompt). This system allows GPT-5 to be both responsive and thorough: simple questions get answered almost instantly by the lightweight model, while complex tasks trigger a slower, step-by-step reasoning process for a high-quality answer. The routing is informed by continuous training on signals like user preferences and correctness feedback, and it improves over time.

  • Capabilities: GPT-5 is a general-purpose powerhouse. It outperforms GPT-4.1 and GPT-4o on nearly all quantitative benchmarks, setting new records in coding, advanced math, multimodal understanding, and more. Early testing showed major improvements in difficult areas like front-end web programming, large codebase debugging, and complex mathematical reasoning. For example, GPT-5 can often create entire apps or games from scratch based on a single prompt, demonstrating not just coding skill but also a sense for design/aesthetics in the output. In writing tasks, GPT-5 is the “most capable writing collaborator yet” – able to carry a specific tone or literary style consistently (such as maintaining unrhymed iambic pentameter). It’s better at handling ambiguous instructions and steering the style as the user desires. OpenAI specifically noted GPT-5’s advances in three common ChatGPT use cases: writing, coding, and health:

    • Coding: GPT-5 is the strongest coding model OpenAI has built. It improves on complex tasks like generating multi-file projects and reasoning about large codebases. An example given is creating a fully working mini-game (HTML/JS) with specified features and cartoonish design – GPT-5 could produce this in one prompt. It also has more “code sense,” understanding things like proper spacing and UI elements more than GPT-4 did.

    • Health: GPT-5 achieves a new level on OpenAI’s internal HealthBench evaluation, scoring significantly higher than previous models. It acts more like a thoughtful medical assistant: flagging potential issues, asking clarifying questions, and giving tailored advice. It adapts to the user’s context and location for health queries, providing more precise and reliable answers. (OpenAI still cautions it’s not a doctor, but it’s meant to help users have better-informed discussions with medical professionals.)

    • Creative Writing: GPT-5 can produce writing with literary depth and rhythm, and handle complex poetic forms better. A comparison in OpenAI’s blog shows a table of writing style differences between GPT-5 and GPT-4o. GPT-5 tends to produce more coherent, expressive long-form text where GPT-4o might have been a bit simpler or more repetitive.

    Beyond those, GPT-5 is noted to be more useful for real-world queries by reducing issues like hallucination and sycophantic agreeing. It’s also more “honest” and won't pretend to know things it doesn’t.

  • Safety & Alignment: OpenAI introduced a new “safe-completions” paradigm with GPT-5. Instead of the older models’ often blunt refusals (“I’m sorry, I cannot do that”), GPT-5 tries to give helpful, harmless answers by redirecting or reframing requests when possible. This led to measurable decreases in GPT-5’s hallucinations and manipulative or biased outputs. The model is more likely to produce a nuanced response that adheres to policy without completely stonewalling the user. For instance, rather than giving direct advice on a personal decision (which 4o sometimes did inappropriately), GPT-5 might ask questions and guide the user to think it through. OpenAI also implemented extensive safeguards in high-risk domains like biology/chemistry: GPT-5’s “Thinking” mode is treated as a High-Capability system and runs with multilayered protections against misuse (including 5,000 hours of red-teaming). All these efforts reflect that GPT-5 is built to be enterprise-grade – reliable and safe enough for wide deployment.

  • Personalization: GPT-5 responded to user feedback about the “personality” of AI. After the GPT-4o retirement incident, OpenAI recognized that one size doesn’t fit all in terms of style. With GPT-5, they launched a preview of Custom GPT personalities. Users (even free users) can choose from presets like Cynic, Robot, Listener, or Nerd, which adjust ChatGPT’s tone – e.g. concise and professional vs. sarcastic wit. These presets leverage GPT-5’s improved steerability to maintain a chosen style without needing cumbersome prompt engineering. OpenAI also improved how GPT-5 follows the user’s own custom instructions: it is significantly better at adhering to user-provided guidelines (from the “Customize Instructions” feature). This means the user can tell GPT-5 about their preferences once, and the model will remember and apply it more consistently (e.g. always responding in markdown, or adopting a certain role).

