GPT-4.1 nano vs Qwen3 32B
Qwen3 32B leads the LLM Stats Score 18.4 to 1.6. Qwen3 32B is 1.3x cheaper per token.
OpenAI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Qwen3 32B leads the overall LLM Stats Score 18.4 to 1.6, ranking #218 overall.
In the 1 individual benchmarks reported for both models, Qwen3 32B wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, Qwen3 32B is roughly 1.3x 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
- you process long inputs — it offers a 1,047,576 token context window
Choose Qwen3 32B
- overall performance matters — it scores 18.4 and ranks #218 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- cost matters — it's about 1.3x cheaper per token
- you want the most recent training data — it shipped Apr 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 · 9 for Qwen3 32B
GPT-4.1 nano outperforms in 0 benchmarks, while Qwen3 32B is better at 1 benchmark (AIME 2024).
Qwen3 32B 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 1.3x more expensive than Qwen3 32B ($0.08/1M tokens).
For output processing, GPT-4.1 nano ($0.40/1M tokens) is 1.4x more expensive than Qwen3 32B ($0.28/1M tokens).
In conclusion, GPT-4.1 nano is more expensive than Qwen3 32B.*
* 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 32B's 40,960 tokens. Qwen3 32B can generate longer responses up to 40,960 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 32B 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 32B
License
Usage and distribution terms
GPT-4.1 nano is licensed under a proprietary license, while Qwen3 32B 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 32B was released on 2025-04-29.
Qwen3 32B is 1 month newer than GPT-4.1 nano.
Apr 14, 2025
1.4 years ago
Apr 29, 2025
1.4 years ago
2w newerKnowledge Cutoff
When training data ends
GPT-4.1 nano has a documented knowledge cutoff of 2024-05-31, while Qwen3 32B'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 32B's cutoff date.
May 2024
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Provider Availability
GPT-4.1 nano is available from OpenAI. Qwen3 32B is available from DeepInfra, Novita, Sambanova.
GPT-4.1 nano
Qwen3 32B
Outputs Comparison
Judge for yourself.
Run your own prompts against GPT-4.1 nano and Qwen3 32B side-by-side, then vote on the output you prefer.
FAQ
Common questions about GPT-4.1 nano vs Qwen3 32B.