ChatGPT vs Claude: Full report and comparison on models, features, performance, pricing, and use cases
- Graziano Stefanelli
- 21 hours ago
- 7 min read
ChatGPT (OpenAI) and Claude (Anthropic) have matured into two distinct ecosystems with different strengths in reasoning depth, cost profiles, integrations, and workflow automation. This report maps the current public model lineups, explains how they behave in real work, and offers workload-specific recommendations with tables and practical guidance.
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What the current public models include and how they are positioned.
OpenAI exposes GPT-5 as the default experience across ChatGPT’s web and mobile interfaces, while GPT-4.1, GPT-4o, and the o-series models (o3, o4-mini) remain selectable under “More Models.” Each variant targets a trade-off between reasoning depth, speed, and price. GPT-5 operates as a unified system: a central router determines whether a query is handled in a fast or deliberate reasoning mode.
Anthropic’s Claude platform now revolves around the Claude 4.5 generation. It consists of three publicly available models: Haiku 4.5, Sonnet 4.5, and Opus 4.1.
Haiku 4.5 emphasizes high speed and low cost, powering the free tier.
Sonnet 4.5 is the balanced, high-performance model available to Pro and Max subscribers, offering near-Opus reasoning at a fraction of the price.
Opus 4.1 remains the premium, frontier-grade model tuned for exhaustive reasoning, long-context workflows, and code precision.
Public model lineup and positioning
Vendor | Model name | Context window | Access tier | Core purpose |
OpenAI (ChatGPT) | GPT-5 | 400 k tokens (app/API) | Free, Plus, Pro, Enterprise | Flagship model combining reasoning, speed, and multimodality |
GPT-4.1, GPT-4o, o3, o4-mini | 128 k–200 k tokens | Paid / Admin toggle | Fast legacy or cost-optimized variants | |
Anthropic (Claude) | Haiku 4.5 | 200 k tokens | Free | Entry-level for general tasks |
Sonnet 4.5 | 200 k tokens | Pro / Max / API | Advanced coding and agent workflows | |
Opus 4.1 | 200 k tokens | Max / Enterprise / API | Frontier reasoning, long research, and reliability-critical tasks |
Anthropic’s lineup is simpler and more vertically tiered, while OpenAI’s structure is broader, offering multiple generations simultaneously for flexibility and backward compatibility.
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How model behavior and context windows affect real tasks.
Both companies now operate in the “large-context era,” where practical differences lie less in absolute window size and more in how models use that window. GPT-5’s router decides internally how to allocate compute — a 20-token note may use the fast path, while a 50-page research file triggers a deliberate reasoning mode. Claude models, by contrast, are explicitly long-memory systems that maintain coherence across very long dialogues.
Context handling and reasoning orientation
Model | Reasoning design | Memory retention behavior | Typical output tone |
GPT-5 | Dynamic router toggling between quick and extended thinking | Session-based; resets on new chat; enterprise tier adds temporary memory | Polished, balanced between formal and conversational |
Claude Opus 4.1 | Fixed deliberate reasoning | Very long coherence; handles recursive summaries | Analytical and cautious |
Claude Sonnet 4.5 | Hybrid: instant for simple, extended for hard | Maintains structured state for hours-long tasks | Concise, technical, agent-style |
For spreadsheet-scale or document-scale workloads, all three can sustain several hundred pages of data. GPT-5’s 400 k window gives more one-shot breadth; Claude’s architecture ensures that even after many iterative turns, earlier context remains integrated rather than summarized.
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How coding and complex reasoning strengths diverge.
The most concrete difference between ChatGPT and Claude appears in code comprehension, refactoring, and logical reasoning chains. OpenAI’s GPT-5 integrates the same code execution environment used in ChatGPT’s Python sandbox and GitHub Copilot. Anthropic’s Claude models are designed for autonomy: they reason, plan, and modify code without continuous user prompts.
Technical performance summary
Capability | GPT-5 | Claude Opus 4.1 | Claude Sonnet 4.5 |
Coding accuracy (SWE-Bench Verified) | 74.9 % | 74.5 % | 77.2 % (best published) |
Reasoning depth | Dynamic, router-controlled | Deterministic, explicit chain-of-thought | Adaptive long-horizon reasoning |
Refactor conservatism | Moderate | High (minimal-diff editing) | High, faster throughput |
Mathematical reasoning | Excellent (90 % + GSM8K) | Excellent | Excellent |
Autonomy / tool use | Plugins + Code Interpreter | Agent SDK, stable multi-tool calls | Multi-agent orchestration, parallel tasks |
In practice
GPT-5: best for iterative coding, creative prototyping, and integration with existing dev tools.