  • Performance: Although OpenAI hasn’t published a full benchmark table publicly, GPT-5 is reported to achieve state-of-the-art results on many evaluations. It “set new records” on complex coding challenges, advanced reasoning tests, and multimodal tasks. For example, GPT-5 is said to have exceeded GPT-4.1 on benchmarks like MMLU, Math (AIME), coding, etc., presumably placing at or near the top on leaderboards. A third-party analysis (DataStudios) noted GPT-5’s capabilities in complex coding, math, and reasoning are state-of-the-art, with nearly all quantitative metrics above its predecessors. GPT-5 Pro – an even more powerful version (see below) – achieved the highest performance on very challenging benchmarks like GPQA (a detailed science Q&A test). In summary, GPT-5 is currently one of the most powerful models available, if not the most powerful, especially when both speed and accuracy are considered.

  • GPT-5 Pro: Alongside GPT-5, OpenAI offers GPT-5 Pro to Pro plan subscribers and enterprises. GPT-5 Pro is essentially a version that “thinks even longer” – it uses scaled-up parallel compute at inference time to delve deeper into problems. This yields the absolutely highest quality outputs, at the cost of more latency/compute. GPT-5 Pro is only invoked when needed (or when chosen by the user) due to its expense. According to OpenAI, GPT-5 Pro delivers the best results on the hardest tasks: external experts preferred its answers over the normal GPT-5 (a.k.a. GPT-5 “Thinking” mode) about 68% of the time on a set of 1000 hard prompts. It made 22% fewer major errors and was especially strong in domains like health, science, math, and coding. For example, GPT-5 Pro currently holds the state-of-the-art on GPQA (a tough science QA benchmark) and other “high frontier” tests. Essentially, GPT-5 Pro pushes a bit further on accuracy and depth, using more computation (possibly via model ensembles or longer reasoning chains). In practice, a Plus user would not have GPT-5 Pro, whereas a Pro user could toggle GPT-5 Pro for the most demanding queries.

  • Usage & Access: GPT-5 became the default model for all ChatGPT users upon launch. Free users started getting GPT-5 gradually (with some limits to manage load), and Plus users got it immediately with higher message allowances. Pro users have unlimited access and can use GPT-5 Pro as well. The new model picker essentially disappeared for normal use – users just use ChatGPT and GPT-5 decides how to respond (simple or “thinking”). However, paid tier users can explicitly choose GPT-5 Thinking mode if they want to force deep reasoning. On the API side, OpenAI had not yet made GPT-5 generally available as an API model by September 2025 (developers still used GPT-4.1 or older for now), though that is expected in the future. OpenAI is likely being cautious given the scale: GPT-5 is a very large system, and initially they deployed it in their own ChatGPT service where they can manage the traffic and costs.

  • Cost and Plans: ChatGPT Plus remains $20/month and Pro is $40/month (reported) – giving an idea of pricing tiers. The Plus plan now includes GPT-5 (standard) with generous limits, whereas previously $20 got you GPT-4. The Pro plan is intended for power users or professionals needing heavy usage and the Pro model. In terms of API pricing, if we extrapolate, GPT-5’s cost per token might be similar or slightly above GPT-4.1’s, but below the exorbitant GPT-4.5 preview. TechCrunch reported that OpenAI’s “o1-pro” model (likely referring to an early internal version related to GPT-5) had an extremely high price of $150 per million input and $600 per million output tokens, used as a premium option. Those prices are not for GPT-5 generally, but it shows OpenAI has tiers even above GPT-4 in certain offerings. Over time, we can expect GPT-5’s cost to come down as OpenAI optimizes the system and faces competition.

In summary, GPT-5 combines the best of GPT-4o and GPT-4.1: it has GPT-4o’s broad, multimodal conversational skills and GPT-4.1’s deep analytic and coding prowess, all packaged behind an intelligent router so the user no longer has to choose models. It represents OpenAI’s most advanced step toward artificial general intelligence, while also highlighting the importance of usability (custom personalities, no model switching burden) as a core feature.


Anthropic: Claude 4 and Claude 4.1

Anthropic’s Claude series is another leading AI model family, known for its emphasis on helpfulness, safety, and long-context capabilities. Claude 4 (released May 2025) and Claude 4.1 (Aug 2025) are the latest iterations, significantly more powerful than the Claude 2 model from 2023. Anthropic differentiates between an “Opus” model (maximum capability) and a “Sonnet” model (smaller, faster) to cater to different use cases.


