Model Comparison
GPT-5 nano vs Qwen3 VL 4B ThinkingWhich is better in 2026?
GPT-5 nano significantly outperforms across most benchmarks. GPT-5 nano is 2.4x cheaper per token.
Verdict: GPT-5 nano vs Qwen3 VL 4B Thinking — which is better?
GPT-5 nano (by OpenAI) and Qwen3 VL 4B Thinking (by Alibaba Cloud / Qwen Team) 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-5 nano outperforms in 2 benchmarks (AIME 2025, GPQA), while Qwen3 VL 4B Thinking is better at 0 benchmarks. GPT-5 nano significantly outperforms across most benchmarks.
On price, GPT-5 nano is roughly 2.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GPT-5 nano also accepts a larger context window (400,000 input tokens), making it the stronger choice for long documents and large codebases.
Choose GPT-5 nano if…
- you want the strongest raw capability — it leads on 2 of 2 shared benchmarks
- cost matters — it's about 2.4x cheaper per token
- you process long inputs — it offers a 400,000 token context window
Choose Qwen3 VL 4B Thinking if…
- you want the most recent training data — it shipped Sep 2025
- you need open weights you can self-host or fine-tune
Performance Benchmarks
Comparative analysis across standard metrics
GPT-5 nano outperforms in 2 benchmarks (AIME 2025, GPQA), while Qwen3 VL 4B Thinking is better at 0 benchmarks.
GPT-5 nano significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, GPT-5 nano ($0.05/1M tokens) is 2.0x cheaper than Qwen3 VL 4B Thinking ($0.10/1M tokens).
For output processing, GPT-5 nano ($0.40/1M tokens) is 2.5x cheaper than Qwen3 VL 4B Thinking ($1.00/1M tokens).
In conclusion, Qwen3 VL 4B Thinking is more expensive than GPT-5 nano.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
GPT-5 nano accepts 400,000 input tokens compared to Qwen3 VL 4B Thinking's 262,144 tokens. Qwen3 VL 4B Thinking can generate longer responses up to 262,144 tokens, while GPT-5 nano is limited to 128,000 tokens.
Input Capabilities
Supported data types and modalities
Both GPT-5 nano and Qwen3 VL 4B Thinking support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GPT-5 nano
Qwen3 VL 4B Thinking
License
Usage and distribution terms
GPT-5 nano is licensed under a proprietary license, while Qwen3 VL 4B Thinking 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-5 nano was released on 2025-08-07, while Qwen3 VL 4B Thinking was released on 2025-09-22.
Qwen3 VL 4B Thinking is 2 months newer than GPT-5 nano.
Aug 7, 2025
10 months ago
Sep 22, 2025
8 months ago
1mo newerKnowledge Cutoff
When training data ends
GPT-5 nano has a documented knowledge cutoff of 2024-05-30, while Qwen3 VL 4B Thinking's cutoff date is not specified.
We can confirm GPT-5 nano's training data extends to 2024-05-30, but cannot make a direct comparison without Qwen3 VL 4B Thinking's cutoff date.
May 2024
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Provider Availability
GPT-5 nano is available from OpenAI. Qwen3 VL 4B Thinking is available from DeepInfra.
GPT-5 nano
Qwen3 VL 4B Thinking
Outputs Comparison
Key Takeaways
GPT-5 nano
View detailsOpenAI
Qwen3 VL 4B Thinking
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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FAQ
Common questions about GPT-5 nano vs Qwen3 VL 4B Thinking.