o4-mini vs Qwen3 VL 8B Thinking
o4-mini leads the LLM Stats Score 27.7 to 16.3. Qwen3 VL 8B Thinking is 2.9x cheaper per token.
OpenAI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
o4-mini leads the overall LLM Stats Score 27.7 to 16.3, ranking #132 overall.
In the 3 individual benchmarks reported for both models, o4-mini wins 3; this is a narrower head-to-head signal than the composite indexes.
On price, Qwen3 VL 8B Thinking is roughly 2.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3 VL 8B Thinking also accepts a larger context window (262,144 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 o4-mini
- overall performance matters — it scores 27.7 and ranks #132 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 3 of 3 exact shared results
Choose Qwen3 VL 8B Thinking
- cost matters — it's about 2.9x 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
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
14 reported for o4-mini · 50 for Qwen3 VL 8B Thinking
o4-mini outperforms in 3 benchmarks (AIME 2025, CharXiv-R, GPQA), while Qwen3 VL 8B Thinking is better at 0 benchmarks.
o4-mini 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, o4-mini ($1.10/1M tokens) is 6.1x more expensive than Qwen3 VL 8B Thinking ($0.18/1M tokens).
For output processing, o4-mini ($4.40/1M tokens) is 2.1x more expensive than Qwen3 VL 8B Thinking ($2.09/1M tokens).
In conclusion, o4-mini is more expensive than Qwen3 VL 8B Thinking.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Qwen3 VL 8B Thinking accepts 262,144 input tokens compared to o4-mini's 200,000 tokens. Qwen3 VL 8B Thinking can generate longer responses up to 262,144 tokens, while o4-mini is limited to 100,000 tokens.
Input capabilities
Documented input modalities across available providers
Both o4-mini and Qwen3 VL 8B Thinking support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
o4-mini
Qwen3 VL 8B Thinking
License
Usage and distribution terms
o4-mini 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
o4-mini was released on 2025-04-16, while Qwen3 VL 8B Thinking was released on 2025-09-22.
Qwen3 VL 8B Thinking is 5 months newer than o4-mini.
Apr 16, 2025
1.4 years ago
Sep 22, 2025
11 months ago
5mo newerKnowledge Cutoff
When training data ends
o4-mini has a documented knowledge cutoff of 2024-05-31, while Qwen3 VL 8B Thinking's cutoff date is not specified.
We can confirm o4-mini's training data extends to 2024-05-31, but cannot make a direct comparison without Qwen3 VL 8B Thinking's cutoff date.
May 2024
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Provider Availability
o4-mini is available from OpenAI. Qwen3 VL 8B Thinking is available from DeepInfra.
o4-mini
Qwen3 VL 8B Thinking
Outputs Comparison
Judge for yourself.
Run your own prompts against o4-mini and Qwen3 VL 8B Thinking side-by-side, then vote on the output you prefer.
FAQ
Common questions about o4-mini vs Qwen3 VL 8B Thinking.