Claude 4 (Opus 4 and Sonnet 4)

Overview: Claude 4 was introduced in May 2025 as “the next generation of Claude models”, comprising Claude Opus 4 and Claude Sonnet 4. Opus 4 is the large model aimed at frontier performance, while Sonnet 4 is a mid-sized model for general usage (successor to Claude 2’s “Claude Instant” line, which had names like Claude 1.3, 1.3.1 etc., and more directly to Claude 3.7 Sonnet). Claude 4 places a strong focus on coding, complex reasoning, and agent-based tasks.

  • Coding Prowess: Claude Opus 4 is explicitly described as “the world’s best coding model” at launch. It leads benchmarks like SWE-Bench Verified with 72.5% accuracy, which at the time surpassed other models (for context, GPT-4.1 was ~54.6%, and older GPT-4 ~33%). It also topped Terminal-Bench with 43.2%, which is another coding/terminal automation benchmark. This means Claude 4 can reliably solve complex multi-file coding tasks, understand and modify large codebases, and produce working code across various languages. Anecdotally, partner companies like Cursor AI, Replit, Sourcegraph, etc., praised that Claude 4 significantly advanced the state of AI coding assistants – e.g. by maintaining code quality during edits and handling instructions without hallucinating changes.

  • Extended Reasoning (“Thinking” Mode): Both Opus 4 and Sonnet 4 introduced a two-mode approach: near-instant responses vs. extended thinking. In quick mode, the model responds in a few seconds (useful for straightforward queries). In “extended thinking,” Claude can spend much more time reasoning step-by-step, even interleaving tool use if allowed (for example, calling a web search or running Python code in the background). This allows Claude to handle long-running tasks (hours long) that require many intermediate steps or decisions. Anthropic specifically tested Claude 4’s ability to work continuously for several hours and accomplish goals that previous models could not. An example given: Claude Opus 4 autonomously performed a 7-hour code refactoring job on an open-source project, succeeding without crashing or going off track. This showcases a new level of consistency and planning in its reasoning process.

  • Memory and Tool Use: Claude 4 made strides in integrating with tools and remembering context over long sessions. It can use tools such as a web browser, calculators, or even custom developer-provided tools during its reasoning (Anthropic’s API allows developers to enable this). Notably, when given access to a filesystem, Claude 4 will create and update “memory files” to store important information it learns along the way. This is a novel capability – for example, if Claude is playing a game or doing research, it can keep notes in a file about what it’s done or what remains, and refer back to those notes later. That effectively extends its working memory beyond the fixed context window, improving coherence on very long tasks. Anthropic reported that Opus 4 dramatically outperformed previous models on internal memory evaluations, thanks to these techniques.

  • Safety and Alignment: Safety remains a core focus for Anthropic. Claude 4 models underwent extensive training to reduce problematic behaviors. Anthropic mentions they cut instances of the model using “shortcuts or loopholes” to get to an answer by 65% compared to Claude 3.7. This refers to behaviors where a model might try to game the evaluation or give an answer without truly doing the reasoning (for example, guessing an answer to pass a test). By reducing that, Claude 4 is more trustworthy in agentic tasks where it shouldn’t cheat or bypass constraints. They also implemented “higher AI Safety Levels (ASL-3)” measures for Claude 4, indicating stricter guardrails around sensitive use cases. Overall, Claude has a reputation for being relatively cautious and well-behaved; these improvements continue that trend to make Claude suitable for enterprise settings.

  • Claude Sonnet 4: This is the smaller sibling of Opus 4. Sonnet 4 “delivers an instant upgrade” from the earlier Claude Instant models, excelling in coding with 72.7% on SWE-Bench (almost as high as Opus 4’s 72.5%, interestingly). It is designed to balance performance and efficiency – likely having fewer parameters, it’s cheaper to run, but still very capable. Anthropic highlights Sonnet 4’s enhanced steerability and precision in following instructions. Many partners (GitHub, iGent, Sourcegraph, etc.) were excited about Sonnet 4 because it can be used widely (even free users get access to Sonnet 4 on Claude.ai) and yet it provides a big chunk of Opus’s capabilities in a faster package. For everyday use cases (drafting emails, summarizing text, writing code with a quick turnaround), Sonnet 4 is the workhorse, while Opus 4 is reserved for the heaviest tasks.

