OpenAI has introduced a new generation of AI models under the GPT-4.1 family, designed specifically to excel at coding and instruction-based tasks. This includes three variants: GPT-4.1, GPT-4.1 mini, and GPT-4.1 nano — all available via API but not integrated into ChatGPT.
Advanced Coding Capabilities with GPT-4.1
The new models come with a 1-million-token context window, enabling them to process roughly 750,000 words — more than the entire length of War and Peace. According to OpenAI, this massive capacity supports long-form reasoning and complex software development workflows.
These releases come amid heightened competition from AI leaders like Google and Anthropic. Google’s Gemini 2.5 Pro and Anthropic’s Claude 3.7 Sonnet have both posted strong performances on coding benchmarks such as SWE-bench Verified. OpenAI now joins the fray with its own benchmark-driven progress.
A Step Toward Autonomous Software Engineers
OpenAI has openly stated its goal of building an “agentic software engineer” — AI that can manage full application development, including bug fixing, documentation, QA, and deployment. The launch of GPT-4.1 marks a step toward that future.
“We’ve optimized GPT-4.1 for real-world use based on developer feedback,” said an OpenAI spokesperson. “It’s better at frontend development, formatting, consistent tool usage, and reducing unnecessary edits.”
Performance and Pricing Details
GPT-4.1 is engineered for real-world software engineering and outperforms previous OpenAI models like GPT-4o and GPT-4o mini on tasks like SWE-bench. Performance tiers vary:
- GPT-4.1: $2/million input tokens, $8/million output tokens
- GPT-4.1 Mini: $0.40/million input tokens, $1.60/million output tokens
- GPT-4.1 Nano: $0.10/million input tokens, $0.40/million output tokens
The nano variant is OpenAI’s most affordable and fastest model to date, sacrificing some accuracy for speed.
Benchmark Results and Limitations
In internal tests, GPT-4.1 scored 52% to 54.6% on SWE-bench Verified, slightly behind Google’s Gemini 2.5 Pro (63.8%) and Anthropic’s Claude 3.7 Sonnet (62.3%). The variance in scores is due to infrastructure constraints, according to OpenAI.
In multimedia benchmarks, GPT-4.1 scored 72% accuracy on the ‘long, no subtitles’ category of Video-MME, highlighting its enhanced ability to interpret video content.
Despite these advances, OpenAI cautions that GPT-4.1 still has limitations. It may become less accurate with longer input sequences. In one test, accuracy dropped from 84% at 8,000 tokens to 50% at 1 million tokens. Additionally, GPT-4.1 tends to be more “literal,” requiring users to craft more specific and structured prompts.
