ChatGPT-4.5 vs ChatGPT-4.1: Full Comparison and Report on Performance, Features, Developer tools, Pricing, and Limitations

ChatGPT-4.5 and ChatGPT-4.1 are both advanced versions of the GPT-4 series developed by OpenAI.

ChatGPT-4.5 was introduced as a high-capacity, multimodal model with a focus on language quality and visual input handling. ChatGPT-4.1, on the other hand, represents a new direction emphasizing efficiency, coding accuracy, and long-context capabilities.

The two models differ significantly in performance, cost, API access, and suitable use cases.

GPT-4.5 excels in fluent conversation, image analysis, and creative writing tasks.

GPT-4.1 dominates in code generation, problem-solving, and processing long documents.

While GPT-4.5 was offered as a premium preview with limited availability, GPT-4.1 is positioned as a scalable API-first model for broad developer use.

FULL REPORT INDEX:

1. Performance

Speed and Latency: ChatGPT-4.5 is a massive model (~12.8 trillion parameters) focused on high performance, but it comes with high computational load. In practice, it generates text at roughly 37 tokens per second. This made it one of the fastest GPT-4 versions in raw generation speed, suitable for rapid responses. ChatGPT-4.1 takes a different approach by offering multiple model sizes (base, mini, and nano) optimized for latency. The GPT-4.1 mini model cuts latency nearly in half compared to the original GPT-4 (GPT-4o), while GPT-4.1 nano is even faster (the fastest and cheapest model OpenAI offers). In other words, GPT-4.1’s smaller variants sacrifice some scale to significantly reduce response time, making it more efficient for real-time applications. Overall, ChatGPT-4.1 provides more options on the latency curve, whereas 4.5 was a one-size (very large) model.

Accuracy and Reasoning: Both models are high-end GPT-4-series, but their focus differs. ChatGPT-4.5 was tuned to produce very fluent, coherent answers with an improved emotional and conversational tone. Users noticed its outputs are more polished and natural-sounding, excelling at storytelling and empathetic responses. However, testers reported that GPT-4.5 did not markedly improve logical reasoning or problem-solving over the original GPT-4; in some multi-step reasoning tasks it even struggled or made more errors, failing to self-correct unless guided. By contrast, ChatGPT-4.1 shows notable reasoning and task-performance improvements. OpenAI specifically optimized GPT-4.1 for better instruction-following and complex task completion. For example, on a coding benchmark (SWE-bench Verified), GPT-4.1 scored 54.6%, which is 26.6 percentage points higher than GPT-4.5’s score on the same test. This indicates a leap in problem-solving ability for coding and structured tasks. Similarly, GPT-4.1 demonstrated a 10.5% improvement over GPT-4 (GPT-4o) in a general instruction-following challenge, reflecting better reasoning when following complex user instructions. In summary, GPT-4.5 refines language quality, while GPT-4.1 delivers more intelligence gains – especially for technical and logical tasks.

Context Length and Memory: One of the biggest differences is the context window (how much conversation or data the model can remember and process at once). ChatGPT-4.5 introduced an expanded context of 128K tokens, a huge jump from the original 8K/32K tokens of GPT-4. In theory this allows GPT-4.5 to ingest very large documents or lengthy conversations in one go. However, OpenAI did not advertise a breakthrough in long-term retention or utilization of context for GPT-4.5 – anecdotal evidence suggested no clear memory retention improvement over GPT-4 in practice. ChatGPT-4.1, on the other hand, pushes context length to an unprecedented 1 million tokens. This 10× larger context means GPT-4.1 can handle extraordinarily long inputs (equivalent to hundreds of pages of text) in a single session. More importantly, GPT-4.1 was designed with improved long-context comprehension, meaning it can make better use of that lengthy input without losing track. This makes GPT-4.1 especially powerful for tasks like analyzing large documents, multi-document summarization, or extended dialogues. In summary, while GPT-4.5 already offered a very large context window (128K), GPT-4.1 extends this eight-fold and is engineered to use long contexts more effectively, essentially giving it a much better “working memory” for complex tasks.