  • Context Length: While exact numbers aren’t explicitly stated in the announcement, Claude 4 inherited Claude 2’s very large context window (which was 100K tokens). It’s safe to assume Claude 4 supports on the order of 100K tokens or more in context. In practice, this means you could provide hundreds of pages of text to Claude 4 in one prompt. Coupled with its ability to use extended reasoning and memory files, Claude 4 is perhaps the best suited model for extremely long, coherent outputs or analyzing massive documents.

  • Integration and API: Claude 4 (both Opus and Sonnet) is accessible via the Anthropic API, and notably also through cloud platforms like AWS Bedrock and Google Vertex AI. That makes it easy for businesses to integrate Claude into applications or use it alongside other models. Anthropic also rolled out Claude Code with Claude 4 – extensions for VS Code and JetBrains IDEs that integrate Claude as a coding assistant right in the development environment. This shows Anthropic’s strategy of deeply integrating into developer workflows (similar to how Microsoft integrated OpenAI models into GitHub Copilot). Additionally, Anthropic provided an SDK for Claude Code so developers can build custom agents using Claude’s core abilities.

  • Pricing: Claude Opus 4 usage is priced at $15 per million input tokens and $75 per million output tokens. Claude Sonnet 4 is much cheaper: $3 per million input, $15 per million output. This pricing is consistent with previous Claude models (Claude 2, Claude 3, etc. had similar tiers). It is higher than OpenAI’s GPT-4.1 in absolute terms, especially the output cost for Opus. However, the rationale is that Opus might be used for tasks that truly need its extended thinking, while Sonnet covers common tasks at a moderate price. Enterprises typically purchase plans (Claude Pro, Claude Enterprise) that include a quota of tokens. Notably, Anthropic offers prompt caching – if the same input or context is reused, costs can drop (they mention up to 75-90% savings with caching and batching).


Claude 4.1 (Opus 4.1)

Overview: Claude 4.1 (often referred to as Claude Opus 4.1) is an incremental upgrade to Claude 4, released in late August 2025. While it’s not a full “Claude 5,” it brings important refinements in coding, safety, and reliability. It’s currently Anthropic’s best model, “our most intelligent model to date,” aimed at frontier coding tasks and complex agent applications.

  • Coding Improvements: Claude 4.1 strengthened multi-file coding abilities and refactoring. It raised its SWE-Bench Verified score to 74.5% (from 72.5%), marking a new high. This suggests it catches more edge cases and produces even more reliable code. GitHub, in the release notes, observed that Claude 4.1 handled complex refactoring tasks better – e.g. pinpointing exactly where a fix is needed in a large codebase without messing up other code. This kind of precision is critical for using AI in real development workflows (developers want minimal unnecessary changes). A startup called Windsurf noted Claude 4.1 gave about a one standard deviation improvement in their internal coding benchmark over Claude 4, which is analogous to the jump they saw from Claude 3.7 to Claude 4. That indicates a fairly meaningful step up for coding-heavy use cases.

  • Reasoning & Long Interactions: Claude 4.1 also focused on improving long-horizon reasoning and state tracking. Anthropic mentions better ability to follow reasoning chains and track state over long interactions, which is critical for agent-like workflows that involve many back-and-forth steps. For example, an AI agent might plan a marketing campaign over dozens of steps – Claude 4.1 will more reliably remember earlier decisions and not contradict itself as it goes. It also reportedly has strong results on TAU-bench (a benchmark for complex agent tasks), showing it can manage multi-step, goal-oriented processes effectively.

  • Safety Enhancements: Safety got a boost in 4.1. The “harmless response rate” (how often the model properly refuses or safe-completes disallowed requests) improved to 98.76%, up from 97.27% in Claude 4. Also, Claude 4.1 is 25% less likely to comply with high-risk requests (like instructions related to weapons or illicit behavior). These are significant because enterprises care about AI not generating content that could cause liability. Anthropic’s system card for Claude 4.1 details these new safety results. In short, Claude 4.1 continues Anthropic’s trend of heavily tuning the model to avoid problematic outputs while still being useful.

