GPT-4.1 Released: Benchmarks, Performance, and How to Safely Migrate to Production

1. Introduction

Developers are racing to create the next ground-breaking model that has the potential to surpass all previous models, as rumors about GPT-5 are generating excitement among the AI world. It's time to set new standards for how smart and useful AI can be. Will you be the one to do it? But before that, OpenAI released GPT-4.1.

GPT-4.1, OpenAI's most recent model series optimized for coding, long-context understanding, and instruction following through the API.

GPT-4.1 improves code correctness by 21% on SWE-Bench, which is important for developers. It also makes context windows bigger by up to 1 million tokens, so builds can work on bigger projects without losing track of the little things. Additionally, its faster speeds (up to 40% faster) and lower prices make it more affordable to run complicated AI processes. Let’s explore more about it. 

2. What’s New in GPT‑4.1 Compared to GPT-4?

• Release Variants: The primary GPT-4.1 model provides exceptional performance for challenging tasks, while the Mini version achieves a balance between throughput and cost. The Nano variant is designed to target ultra-low latency and affordability. You can choose the best trade-off between these parameters for real-time categorization and batch code production.

• Context Window Expansion: GPT-4.1 allows for a context window of up to 1 million tokens, which is an increase from the 128 K limit of GPT-4o. So, it enables the feeding of entire codebases or extensive reports in a single query. The model is prevented from "forgetting" earlier portions of long inputs by this expansion, which reduces the need for manual text chunking.

• API‑Only Distribution: OpenAI delivers GPT-4.1 via API, disabling GPT-4.5 preview by July 14, 2025, and removing previous ChatGPT models by April 2025. It also lowers the price of each token by about 26% compared to GPT-4o, which makes it a better choice for long-term production use.

• Updated Knowledge Cutoff: GPT-4.1's training involves data as recent as June 2024, which provides it with a more up-to-date understanding than GPT-4, which had an earlier cutoff. This upgrade ensures proper reactions to new technology, events, and research beyond GPT-4.

• Developer‑Focused: The GPT-4.1 family does 21% better on coding tests than the GPT-4o family. It also does a great job of following instructions exactly, which makes tasks like diff-style code changes more reliable. It simplifies sophisticated AI-driven pipelines without orchestration by maintaining state across multistep methods.

3. How Does GPT‑4.1 Perform on Key Benchmarks?

GPT-4.1 provides significant improvements in long-context comprehension, instruction following, and coding.

3.1 Coding Benchmarks

• SWE-bench Verified: GPT-4.1 comes out with a score of 54.6%, higher than GPT-4.5 (28%) and GPT-4o (33.2%), in the solution of real GitHub issues.

• Code Diff Accuracy: 52.9% is the accuracy of code diffs, more than twice the 18.3% attained by GPT‑4o when updating particular code sections.

• Unnecessary Changes: The Number of needless file changes dropped from 9% with GPT‑4o to just 2% with GPT‑4.1 in internals tests.

• Independent Verification: Based on Reuters, GPT‑4.1 shows how it performs in the real world by roughly 21% improving total code performance over GPT‑4o.

Figure 1: Coding Benchmarks: Source

3.2 Instruction‑Following Benchmarks

• MultiChallenge: GPT-4.1 marks a 38.3% on multi-turn context management, while GPT-4o marks a 27.8%.

• IFEval Compliance: It follows unambiguous instructions 87.4% of the time, which is higher than GPT-4o's 81.0% and generates more consistent results.

Figure 2: Instruction following benchmark: Source

3.3 Long‑Context Comprehension

Needle-in-Haystack: GPT-4.1 finds exact details with 100% accuracy over a full 1 million-token input.

Figure 3: Haystack Accuracy: Source

Video MME: Important for multimodal workflows, it outperforms GPT-4o by 6.7% in scoring 72% on 30-to 60-minute videos without subtitles.

Figure 4: Video MME: Source

GPT‑4.1 makes significant improvements in coding, instruction following, and long‑context comprehension. Cutting needless edits to 2% and doubling code-diff accuracy, it leaps to 54.6% on SWE‑bench Verified—over 20 points higher than GPT‑4o. On instruction benchmarks, it beats GPT‑4o by 10.5 and 6.4 points respectively: it nails 38.3% on multi-turn tasks and 87.4% compliance on IFEval. Long inputs find any "needle" in a 1-M‑token haystack with 100% accuracy and get 72% on Video MME—a 6.7% increase over its predecessor.

4. How Fast and Cost‑Efficient Is GPT‑4.1 in a Production Environment?

• Throughput: GPT-4.1 improves high-volume request handling by handling up to 132.7 tokens/sec in sustained output, which is about 40% faster than GPT-4.0.

• Latency: It improves interactivity and cuts more than a third off GPT-4o's 0.61s time to first token, delivering the first token in roughly 0.39s.

• Context window: The context window of GPT-4.1 is 1.0M tokens, which is larger than the average.

• API Pricing: The full GPT-4.1 model is priced at $2.00 per million input tokens and $8.00 per million output tokens, with Mini priced at $0.40/$1.60 and Nano priced at $0.10/$0.40.

• Best Practices for Cost Optimization: Choose the full GPT-4.1 model when you require the highest coding accuracy and the largest context window, GPT-4.1 Mini for bulk, cost-sensitive pipelines, and Nano for sub-second classification tasks.

5. GPT-4.1 Series 

OpenAI's GPT-4.1 series includes three API-only variants, GPT-4.1, Mini, and Nano, that share a context window of 1 million tokens but differ in terms of cost and performance. Let's check below:

6. GPT-4.1 vs. Claude 3.7 Sonnet vs Gemini 2.5

Now we compare GPT-4.1 with Claude 3.7 Sonnet and Gemini 2.5 Pro because they are the premier LLMs from OpenAI, Anthropic, and Google, respectively. Each of these LLMs shows industry-leading strengths in multimodal capabilities, context capacity, and reasoning. The following side-by-side view guides developers in evaluating the trade-offs between performance, cost, and feature to determine the optimal model for their particular use case.

7. How to Access GPT‑4.1?

Signing up for an OpenAI API key and mentioning the relevant model string in their API calls will let developers access GPT‑4.1.

Additionally, testing all three GPT-4.1 variants in the OpenAI Playground provides a hands-on interface for adjusting prompts and instantly viewing responses.

8. How can FutureAGI help?

Future AGI is a leading platform that lets you experiment with various models and set up evaluations. It’s a great tool for prototyping and testing new models, especially GPT4.1.

Some key features:

• Automated Quality Assessment: Use Future AGI's evaluation suite to execute queries that monitor over 50 metrics across modalities.

• Real-Time Observability: Get live dashboards that display the token quantities, latency, and error rates of your GPT-4.1 calls.

• Multimodal Evaluation: Develop a unified toolchain to assess the text and image outputs of GPT‑4.1's API.

• Prompt bench: Helps in rapid prototyping and testing your prompts with different models

9. Conclusion

GPT‑4.1 runs 40% faster at 26% lower per‑token API costs, perfect 100% long‑context retrieval, and leaps ahead of GPT‑4o with a 21% jump in coding benchmarks. Eliminating the need for manual chunking, it manages whole codebases and long documents in one call with support for a 1-million-token window. Three variations—Full, Mini, and Nano—from the API‑only release lets you choose the appropriate balance of accuracy, throughput, or low latency without juggling several services. By upgrading to GPT‑4.1, developers get a model that is ready for production and strikes a good mix between cost, speed, and power for next-generation AI applications.