Model Comparison
GPT-4.1 nano vs Pixtral LargeWhich is better in 2026?
Pixtral Large significantly outperforms across most benchmarks. GPT-4.1 nano is 17.1x cheaper per token.
Verdict: GPT-4.1 nano vs Pixtral Large — which is better?
GPT-4.1 nano (by OpenAI) and Pixtral Large (by Mistral AI) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
GPT-4.1 nano outperforms in 0 benchmarks, while Pixtral Large is better at 2 benchmarks (MathVista, MMMU). Pixtral Large significantly outperforms across most benchmarks.
On price, GPT-4.1 nano is roughly 17.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GPT-4.1 nano also accepts a larger context window (1,047,576 input tokens), making it the stronger choice for long documents and large codebases.
Choose GPT-4.1 nano if…
- cost matters — it's about 17.1x cheaper per token
- you process long inputs — it offers a 1,047,576 token context window
- you want the most recent training data — it shipped Apr 2025
Choose Pixtral Large if…
- you want the strongest raw capability — it leads on 2 of 2 shared benchmarks
- you need open weights you can self-host or fine-tune
Performance Benchmarks
Comparative analysis across standard metrics
GPT-4.1 nano outperforms in 0 benchmarks, while Pixtral Large is better at 2 benchmarks (MathVista, MMMU).
Pixtral Large significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, GPT-4.1 nano ($0.10/1M tokens) is 20.0x cheaper than Pixtral Large ($2.00/1M tokens).
For output processing, GPT-4.1 nano ($0.40/1M tokens) is 15.0x cheaper than Pixtral Large ($6.00/1M tokens).
In conclusion, Pixtral Large is more expensive than GPT-4.1 nano.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
GPT-4.1 nano accepts 1,047,576 input tokens compared to Pixtral Large's 128,000 tokens. Pixtral Large can generate longer responses up to 128,000 tokens, while GPT-4.1 nano is limited to 32,768 tokens.
Input Capabilities
Supported data types and modalities
Both GPT-4.1 nano and Pixtral Large support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GPT-4.1 nano
Pixtral Large
License
Usage and distribution terms
GPT-4.1 nano is licensed under a proprietary license, while Pixtral Large uses Mistral Research License (MRL) for research; Mistral Commercial License for commercial use.
License differences may affect how you can use these models in commercial or open-source projects.
Proprietary
Closed source
Mistral Research License (MRL) for research; Mistral Commercial License for commercial use
Open weights
Release Timeline
When each model was launched
GPT-4.1 nano was released on 2025-04-14, while Pixtral Large was released on 2024-11-18.
GPT-4.1 nano is 5 months newer than Pixtral Large.
Apr 14, 2025
1.3 years ago
4mo newerNov 18, 2024
1.7 years ago
Knowledge Cutoff
When training data ends
GPT-4.1 nano has a documented knowledge cutoff of 2024-05-31, while Pixtral Large's cutoff date is not specified.
We can confirm GPT-4.1 nano's training data extends to 2024-05-31, but cannot make a direct comparison without Pixtral Large's cutoff date.
May 2024
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Provider Availability
GPT-4.1 nano is available from OpenAI. Pixtral Large is available from Mistral AI.
GPT-4.1 nano
Pixtral Large
Outputs Comparison
Key Takeaways
GPT-4.1 nano
View detailsOpenAI
Pixtral Large
View detailsMistral AI
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against GPT-4.1 nano and Pixtral Large side-by-side, then vote on the output you prefer.
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FAQ
Common questions about GPT-4.1 nano vs Pixtral Large.