  • Availability & Plans: Claude 4.1 (Opus) is made available to Claude “Max” users (the highest tier of their subscription), as well as Team and Enterprise customers. It’s also accessible via API, Amazon Bedrock, and Google Vertex AI similar to Claude 4. Interestingly, Anthropic’s announcement didn’t mention a “Sonnet 4.1” explicitly; it seems the upgrade was primarily to the Opus model. However, they did mention that the jump from Opus 4 to 4.1 was akin to Sonnet 3.7 to 4 (which implies Sonnet 4.1 might not be a separate thing, or it’s a minor refresh of Sonnet 4). In practice, they gave Claude 4.1 to paying customers who need the best coding performance, while regular Claude.ai users likely still use Claude 4 (Sonnet 4).

  • Use Cases: With Claude 4.1’s improvements, a prime use case is AI software development assistants. For instance, one could have Claude 4.1 read an entire repository, then ask it to implement a new feature; it can plan out changes across files and even execute test cases with its tool-use to verify it worked. Enterprise “AI co-pilots” for data analysis or research are another case – Claude 4.1 can independently sift through large internal knowledge bases and produce comprehensive reports (with citations, etc.), thanks to its long context and improved reliability. Basically, Claude 4.1 cements Anthropic’s position in the high-end, professional AI assistant market, often appealing to those who prioritize transparency and safety (Anthropic is known for its Constitutional AI approach to alignment).

  • Competition: It’s worth noting that right around the same time, OpenAI released GPT-5, and Google had Gemini 2.5 Pro in the works. Claude 4.1’s coding benchmark lead (74.5%) slightly edges out what we know of competitors – Gemini 2.5 Pro was ~63.8%, GPT-4.1 was 54.6%. GPT-5’s coding score isn’t published, but OpenAI claims SOTA; whether GPT-5 surpassed 74.5% on SWE-Bench isn’t confirmed publicly. In coding at least, Anthropic has a strong claim that Claude is top-tier. On general knowledge and reasoning, Claude 4.1 is very strong too (though GPT-5 and Gemini Pro likely outscore it on things like MMLU or math). Anthropic’s differentiation is trustworthiness and long-term task focus – Claude will diligently follow through on tasks for hours with minimal drift, which can be more important than a few points on a benchmark.

  • Pricing: The pricing for Claude 4.1 remains $15/75 per million tokens for Opus (input/output), same as Claude 4. They do offer significant discounts via prompt caching (75% off for reused context) and batch processing (50% off) for these models. That suggests Anthropic is encouraging efficient use: e.g., if you have a long document that many prompts will reference, you can cache it to not pay full price each time. This can bring the effective cost down a lot. Still, using the full power (especially generating large outputs) is expensive – e.g. a 2,000-token answer costs $0.15 on Claude Opus 4. For comparison, Gemini Pro costs $15 per million output too, and GPT-4.1 is $8 per million output. Anthropic’s price is higher, justified by the longer contexts and arguably more “premium” positioning.


In summary, Claude 4 is a highly capable assistant excelling in coding and lengthy tasks, with Claude 4.1 polishing those capabilities further. Anthropic’s focus on tool use, extended reasoning, and safety is evident, making Claude a strong choice for complex applications where reliability and ethical guardrails are paramount. Many organizations may employ a mix of models (Claude for some tasks, GPT/Gemini for others) to get the best of each – and Claude 4.1 firmly secures Anthropic’s place among the top-tier AI models of 2025.


Google: Gemini 2.5 (Flash and Pro)

Google’s Gemini is a family of next-generation foundation models developed by Google DeepMind, poised as Google’s answer to GPT-4 and beyond. Announced initially at Google I/O 2023, Gemini underwent multiple iterations. By 2025, Google introduced Gemini 2.5, which comes in different tiers such as Flash, Flash-Lite, and Pro (and an Ultra variant teased for future). Gemini models are deeply integrated into Google’s ecosystem (Cloud, Workspace, Android) and are characterized by “thinking” capabilities (chain-of-thought reasoning), multimodality, and massive context windows.


Gemini 2.5 Flash

Overview: Gemini 2.5 Flash is the mid-tier variant of Gemini 2.5, optimized for speed and cost-efficiency while still offering advanced reasoning (the first of Google’s “Flash” models to do so). It was released into general availability around mid-2025 as part of expanding the Gemini 2.5 family. Flash is intended for high-volume, everyday applications – think of it as the workhorse model that balances power with practicality.

