Model Comparison
o1-mini vs Qwen3 VL 4B InstructWhich is better in 2026?
o1-mini significantly outperforms across most benchmarks. Qwen3 VL 4B Instruct is 23.3x cheaper per token.
Verdict: o1-mini vs Qwen3 VL 4B Instruct — which is better?
o1-mini (by OpenAI) and Qwen3 VL 4B Instruct (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.
o1-mini outperforms in 1 benchmarks (MMLU), while Qwen3 VL 4B Instruct is better at 0 benchmarks. o1-mini significantly outperforms across most benchmarks.
On price, Qwen3 VL 4B Instruct is roughly 23.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3 VL 4B Instruct also accepts a larger context window (262,144 input tokens), making it the stronger choice for long documents and large codebases.
Choose o1-mini if…
- you want the strongest raw capability — it leads on 1 of 1 shared benchmarks
Choose Qwen3 VL 4B Instruct if…
- cost matters — it's about 23.3x 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
o1-mini outperforms in 1 benchmarks (MMLU), while Qwen3 VL 4B Instruct is better at 0 benchmarks.
o1-mini significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, o1-mini ($3.00/1M tokens) is 30.0x more expensive than Qwen3 VL 4B Instruct ($0.10/1M tokens).
For output processing, o1-mini ($12.00/1M tokens) is 20.0x more expensive than Qwen3 VL 4B Instruct ($0.60/1M tokens).
In conclusion, o1-mini is more expensive than Qwen3 VL 4B Instruct.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Qwen3 VL 4B Instruct accepts 262,144 input tokens compared to o1-mini's 128,000 tokens. Qwen3 VL 4B Instruct can generate longer responses up to 262,144 tokens, while o1-mini is limited to 65,536 tokens.
Input Capabilities
Supported data types and modalities
Qwen3 VL 4B Instruct supports multimodal inputs, whereas o1-mini does not.
Qwen3 VL 4B Instruct can handle both text and other forms of data like images, making it suitable for multimodal applications.
o1-mini
Qwen3 VL 4B Instruct
License
Usage and distribution terms
o1-mini is licensed under a proprietary license, while Qwen3 VL 4B Instruct 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
o1-mini was released on 2024-09-12, while Qwen3 VL 4B Instruct was released on 2025-09-22.
Qwen3 VL 4B Instruct is 13 months newer than o1-mini.
Sep 12, 2024
1.7 years ago
Sep 22, 2025
8 months ago
1.0yr newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
o1-mini is available from OpenAI, Azure. Qwen3 VL 4B Instruct is available from DeepInfra.
o1-mini
Qwen3 VL 4B Instruct
Outputs Comparison
Key Takeaways
o1-mini
View detailsOpenAI
Qwen3 VL 4B Instruct
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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FAQ
Common questions about o1-mini vs Qwen3 VL 4B Instruct.