Opus 4.1: ideal for complex refactoring in regulated or safety-critical environments where accuracy outweighs cost.
Sonnet 4.5: emerging as the most practical daily development model for long projects due to its 5× cheaper token rate and lower latency.
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How multimodality, files, and tools change real workflows.
OpenAI’s ChatGPT ecosystem now functions as a workspace rather than a simple chatbot. GPT-5 can analyze PDFs, tables, charts, and images; record or respond in real-time voice; and integrate with hundreds of plugins. Claude focuses on text and file reasoning, operating as a structured workspace for code, data, and knowledge management.
Feature-level comparison
Feature | GPT-5 (ChatGPT) | Claude Opus 4.1 / Sonnet 4.5 |
Text and image input | Native multimodal reasoning; strong on charts and photos | Text-centric, limited image parsing |
Voice interface | Two-way real-time voice (app) | No native voice; third-party integration possible |
File uploads | PDFs, CSV, XLSX, DOCX, slides | CSV, XLSX, DOCX, PDFs |
File memory | Temporary within session; persistent for enterprise | Memory tool stores context across sessions |
Plugins / connectors | Plugin store + custom GPTs + function calling | Agent SDK + Connectors + Chrome extension |
Browser / code execution | Built-in browsing and Python sandbox | Browsing (Pro+) + full code workspace |
Claude’s advantage lies in coherence and precision. While GPT-5 covers more modalities, Claude maintains exact logical consistency across very long chains of reasoning, making it ideal for regulated or research workflows.
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Pricing, plans, and token economics presented clearly.
Both ecosystems rely on token-based metering, but their pricing structures differ sharply. OpenAI’s GPT-5 is generally cheaper per token; Anthropic compensates with aggressive caching discounts and a lower-cost Sonnet tier.
API token pricing
Model | Input cost (per M tokens) | Output cost (per M tokens) | Approximate relative cost |
GPT-5 | $1.25 | $10.00 | Baseline = 1× |
Claude Sonnet 4.5 | $3.00 | $15.00 | ~1.5× GPT-5 per output token |
Claude Opus 4.1 | $15.00 | $75.00 | ~7.5× GPT-5 per output token |
Anthropic offers prompt caching (–90 %) and batch request (–50 %) discounts, which can make Sonnet nearly cost-parity with GPT-5 in high-volume deployments.
Consumer and enterprise plan summary
Platform | Free Tier | Pro/Plus Tier | Enterprise |
ChatGPT | GPT-5 access with limits | $20 / $200 per month depending on plan | Custom; admin console, SSO, high-context |
Claude | Haiku 4.5 (daily limits) | $20 Pro / $100 Max (more requests, priority) | Team/Enterprise with workspace governance |
In large organizations, the total cost depends on task patterns: GPT-5 wins for short, frequent queries; Claude Sonnet wins for continuous, high-context operations that benefit from caching.
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Benchmarks are close overall but diverge in coding and agents.
Performance across general reasoning benchmarks is now nearly saturated—both GPT-5 and Claude models exceed 85 % + accuracy on MMLU-Pro and 90 % + on GSM8K. The practical gap emerges in applied tasks.
Consolidated benchmark overview
Benchmark Type | GPT-5 | Claude Opus 4.1 | Claude Sonnet 4.5 |
MMLU (academic) | 87 % | 88 % | 87 % |
GSM8K (math) | 94 % | 95 % | 91 % |
SWE-Bench Verified (coding) | 74.9 % | 74.5 % | 77.2 % |
Agentic/Tool Use (TAU-Bench) | High | Highest | Highest |
Long-run stability | Very Good | Excellent | Excellent |
GPT-5’s router grants adaptability and speed, but in complex workflows involving multi-step reasoning and tool coordination, Claude’s deterministic approach yields higher repeatability.
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Enterprise controls, privacy posture, and governance expectations.
Privacy and compliance have become central differentiators. Both providers enforce non-training isolation for business data. OpenAI highlights end-to-end encryption and SOC 2 compliance; Anthropic emphasizes auditable transparency and fine-grained safety tiers.