  • “Thinking” Mode: A key feature is that Flash includes Gemini’s chain-of-thought reasoning ability in an interactive form. Developers (or even end-users via certain interfaces) can enable thinking visibility, which lets you see the model’s step-by-step thought process as it works on a query. This transparency is valuable for debugging and for sensitive applications where you want to understand how the model arrived at an answer. Under the hood, Gemini Flash can internally reason through complex tasks (like multi-step arithmetic or logical problems) before finalizing a response. This was first introduced in Gemini 2.0 Flash (which was a preview) and fully integrated in 2.5 Flash.

  • Multimodal Inputs & Outputs: Gemini 2.5 Flash is natively multimodal. According to Google’s documentation, it accepts text, code, images, audio, and video as input and produces text outputs. The model can handle multiple input types in one prompt (for example, you could give it an image and ask questions about it, or provide an audio clip to summarize). In addition, there are specialized extensions:

    • Flash Image (Preview): a mode of Gemini Flash that can generate and edit images based on text instructions. This allows users to create images or perform multi-turn editing (e.g., “make the sky brighter in the image”) through natural language – essentially Google’s answer to DALL-E or Midjourney, integrated with the language model.

    • Flash with Live Audio (Preview): a variant with advanced voice capabilities. It can output high-quality synthesized speech, engage in voice conversation with nuanced intonation and emotion (affective dialogue). For instance, it can act as a voice-based customer service agent that responds with an expressive human-like voice. It also understands voice input more deeply (detecting sentiment, etc.).

    These multimodal features mean Gemini Flash is not just a text chatbot – it’s a platform for interactive, multimedia AI experiences (from generating images for a design to speaking as a voice assistant).

  • Performance: Despite being faster and smaller than the Pro model, Gemini 2.5 Flash is quite capable. Google noted that in addition to strong academic benchmark performance, it “tops the popular WebDev Arena coding leaderboard.” WebDev Arena is a community-driven benchmark focusing on web development tasks; Flash leading there indicates it’s very good at generating web app code quickly. Flash also likely performs well on standard tasks: it uses the same base technology as Pro, just optimized for latency. It may not set records on MMLU or math like Pro does, but it’s no slouch – it benefits from the chain-of-thought techniques which improve accuracy without needing to scale up parameters too much.

  • Use Cases: Gemini 2.5 Flash is ideal for real-time applications. For example, powering a chatbot on a website that needs quick response, or an interactive coding assistant that autocompletes as you type. It’s also suited for scenarios where cost is a concern – you can deploy Flash at scale (it’s significantly cheaper per token than Pro). With its multimodal skills, Flash can be used in creative tools (like generating images from a prompt or doing basic video analysis), customer support (text or voice-based with emotional awareness), and productivity apps (summarizing documents, answering questions from PDFs, etc.). Basically, it covers the majority of tasks one might want an AI to do, with the advantage of being faster and more affordable to run than the largest models.

  • Latency and Efficiency: As the name implies, Flash is tuned for fast responses. Google hasn’t shared exact latency numbers publicly, but anecdotal reports suggest it’s comparable to or faster than GPT-3.5 in many cases, thanks to efficient serving and perhaps a smaller architecture than Pro. It also includes features like “adjustable thinking budgets” – meaning developers can set how much reasoning time/tokens Flash should spend. If you need a super-fast answer and are okay with a slight quality hit, you can cap the “thinking”. Conversely, you can let it think a bit more (at cost of latency) to boost accuracy, all under the hood. This kind of control is quite innovative, allowing dynamic trade-offs per request.

  • Context and Memory: Flash supports the same 1,048,576-token context window as Pro. So it can take in enormous inputs even though it’s a “lighter” model. It also supports context caching, meaning repeated parts of the prompt can be reused at lower cost, an important feature for applications that have a lot of static context with each query (similar to Anthropic’s prompt caching). Like Pro, Flash doesn’t retain long-term memory between sessions on its own, but in a deployed system it can utilize Google’s Vertex AI Retrieval (RAG Engine) to fetch relevant info when needed.

