Coding improvements in new OpenAI GPT models

OpenAI recently launched three new models in the API: GPT-4.1, GPT-4.1 mini, and GPT-4.1 nano. These models outperform GPT-4o and GPT-4o mini across the board, with major gains in coding and instruction following. They also have larger context windows—supporting up to 1 million tokens of context, and are able to better use that context with improved long-context comprehension. They feature a refreshed knowledge cutoff of June 2024.

Introducing GPT-4.1 in the API: Major coding improvements in new OpenAI GPT models

GPT-4.1 performance highlights:

Coding:

• GPT-4.1 scores 54.6% on SWE-bench Verified, improving by 21.4%abs over GPT-4o and 26.6%abs over GPT-4.5—making it a leading model for coding.
   

Instruction following:

• On Scale’s MultiChallenge benchmark, GPT-4.1 scores 38.3%, a 10.5%abs increase over GPT-4o.
   

Long context:

• On Video-MME, a benchmark for multimodal long context understanding, GPT-4.1 sets a new state-of-the-art result—scoring 72.0% on the long, no subtitles category, a 6.7%abs improvement over GPT-4o.
   

While benchmarks provide valuable insights, we trained these models with a focus on real-world utility. Close collaboration with the developer community enabled us to optimize these models for the tasks that matter most.

GPT 4.1 Family Intelligence by Latency

Cost and latency improvements:

The GPT-4.1 model family offers exceptional performance at a lower cost, pushing forward at every point on the latency curve.

GPT-4.1 mini:

• A significant leap in small model performance, surpassing GPT-4o in many benchmarks. It reduces latency by nearly half and cost by 83%.
   

GPT-4.1 nano:

• The fastest and cheapest model available. Ideal for low-latency tasks like classification or autocompletion, with a 1 million token context window.
   

Scores:

• MMLU: 80.1%
• GPQA: 50.3%
• Aider polyglot coding: 9.8%
   

Real-world applications and developer feedback:

The GPT-4.1 models improve reliability and long context comprehension, making them ideal for powering agents that perform tasks independently on behalf of users.
Early testers noted that GPT-4.1 can be more literal, so explicit and specific prompts are recommended.

Deprecation notice:

GPT-4.5 Preview will be deprecated on July 14, 2025, as GPT-4.1 offers improved performance at lower cost and latency. We will maintain the creativity, writing quality, humor, and nuance appreciated in GPT-4.5 in future models.

Benchmark performance and real-world usage:

GPT-4.1 demonstrates significant improvements across coding, instruction following, and long context handling. It excels in:

• Coding tasks: Agentically solving coding tasks, reliable code diffs, and frontend coding.
• Instruction following: Improved format compliance, multi-turn instructions, and reduced overconfidence.
• Long context processing: Efficient retrieval from up to 1 million tokens of input.
   

Real-world examples include improvements in coding benchmarks with Windsurf, accurate legal data extraction with Thomson Reuters, and fast, reliable code generation with Qodo.

Vision and multimodal capabilities:

The GPT-4.1 family excels at image understanding and processing long videos without subtitles, making it suitable for multimodal applications. GPT-4.1 series models are available now to all developers, with lower prices through efficiency improvements:

GPT-4.1 pricing:

• Input: $2.00
• Cached Input: $0.50
• Output: $8.00
• Blended Pricing: $1.84
   

GPT-4.1 mini pricing:

• Input: $0.40
• Cached Input: $0.10
• Output: $1.60
• Blended Pricing: $0.42
   

GPT-4.1 nano pricing:

• Input: $0.10
• Cached Input: $0.025
• Output: $0.40
• Blended Pricing: $0.12
   

GPT-4.1 represents a major leap in practical AI application, addressing real-world developer needs from coding to long context comprehension. We look forward to seeing the innovations that the developer community builds using these models.