Enterprise governance comparison
Area | GPT-5 (OpenAI) | Claude 4.x (Anthropic) |
Data usage for training | Opt-out by default for Enterprise | Opt-out by default all tiers |
Audit & logging | Detailed per-user event logs | Workspace-level logs with model actions |
Admin features | SSO, usage dashboard, role permissions | SSO, workspace management, connectors |
Regulatory focus | SOC 2, GDPR, HIPAA (selected sectors) | SOC 2, GDPR, AI Safety Levels (ASL) |
Memory & retention | Optional memory; session isolation | Memory tool; checkpoint save/restore |
Deployment options | API, Azure OpenAI, embedded Copilot | API, Amazon Bedrock, Google Vertex AI |
In finance, legal, and healthcare workflows, Anthropic’s transparent reasoning trace can be an advantage; in general enterprise suites, OpenAI’s integration breadth is unmatched.
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Workload-based recommendations that match real teams.
Different teams value different traits: throughput, reasoning rigor, or stability. The tables below align model selection to organizational roles and workloads.
Model selection by team function
Department / Role | Preferred Model | Rationale |
Finance & Accounting | Claude Sonnet 4.5 | Handles long tables, variance analysis, and audit narratives coherently. |
Software Development | Claude Sonnet 4.5 / Opus 4.1 | Stable refactors, tool-driven agents, fine error control. |
Marketing & Content | GPT-5 | Versatile generation and style control; fast iteration. |
Legal & Compliance | Claude Opus 4.1 | Traceable reasoning; conservative tone; long-document accuracy. |
Research & Data Analysis | GPT-5 or Opus 4.1 | GPT-5 for multimodal, Opus for structured synthesis. |
Customer Support Automation | GPT-5 | High throughput, good summarization, integrated connectors. |
Cost and efficiency guideline
Usage pattern | Best choice | Why |
High-volume short chats | GPT-5 | Lower per-token cost and latency. |
Few but very long sessions | Claude Sonnet 4.5 | Caching makes long interactions efficient. |
Mission-critical reasoning | Claude Opus 4.1 | Accuracy prioritized over spend. |
Mixed creative + technical tasks | GPT-5 | Wider modality coverage. |
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Practical agent designs that keep quality high and costs under control.
Advanced teams increasingly combine models inside pipelines—routing tasks automatically based on complexity and length. This section distills effective operational patterns.
Operational patterns for hybrid deployments
Pattern | Implementation | Outcome |
Dynamic routing | Detect query length > n → send to Sonnet/Opus; else GPT-5 | Saves cost, preserves accuracy |
Checkpointed sessions | Claude memory tool checkpoints every 5k tokens | Zero context loss in multi-hour runs |
Batch processing | Group long documents for cached prompts | 50–90 % token cost reduction |
Multi-model fallback | If Claude returns refusal → retry GPT-5; log discrepancy | Resolves safety over-blocking |
Verification chain | Second model cross-checks first’s output summary | Reduces hallucination risk in production |
These strategies allow enterprises to leverage both ecosystems simultaneously, aligning compute use with business importance.
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Limitations and mitigation strategies you should factor into design.
Despite improvements, both models share predictable weaknesses. Understanding them early prevents operational friction.
Common limitations
Issue | Observed in | Mitigation |
Over-explanation or verbosity | Claude Opus 4.1 | Set explicit length targets (“≤ 150 words”) |
Occasional factual drift after long sessions | GPT-5 | Periodic summarization checkpoints |
Slower multi-step responses | All long-context models | Batch or cache intermediate summaries |
Recent-event blind spot (post-2024) | Both vendors | Enable browsing or retrieval plugins |
Cost growth in long reasoning mode | GPT-5 “Thinking” path | Use fast mode where precision margin allows |
Consistent prompt templates and external evaluation loops are essential for quality assurance at scale.
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What to deploy next with a simple decision path.
If your workflows are creative, document-heavy, or multimodal, deploy GPT-5 first; integrate file tools and plugin automations.
If your workflows are procedural, technical, or require sustained context, standardize on Claude Sonnet 4.5 for most users and keep Opus 4.1 reserved for critical reasoning pipelines.
Hybrid adoption strategy
Use GPT-5 for high-frequency, general communication and internal knowledge bases.
Use Claude Sonnet 4.5 for coding agents, RAG-based analysis, and long research projects.
Fallback to Opus 4.1 when interpretability, reliability, or auditability trump speed and cost.
Log, cache, and measure token usage to maintain predictable budgets.
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