Model Comparison
o3 vs Qwen3 VL 4B ThinkingWhich is better in 2026?
o3 significantly outperforms across most benchmarks. Qwen3 VL 4B Thinking is 10.8x cheaper per token.
Verdict: o3 vs Qwen3 VL 4B Thinking — which is better?
o3 (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.
o3 outperforms in 6 benchmarks (AIME 2025, CharXiv-R, ERQA, GPQA, MMMU-Pro, VideoMMMU), while Qwen3 VL 4B Thinking is better at 0 benchmarks. o3 significantly outperforms across most benchmarks.
On price, Qwen3 VL 4B Thinking is roughly 10.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3 VL 4B Thinking also accepts a larger context window (262,144 input tokens), making it the stronger choice for long documents and large codebases.
Choose o3 if…
- you want the strongest raw capability — it leads on 6 of 6 shared benchmarks
Choose Qwen3 VL 4B Thinking if…
- cost matters — it's about 10.8x cheaper per token
- you process long inputs — it offers a 262,144 token context window
- 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
o3 outperforms in 6 benchmarks (AIME 2025, CharXiv-R, ERQA, GPQA, MMMU-Pro, VideoMMMU), while Qwen3 VL 4B Thinking is better at 0 benchmarks.
o3 significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, o3 ($2.00/1M tokens) is 20.0x more expensive than Qwen3 VL 4B Thinking ($0.10/1M tokens).
For output processing, o3 ($8.00/1M tokens) is 8.0x more expensive than Qwen3 VL 4B Thinking ($1.00/1M tokens).
In conclusion, o3 is more expensive than Qwen3 VL 4B Thinking.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Qwen3 VL 4B Thinking accepts 262,144 input tokens compared to o3's 200,000 tokens. Qwen3 VL 4B Thinking can generate longer responses up to 262,144 tokens, while o3 is limited to 100,000 tokens.
Input Capabilities
Supported data types and modalities
Both o3 and Qwen3 VL 4B Thinking support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
o3
Qwen3 VL 4B Thinking
License
Usage and distribution terms
o3 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
o3 was released on 2025-04-16, while Qwen3 VL 4B Thinking was released on 2025-09-22.
Qwen3 VL 4B Thinking is 5 months newer than o3.
Apr 16, 2025
1.1 years ago
Sep 22, 2025
8 months ago
5mo newerKnowledge Cutoff
When training data ends
o3 has a documented knowledge cutoff of 2024-05-31, while Qwen3 VL 4B Thinking's cutoff date is not specified.
We can confirm o3's training data extends to 2024-05-31, but cannot make a direct comparison without Qwen3 VL 4B Thinking's cutoff date.
May 2024
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Provider Availability
o3 is available from OpenAI. Qwen3 VL 4B Thinking is available from DeepInfra.
o3
Qwen3 VL 4B Thinking
Outputs Comparison
Key Takeaways
o3
View detailsOpenAI
Qwen3 VL 4B Thinking
View detailsAlibaba Cloud / Qwen Team
o3
Qwen3 VL 4B ThinkingDetailed Comparison
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FAQ
Common questions about o3 vs Qwen3 VL 4B Thinking.