  • Integration and API: Gemini 2.5 Flash is available on Vertex AI (Google Cloud) as a managed model endpoint, and via the Gemini API for developers. It’s also accessible in the Google AI Studio web interface for trying out. Many of Google’s products likely use Flash under the hood for responsive AI features – e.g., perhaps in Google Sheets (Help me formula), or in Android’s on-device assistants (for something like Pixel’s Summarize feature). Flash is offered in both online inference and batch mode (with half-price for batch, as mentioned). Additionally, Gemini 2.5 Flash-Lite was introduced (a further distilled model for ultra-high-volume tasks, even cheaper and faster, though with lower capability). Flash-Lite is not as capable in reasoning, but for simple tasks it’s extremely cost-effective.

  • Pricing: Per Google’s pricing sheet, Gemini 2.5 Flash (paid tier) costs $0.30 per million input tokens (for text) and $2.50 per million output tokens. If the Flash model “thinks” with chain-of-thought, those reasoning tokens are counted in the output token price. There’s also a small premium for audio inputs (since audio needs processing) at $1.00/M input. Compared to Pro, Flash is much cheaper – roughly one-sixth the cost on output tokens. This means for many applications, Flash provides a great price-performance sweet spot. Google also provides a free usage quota on the API (for development/testing) and AI Studio is free to experiment with up to certain limits.


Gemini 2.5 Pro

Overview: Gemini 2.5 Pro (Experimental) is Google’s top-tier large language model as of 2025. It’s described as “our most advanced model for complex tasks”, incorporating the full might of Google’s AI research. It leads many benchmarks and is positioned to tackle challenging problems that require top-notch reasoning, coding, and multimodal understanding.

  • Advanced Reasoning: Gemini 2.5 Pro is built as a “thinking model” – it natively employs sophisticated reasoning strategies. For example, it achieved state-of-the-art results on math and science benchmarks like GPQA and AIME 2025. Without using extra tricks like majority voting, it leads those categories, showcasing how well it can handle problems that require careful step-by-step logic (AIME is a math competition for example). It also scored 18.8% on Humanity’s Last Exam (HLE), which might sound low but HLE is extremely difficult (it’s designed to be near human-level challenge; other models often score in the low teens or single digits). This was the top score among models not using external tools, indicating Gemini’s core reasoning is very strong. Google has put emphasis on things like Multi-Round Coherence (MRCR tests) as well – they updated results showing Gemini 2.5 Pro handles tricky coreference and multi-turn reasoning better than prior models.

  • Coding and Agents: On the coding side, Gemini 2.5 Pro excels at not only writing code, but doing so agentically. Google reported it got 63.8% on SWE-Bench Verified when using a custom agent approach. While that’s below Claude’s score, it’s still very high and likely second-best among known models in mid-2025. Moreover, Gemini Pro can produce visually rich outputs like web apps or games. Demos show it creating interactive animations and JavaScript apps from scratch by reasoning about the problem (similar to GPT-5’s coding prowess). Pro is also used for tasks like writing complex SQL queries, analyzing big spreadsheets, or orchestrating cloud workflows – things that need it to plan and possibly break a problem into parts.

  • Multimodality: Gemini 2.5 Pro has native multimodal comprehension. It can analyze images in detail (e.g., describing what’s in an image, understanding charts, reading handwriting), transcribe and interpret audio (including nuances like who is speaking, the sentiment, etc.), and even process video content (like summarizing a video or answering questions about it). It was built on multimodal training from the start, unlike GPT-4 which added vision later. As such, Pro can handle queries that involve multiple data types seamlessly. For instance, a user could input a PDF document, some related images, and a question – Gemini Pro could read the PDF, look at the images, and answer the question drawing on all provided info. The output is typically text, but its understanding spans modalities. (Note: for image generation, Google provides separate models like “Gemini 2.5 Flash Image”; Pro itself doesn’t output images in a chat, focusing instead on analysis and text synthesis).

  • Context Window: Like Flash, Pro currently supports a 1,048,576 token context (which is about 800k words). Google has mentioned work on expanding this to 2 million tokens in the near future. This enormous context means Gemini Pro can literally take in multiple books or an entire code repository as input. It’s useful for enterprises that want to feed their entire knowledge base or large datasets into the model for analysis. For example, Pro could be asked to read a year’s worth of company reports (thousands of pages) and answer questions – all in one prompt. Managing such context is non-trivial, but Google’s internal testing (per the tech report) shows it performs strongly even as context grows, thanks to efficient retrieval and attention mechanisms.

