GPT-4.1: Everything You Need to Know (+ What OpenAI Didn’t Say)

GPT-4.1 API Release: A Deep Dive

Key Concepts:

• GPT-4.1, GPT-4.1 Mini, GPT-4.1 Nano: New models from OpenAI, improvements over GPT-4.0.
• 1 Million Token Context Window: Significantly expanded context length.
• Instruction Following: Improved ability to adhere to user instructions.
• Coding Performance: Enhanced capabilities in coding tasks and front-end development.
• Long Context Retrieval: Ability to retrieve information from large contexts.
• Multi-Turn Co-reference: New benchmark for evaluating long context retrieval.
• Pricing: Cost-effectiveness compared to previous models.
• Benchmarks: Internal and external evaluations of model performance.

Model Overview and Naming

OpenAI has released GPT-4.1 in the API, featuring three models: GPT-4.1, GPT-4.1 Mini, and GPT-4.1 Nano. These models are designed to improve upon GPT-4.0, GPT-4.0 Mini, and GPT-4.0 respectively. A key feature is the expanded context window of 1 million tokens. However, the knowledge cutoff date is June 2024.

Benchmarks and Performance

OpenAI is releasing some of their internal benchmarks. The release highlights intelligence versus latency, with the GPT-4.1 family aiming for better intelligence at lower latency. The blog post claims that GPT-4.1 matches or exceeds GPT-4.0's intelligence while reducing latency by nearly half and cost by 83%. GPT-4.1 Nano is recommended as a fast and cheap workhorse model with a 1 million token context window and similar performance on needle-in-the-haystack tests.

Coding Capabilities

GPT-4.1 is significantly better than GPT-4.0 at coding tasks, including agentically solving coding tasks, front-end development, making fewer extraneous edits, following diff formats reliably, and ensuring consistent tool usage.

• SWE-bench Verified Benchmark: GPT-4.1 achieves 55% on this benchmark, which focuses on Python. After accounting for problems that couldn't run on their infrastructure, the performance becomes 52%.
• ADAS Polyglot Benchmark: GPT-4.1 performs well compared to GPT-4.0 but lags behind OpenAI's reasoning models. This benchmark involves multiple languages.
• Front-End Development: GPT-4.1 produces aesthetically better front-ends compared to GPT-4.0.

Instruction Following

GPT-4.1 follows instructions more reliably, with improvements in format following, negative instructions, ordered instructions, content requirements, and ranking overconfidence. OpenAI created an internal instruction following eval data set where GPT-4.1 performs better than GPT-4.0, even in the Nano and Mini versions. Multi-turn instruction following is also improved, maintaining coherence deep into conversations.

Long Context Retrieval

GPT-4.1 has a 1 million token context window. OpenAI acknowledges that needle-in-the-haystack tests are not representative of real-world applications. They introduced a new benchmark called multi-round co-reference, which tests the model's ability to find and disambiguate between multiple needles hidden in the text.

• Multi-Round Co-reference Benchmark: With two needles, GPT-4.1 performs better than existing models. However, with four or eight needles, reasoning models perform better, especially with shorter contexts.
• Graph Benchmark: GPT-4.1 performs better than GPT-4.0 but lags behind GPT-4.5.

Multimodal Reasoning

GPT-4.1 shows improvements in multimodal reasoning.

• MMMU Benchmark: GPT-4.1 achieves a 75% score, comparable to Llama 4 behemoth.
• Video MME Benchmark: GPT-4.1 achieves 72% on reasoning over long video context, similar to intern vision language model 2.5 from Shangghai lab.

Pricing

GPT-4.1 is less expensive than GPT-4.0, with a price difference of about 26% or 25% lower. However, it is still more expensive than DeepSeek R1. Gemini 2.5 Pro might be a better option for less than 200,000 input tokens, but GPT-4.1 is more cost-effective for larger token counts.

Academic Datasets

OpenAI provides benchmarks on academic datasets, focusing on OpenAI models without comparisons to other providers.

Conclusion

GPT-4.1 is an impressive model with improvements in coding, instruction following, and long context retrieval. It is a viable replacement for GPT-4.0, especially for developers. However, its performance compared to other models like Gemini and DeepSeek needs further evaluation in real-world applications. The 1 million token context window is a significant advantage, but its effectiveness depends on the specific retrieval tasks.