GPT-4.1 nano vs Qwen3-235B-A22B-Instruct-2507
Qwen3-235B-A22B-Instruct-2507 leads the LLM Stats Score 24.2 to 1.6. GPT-4.1 nano is 1.2x cheaper per token.
OpenAI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Qwen3-235B-A22B-Instruct-2507 leads the overall LLM Stats Score 24.2 to 1.6, ranking #166 overall.
In the 4 individual benchmarks reported for both models, Qwen3-235B-A22B-Instruct-2507 wins 4; this is a narrower head-to-head signal than the composite indexes.
On price, GPT-4.1 nano is roughly 1.2x 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.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose GPT-4.1 nano
- cost matters — it's about 1.2x cheaper per token
- you process long inputs — it offers a 1,047,576 token context window
Choose Qwen3-235B-A22B-Instruct-2507
- overall performance matters — it scores 24.2 and ranks #166 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 4 of 4 exact shared results
- you want the most recent training data — it shipped Jul 2025
- you need open weights you can self-host or fine-tune
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
24 reported for GPT-4.1 nano · 25 for Qwen3-235B-A22B-Instruct-2507
GPT-4.1 nano outperforms in 0 benchmarks, while Qwen3-235B-A22B-Instruct-2507 is better at 4 benchmarks (Aider-Polyglot, GPQA, IFEval, Multi-IF).
Qwen3-235B-A22B-Instruct-2507 significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, GPT-4.1 nano ($0.10/1M tokens) is 1.1x more expensive than Qwen3-235B-A22B-Instruct-2507 ($0.09/1M tokens).
For output processing, GPT-4.1 nano ($0.40/1M tokens) is 1.4x cheaper than Qwen3-235B-A22B-Instruct-2507 ($0.55/1M tokens).
In conclusion, Qwen3-235B-A22B-Instruct-2507 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 Qwen3-235B-A22B-Instruct-2507's 262,144 tokens. Qwen3-235B-A22B-Instruct-2507 can generate longer responses up to 262,144 tokens, while GPT-4.1 nano is limited to 32,768 tokens.
Input capabilities
Documented input modalities across available providers
GPT-4.1 nano supports multimodal inputs, whereas Qwen3-235B-A22B-Instruct-2507 does not.
GPT-4.1 nano can handle both text and other forms of data like images, making it suitable for multimodal applications.
GPT-4.1 nano
Qwen3-235B-A22B-Instruct-2507
License
Usage and distribution terms
GPT-4.1 nano is licensed under a proprietary license, while Qwen3-235B-A22B-Instruct-2507 uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
Proprietary
Closed source
Apache 2.0
Open weights
Release Timeline
When each model was launched
GPT-4.1 nano was released on 2025-04-14, while Qwen3-235B-A22B-Instruct-2507 was released on 2025-07-22.
Qwen3-235B-A22B-Instruct-2507 is 3 months newer than GPT-4.1 nano.
Apr 14, 2025
1.4 years ago
Jul 22, 2025
1.2 years ago
3mo newerKnowledge Cutoff
When training data ends
GPT-4.1 nano has a documented knowledge cutoff of 2024-05-31, while Qwen3-235B-A22B-Instruct-2507'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 Qwen3-235B-A22B-Instruct-2507's cutoff date.
May 2024
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Provider Availability
GPT-4.1 nano is available from OpenAI. Qwen3-235B-A22B-Instruct-2507 is available from DeepInfra, Fireworks, Novita.
GPT-4.1 nano
Qwen3-235B-A22B-Instruct-2507
Outputs Comparison
Judge for yourself.
Run your own prompts against GPT-4.1 nano and Qwen3-235B-A22B-Instruct-2507 side-by-side, then vote on the output you prefer.
FAQ
Common questions about GPT-4.1 nano vs Qwen3-235B-A22B-Instruct-2507.