2. Features and Capabilities

Multimodal Inputs (Image Processing): A key feature that distinguished ChatGPT-4.5 is its multimodal capability. GPT-4.5 can accept both text and image inputs, allowing it to analyze or describe images in addition to text. This was in line with OpenAI’s push towards GPT-4 Vision, making ChatGPT-4.5 useful for tasks like interpreting charts, recognizing objects in photos, or reading screenshots. In contrast, ChatGPT-4.1 is currently a text-only model – it does not accept image inputs. GPT-4.1 was introduced via the API for text processing and code, without the vision features. So for any application involving images or other modalities, GPT-4.5 has the clear advantage, whereas GPT-4.1 focuses on pure language (and code) tasks.

Natural Language Understanding & Generation: ChatGPT-4.5 introduced enhancements in the quality of generated text. It produces responses that are remarkably fluent and coherent, often more so than earlier GPT-4 versions. Its language generation tends to feel more refined, with better handling of tone and nuance, which benefits creative writing, storytelling, and empathetic conversation. Users have noted that if given a well-structured prompt, GPT-4.5 returns very polished and elaborate answers. ChatGPT-4.1 also maintains high proficiency in natural language generation (it inherits GPT-4’s strengths) but much of its improvement effort went into fidelity and correctness rather than creative style. GPT-4.1 is highly attuned to following instructions exactly and producing useful, on-point answers. In practice, GPT-4.1 might be slightly more terse or straightforward in its replies compared to GPT-4.5’s eloquence. OpenAI has noted that many of GPT-4.1’s upgrades (in instruction following, coding, etc.) have been gradually incorporated into the ChatGPT “GPT-4 (Latest)” model as well, so end-users of ChatGPT may have already experienced some of 4.1’s improved clarity and accuracy in understanding prompts. Overall, GPT-4.5 leads in expressiveness and tone, while GPT-4.1 emphasizes precision and adherence to queries.

Coding and Reasoning Skills: One of ChatGPT-4.1’s standout capabilities is its coding prowess. OpenAI specifically trained GPT-4.1 to excel in software engineering tasks – it can generate code, debug, follow diff/patch instructions, and use tools like an AI pair programmer with far greater reliability than before. In benchmarks, GPT-4.1 outperforms both GPT-4 and GPT-4.5 on coding challenges: for instance, it solves 54.6% of tasks on SWE-Bench (software engineering benchmark), whereas GPT-4.5 scored significantly lower on the same test. Developers have observed that GPT-4.1 produces code with fewer errors and is better at understanding repositories to make targeted changes. By contrast, ChatGPT-4.5 did not focus on improving coding or mathematical reasoning. In fact, evaluations showed GPT-4.5 was often no better (or slightly worse) at complex reasoning than GPT-4. For example, on math problem benchmarks like the AIME 2024 exam, GPT-4.5 solved fewer problems (36.7% correct) compared to GPT-4.1 (48.1%). Thus, for tasks like programming, data analysis, or logical problem-solving, GPT-4.1 has a clear edge in capability. GPT-4.5’s strengths lie elsewhere – in general knowledge and language fluency – whereas GPT-4.1 is almost a specialist model for coding and structured reasoning (while still being a strong general AI).

Tool Use and API Functions: OpenAI also introduced new tool-use features with these models. ChatGPT-4.5 came with advanced function calling abilities, which means it can more reliably output JSON or call developer-defined functions to perform actions (a feature OpenAI introduced in mid-2023). GPT-4.5’s large size and training helped it interpret function calling instructions and plugin tools with better accuracy, benefiting developers integrating it via the API. It also supports streaming responses, sending partial output in real-time for a smoother experience. GPT-4.1 continues along this path: it has been trained to follow structured formats (like diffs, JSON, code blocks) more reliably, ensuring it works well with tools that require a certain output format. OpenAI also announced platform-level enhancements alongside GPT-4.1, such as a new “Responses API” to easily orchestrate multi-step agent-like interactions, and “Predicted Outputs” to cache and speed up repeated queries. These aren’t model features per se, but they show how GPT-4.1 was delivered with developers in mind – e.g., you can build AI agents that leverage GPT-4.1’s strengths (long context, coding) more effectively with these tools. In short, GPT-4.5 expanded the model’s direct capabilities (vision, function calls), while GPT-4.1 combined model improvements with new developer tools for integration.