  • Integration and Services: Gemini 2.5 Pro is available through Google AI Studio (for interactive exploration) and the Vertex AI Model Garden for API access. By late 2025, it was still in preview on Vertex AI (but open to many customers) and was expected to reach general availability soon. Google offers Gemini App (gemini.google.com) for users with “Gemini Advanced” access to try Pro in a chat interface. This is akin to ChatGPT but by Google. We can expect Pro’s capabilities to surface in various Google products: for instance, Google Cloud’s Duet AI uses these models to assist developers in writing code and answering questions in Google Cloud Console. Also, Google Workspace features (like “Help me write” in Gmail/Docs) may start using Gemini Pro for enterprise customers who opt in, to get better quality outputs than the earlier PaLM 2 model.

  • Benchmark Leadership: Google proudly noted that Gemini 2.5 Pro debuted at #1 on the LMArena leaderboard by a significant margin. LMArena is a platform measuring human preference between model outputs across many tasks – being #1 means humans judged Gemini Pro’s answers to be the best overall among many AI models. This implies Pro has a well-rounded, high-quality style (not just raw scores). It’s likely ahead of GPT-4 in those evaluations, and perhaps on par or above GPT-5 (depending when GPT-5 was added to such leaderboards). The positive reception indicates Google achieved a major leap with Gemini 2.5, catching up in areas they lagged (Gemini 1.x and PaLM2 were behind GPT-4) and even surpassing in some.

  • Safety and Alignment: In their technical documentation, Google emphasizes “safety approach” for Gemini, including careful dataset curation, reinforcement learning from human feedback, and evaluations on toxicity, bias, etc.. They also mention sustainability and transparency in the model card. While details are sparse publicly, Google being an enterprise-focused provider likely means Gemini Pro has robust filtering and policy management (especially since it’s offered via Google Cloud with compliance options). One unique aspect is that Google allows users to choose whether their model usage data is used to improve Google’s models (this is in the pricing sheet snippet). Many businesses will appreciate that control for privacy.

  • Pricing: As per TechCrunch and Google’s docs, Gemini 2.5 Pro’s pricing is:

    • $1.25 per million input tokens (if prompt <= 200k tokens; $2.50 if >200k).

    • $10 per million output tokens (<=200k; $15 if >200k). This is indeed Google’s most expensive model to date. It’s more costly for developers than Gemini 2.0 was (which was $0.10/$0.40 per million), and even more than other competitor models in some cases. However, it’s slightly cheaper than Anthropic’s Claude 3.7 was and much cheaper than OpenAI’s ultra-large GPT-4.5 preview. Google seems to justify the price with the higher context limit and performance. They also provide a free tier with strict rate limits for Pro (AI Studio usage is free to try, and the API free tier allows some tokens per month for evaluation). It’s noteworthy that if you use very long contexts, the price goes up (since >200k token contexts presumably use more memory or a different model parallelism). Also, Google charges for thinking tokens as output tokens – meaning the more the model “thinks” internally, the more output tokens counted (OpenAI’s API similarly would charge if the model generates more tokens in its explanation, but OpenAI doesn’t show the chain-of-thought to the user; Google might be counting hidden reasoning steps if they’re enabled and viewable). Additionally, Google offers discounts via batch mode (50% off) and other quotas, as well as provisioned throughput contracts for businesses that need guaranteed capacity.


In summary, Gemini 2.5 Pro is at the cutting edge of AI, comparable to OpenAI’s GPT-5 in many respects. It brings together Google’s expertise in scale (large context, multimodal training) and techniques like chain-of-thought to produce a model that can not only chat, but plan, reason, code, analyze and create across various mediums. With Flash and Pro, Google provides a tiered approach: use Flash for everyday scalable deployments and Pro for the hardest tasks. As of September 2025, Gemini 2.5 Pro stands as a top competitor to OpenAI and Anthropic’s best, often differentiated by its tight integration with Google’s ecosystem and its emphasis on “let the model think things through” for better accuracy.


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