GPT-5 nano vs Qwen3 VL 8B Thinking
GPT-5 nano leads the LLM Stats Score 19.5 to 16.3. GPT-5 nano is 4.8x cheaper per token.
OpenAI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
GPT-5 nano leads the overall LLM Stats Score 19.5 to 16.3, ranking #203 overall.
In the 2 individual benchmarks reported for both models, GPT-5 nano wins 2; this is a narrower head-to-head signal than the composite indexes.
On price, GPT-5 nano is roughly 4.8x 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.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose GPT-5 nano
- overall performance matters — it scores 19.5 and ranks #203 on LLM Stats
- you value its reported benchmark strengths — it wins 2 of 2 exact shared results
- cost matters — it's about 4.8x cheaper per token
- you process long inputs — it offers a 400,000 token context window
Choose Qwen3 VL 8B Thinking
- you want the most recent training data — it shipped Sep 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
5 reported for GPT-5 nano · 50 for Qwen3 VL 8B Thinking
GPT-5 nano outperforms in 2 benchmarks (AIME 2025, GPQA), while Qwen3 VL 8B Thinking is better at 0 benchmarks.
GPT-5 nano 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-5 nano ($0.05/1M tokens) is 3.6x cheaper than Qwen3 VL 8B Thinking ($0.18/1M tokens).
For output processing, GPT-5 nano ($0.40/1M tokens) is 5.2x cheaper than Qwen3 VL 8B Thinking ($2.09/1M tokens).
In conclusion, Qwen3 VL 8B 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 8B Thinking's 262,144 tokens. Qwen3 VL 8B Thinking can generate longer responses up to 262,144 tokens, while GPT-5 nano is limited to 128,000 tokens.
Input capabilities
Documented input modalities across available providers
Both GPT-5 nano and Qwen3 VL 8B Thinking support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GPT-5 nano
Qwen3 VL 8B Thinking
License
Usage and distribution terms
GPT-5 nano is licensed under a proprietary license, while Qwen3 VL 8B 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 8B Thinking was released on 2025-09-22.
Qwen3 VL 8B Thinking is 2 months newer than GPT-5 nano.
Aug 7, 2025
1.1 years ago
Sep 22, 2025
11 months ago
1mo newerKnowledge Cutoff
When training data ends
GPT-5 nano has a documented knowledge cutoff of 2024-05-30, while Qwen3 VL 8B 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 8B Thinking's cutoff date.
May 2024
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Provider Availability
GPT-5 nano is available from OpenAI. Qwen3 VL 8B Thinking is available from DeepInfra.
GPT-5 nano
Qwen3 VL 8B Thinking
Outputs Comparison
Judge for yourself.
Run your own prompts against GPT-5 nano and Qwen3 VL 8B Thinking side-by-side, then vote on the output you prefer.
FAQ
Common questions about GPT-5 nano vs Qwen3 VL 8B Thinking.