3. API Usage and Developer Access

Availability: ChatGPT-4.5 was initially rolled out in a limited way. OpenAI first made GPT-4.5 available via ChatGPT Pro, a higher-tier early access program, and later to all ChatGPT Plus and enterprise Team users in the ChatGPT interface. At the time of its introduction (Feb 2025), GPT-4.5 was not broadly released via the standard API to all developers; it was a research preview model. Eventually, OpenAI allowed some API access to GPT-4.5 as a “GPT-4.5 Preview” model, but this is now being deprecated. In April 2025 OpenAI announced that GPT-4.5 Preview will be turned off by July 14, 2025, encouraging developers to transition to GPT-4.1. In contrast, ChatGPT-4.1 was launched directly as an API offering for all developers (April 14, 2025). OpenAI made the GPT-4.1 family available in the API from day one, while noting that the ChatGPT consumer app would continue using the older GPT-4 model (with some incremental improvements) for a while. In summary, GPT-4.5 lived mostly inside ChatGPT (for subscribed users) with limited API life, whereas GPT-4.1 is an API-first model aimed at broad developer adoption.

Integration and Compatibility: Both models are part of OpenAI’s GPT-4 series and thus compatible with the general OpenAI API, but there are differences in integration options. GPT-4.5’s multimodal ability was accessible in the ChatGPT interface (users could upload images to ChatGPT-4.5). However, OpenAI’s API did not initially support image uploads for GPT-4.5 except via specific endpoints or products (e.g. vision was mainly in ChatGPT UI). GPT-4.1 being text-only avoids this complexity. For function calling and tool integration, both GPT-4.5 and GPT-4.1 support the functions parameter in the API (to define tools the model can call). GPT-4.5’s larger model may handle complex function outputs well, but GPT-4.1 was reported to be very reliable in producing structured outputs and code edits, which aids integration with developer tools. Another aspect is context usage: calling GPT-4.5 with extremely large 100K+ token inputs was technically possible but very expensive and likely restricted. GPT-4.1’s API allows up to the full 1M-token context, but OpenAI introduced features like blended context (using smaller models for parts of context) and caching to manage cost. Developers using GPT-4.1 can take advantage of a 75% discount on repeated (“cached”) input tokens, which encourages efficient reuse of prompts in conversations. Overall, GPT-4.1 offers more flexible integration: multiple model sizes (for scaling latency/cost), tools to handle long contexts, and pricing incentives. GPT-4.5 integration was more limited, and given its imminent deprecation, new development is focused on GPT-4.1 and beyond.

Developer Controls and Usage Patterns: OpenAI’s release of GPT-4.1 came with updated developer controls. The API for GPT-4.1 supports up to 32,768 tokens in a single output (double the 16K output limit of GPT-4.5), which is useful for generating long completions (e.g., lengthy reports or code files). Developers can also choose GPT-4.1 mini or nano for lower-cost inference without changing their code – these are essentially scaled-down versions accessible via different model IDs. GPT-4.5 did not have such size variants; it was a monolithic model (often just one engine). In terms of usage patterns, GPT-4.5 being costly meant developers had to be judicious with prompts and often rely on ChatGPT UI for interactive use. GPT-4.1’s cost-efficiency (discussed below) encourages more frequent and larger-scale use in applications – for example, one can build an agent that reads an entire book (with the 1M token window) and still affordably get results. Additionally, OpenAI indicated that feedback from the GPT-4.5 preview was used to bring the “creativity, writing quality, humor, and nuance” of GPT-4.5 into future models. Thus, GPT-4.1’s API may reflect some of those qualitative tuning improvements but in a model that is easier for developers to work with (faster and cheaper). Finally, it’s worth noting neither model is open-source (they are offered through OpenAI and Azure endpoints), so integration is through cloud APIs only. In summary, GPT-4.5 was a powerful but heavy tool with limited developer controls, whereas GPT-4.1 is delivered as a more versatile platform with configurable model sizes, better context tools, and clear migration support for developers.

4. Costs and Pricing

One of the most striking differences between ChatGPT-4.5 and ChatGPT-4.1 is the cost and pricing structure. The table below summarizes the official API pricing for each model (note: ChatGPT UI usage is subscription-based, but API costs are per token):

Token Cost Type	GPT-4.5 (Preview API)	GPT-4.1 (API)
Input tokens (prompt)	$75.00 per million tokens	$2.00 per million tokens
Output tokens (completion)	$150.00 per million tokens	$8.00 per million tokens

As shown above, GPT-4.5 was extremely expensive to use via the API. Its input token price was $75 per 1M tokens and output was $150 per 1M – on the order of 40–75× more expensive than regular GPT-4 pricing. In contrast, GPT-4.1’s pricing is dramatically lower, at $2 per 1M input tokens and $8 per 1M output tokens. This makes GPT-4.1 roughly 22.5× cheaper than GPT-4.5 on a per-token basis, a huge reduction in cost. In practical terms, a large query that might have cost several dollars with GPT-4.5 could cost only a few cents with GPT-4.1, enabling broader usage. OpenAI was able to lower the price so much in part because GPT-4.1 models are more efficient (and possibly smaller) than the giant GPT-4.5 model.

In terms of subscription models, ChatGPT-4.5 was available to paying ChatGPT users without an explicit per-token charge – ChatGPT Plus ($20/month) subscribers eventually gained access to GPT-4.5 as the default “GPT-4” model in the interface, and some features (like vision) required that subscription. There was also mention of a ChatGPT Pro tier during GPT-4.5’s early rollout, implying some users might have paid more for priority or early access. However,

OpenAI did not change the base pricing of ChatGPT Plus when introducing GPT-4.5; it was bundled as part of the paid plan. For GPT-4.1, there is no ChatGPT Plus integration yet – it is purely an API model. So, instead of a subscription, developers pay usage-based fees as per the table above. Notably, OpenAI has made GPT-4.1 available to all API developers without waitlist, meaning anyone with an API key can use it and pay per token.

Another aspect is token limits and billing differences. Because GPT-4.5 has a 128K context, using it to the full context is very costly – e.g. processing the full 128,000-token input could cost on the order of $9.6 in input charges per request, and the output is capped around 16K tokens (which could add another ~$2.4). In practice, OpenAI might have imposed some limits or required approval for such large contexts to prevent accidental huge bills. GPT-4.1’s 1M token context, if fully used, would cost $2 (for input) in the worst case – a fraction of GPT-4.5’s cost – which shows how much more economical it is to handle long documents with GPT-4.1.

Additionally, GPT-4.1’s pricing includes a 75% discount on cached input tokens. This means if you send the same large context multiple times (as part of a conversation or re-use a document across requests), the repeated portions are charged at only 25% of the normal rate. Such pricing incentives further reduce effective cost for persistent contexts. OpenAI also hinted at blended usage pricing – possibly using smaller models for parts of the prompt at even lower cost – though details are in their docs.

To summarize, ChatGPT-4.5 was a premium, expensive model – likely intended for limited use cases – whereas ChatGPT-4.1 is cost-optimized for wide adoption. The introduction of GPT-4.1 slashed the price of advanced GPT models by orders of magnitude, making features like million-token contexts financially feasible. For developers and businesses, this means GPT-4.1 is much more budget-friendly to integrate. From a user perspective, ChatGPT Plus subscribers benefited from GPT-4.5’s capabilities at a fixed monthly cost, but going forward OpenAI is focusing on GPT-4.1 in the API and presumably will fold improvements into the ChatGPT product without extra charge.

5. Use Cases and Limitations

Recommended Use Cases – ChatGPT-4.5: GPT-4.5 is well-suited for applications that require rich, nuanced language output and the ability to handle images. Its knack for fluent storytelling and empathetic tone makes it ideal for creative writing, narrative generation, and customer support or counseling chatbots that need to sound human-like. If a task involves describing or analyzing images (for example, explaining the content of a picture, reading a diagram, or solving a visual problem), GPT-4.5 is the appropriate choice due to its multimodal input support. It also has “enhanced general knowledge,” meaning it was trained on a broad dataset (with knowledge up to late 2023) and can handle open-domain Q&A or general information tasks confidently. In scenarios where emotional intelligence is valued – say a chatbot providing mental health support or a game NPC with personality – GPT-4.5’s polished and emotionally aware responses shine. Essentially, use GPT-4.5 when output quality and creativity are paramount, and when visuals or high-level reasoning with images are involved.

Recommended Use Cases – ChatGPT-4.1: GPT-4.1 was built to excel in technical and high-precision tasks. A prime use case is as a coding assistant or software engineer’s helper – it can write functions, debug code, generate patches, and understand complex programming instructions more effectively than its predecessors. Developers can leverage GPT-4.1 for tasks like code generation, code review, and working with large codebases (using the long context to ingest multiple files or extensive documentation). Another key use case is any application involving large text analysis: GPT-4.1 can take in hundreds of thousands of tokens of text (books, lengthy reports, knowledge bases) and perform summarization, extraction of insights, or question-answering over that content. This capability opens up possibilities for legal document analysis, research paper summarization, or processing logs and transcripts in one go. GPT-4.1 is also ideal for building agent systems that maintain long conversations or multi-step reasoning over extended contexts – for example, an AI that can remember the entire history of a user’s queries or a multi-turn task planner – since it handles long contexts with improved reliability. In short, use GPT-4.1 for coding, complex problem-solving, lengthy document tasks, and when cost-effective scaling is important (it’s far cheaper per call, enabling high-volume or real-time use cases).

Limitations and Differences: Each model has its limitations. For ChatGPT-4.5, a notable limitation was its lack of significant reasoning improvement over GPT-4. While it writes better, it can still falter on logical consistency and complex calculations. In certain tests of reasoning or math, GPT-4.5 sometimes underperformed GPT-4 (and later GPT-4.1), indicating that its massive size didn’t translate to better accuracy on those fronts. It can be more prone to overlooking its own mistakes or contradictions unless explicitly told to double-check. Another limitation of GPT-4.5 is practical: its enormous model size made it resource-intensive and costly, which is one reason OpenAI treated it as a short-lived preview. Indeed, GPT-4.5 is being deprecated in favor of 4.1, suggesting that maintaining such a huge model was not efficient. On the user side, the 128K context—while impressive—might have been underutilized due to cost and interface constraints, so the real benefit of that large window was limited. Lastly, GPT-4.5’s image understanding, while powerful, is constrained by what the model was trained on and it may not always get fine details right (and of course, it cannot output images, only describe them). Privacy or security considerations also apply; sending images to the model shares that data with OpenAI’s servers.

ChatGPT-4.1’s limitations are somewhat the mirror image of 4.5’s. Because it is tuned for accuracy and code, some users speculate it may be a bit less creative or “spontaneous” in open-ended tasks – it might default to concise factual answers where GPT-4.5 might have given a more narrative response. Its focus on following instructions means it could be less inclined to produce humorous or highly imaginative content unless prompted to do so (OpenAI has noted they will carry over GPT-4.5’s humor/creativity into future models, but it’s unclear if 4.1 fully matches 4.5 in that regard). Another limitation is that GPT-4.1 has no vision capability: if your use case requires analyzing images, GPT-4.1 cannot help, and you’d need to use a different model (like GPT-4.5 or another vision-capable model). In terms of knowledge cutoff, GPT-4.1 is more up-to-date (training data through June 2024) than GPT-4.5 (which was through October 2023). This means GPT-4.1 might know about slightly newer events or research. However, neither model knows the very latest happenings beyond their cutoffs, and they don’t have internet access by default. Finally, accessibility is a consideration: GPT-4.1 currently requires using the API (and paying per token), which might be a barrier for some hobby users who rely on the ChatGPT UI. ChatGPT-4.5, being available in the ChatGPT Plus interface, was easier to experiment with interactively for non-developers. Over time, we expect GPT-4.1’s improvements to be integrated into the ChatGPT app, but as of now 4.1 is a developer-centric release.

Regressions: Interestingly, in certain areas ChatGPT-4.5 can be seen as having regressions compared to ChatGPT-4.1 (despite the version number suggesting otherwise). We’ve already noted that coding tasks and math are areas where GPT-4.1 overtakes 4.5 significantly. For example, GPT-4.1’s superior AIME math score and SWE-Bench coding score show that GPT-4.5 was not the best choice for those uses. Another point is cost-effectiveness: one could say GPT-4.5 regressed in practical usability because its cost put it out of reach for many applications – whereas GPT-4.1 corrected this by being much cheaper for equal or better performance. OpenAI themselves implied GPT-4.5 was something of an experimental dead-end that would be “carried forward” in spirit but replaced by the more efficient GPT-4.1. In summary, GPT-4.5’s legacy is a model that improved the user experience in language quality but at great expense, while GPT-4.1 manages to advance the technical capabilities and lower the barriers. Choosing between them (where both are available) depends on the task: use GPT-4.5 for rich, multimodal conversational experiences, and GPT-4.1 for high-precision, large-context, and cost-sensitive tasks.

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