o1-mini vs Qwen3 VL 8B Thinking
o1-mini and Qwen3 VL 8B Thinking are closely matched at 10.0 and 16.3 on the LLM Stats Score. Qwen3 VL 8B Thinking is 8.0x cheaper per token.
OpenAI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
o1-mini and Qwen3 VL 8B Thinking are closely matched on the overall LLM Stats Score at 10.0 and 16.3.
In the 2 individual benchmarks reported for both models, Qwen3 VL 8B Thinking wins 2; this is a narrower head-to-head signal than the composite indexes.
On price, Qwen3 VL 8B Thinking is roughly 8.0x 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 o1-mini
- you want predictable pricing at $3.00/M input and $12.00/M output
Choose Qwen3 VL 8B Thinking
- you value its reported benchmark strengths — it wins 2 of 2 exact shared results
- cost matters — it's about 8.0x 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
6 reported for o1-mini · 50 for Qwen3 VL 8B Thinking
o1-mini outperforms in 0 benchmarks, while Qwen3 VL 8B Thinking is better at 1 benchmark (GPQA).
Qwen3 VL 8B Thinking has a slight edge in benchmark performance.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, o1-mini ($3.00/1M tokens) is 16.7x more expensive than Qwen3 VL 8B Thinking ($0.18/1M tokens).
For output processing, o1-mini ($12.00/1M tokens) is 5.7x more expensive than Qwen3 VL 8B Thinking ($2.09/1M tokens).
In conclusion, o1-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 o1-mini's 128,000 tokens. Qwen3 VL 8B Thinking can generate longer responses up to 262,144 tokens, while o1-mini is limited to 65,536 tokens.
Input capabilities
Documented input modalities across available providers
Qwen3 VL 8B Thinking supports multimodal inputs, whereas o1-mini does not.
Qwen3 VL 8B Thinking can handle both text and other forms of data like images, making it suitable for multimodal applications.
o1-mini
Qwen3 VL 8B Thinking
License
Usage and distribution terms
o1-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
o1-mini was released on 2024-09-12, while Qwen3 VL 8B Thinking was released on 2025-09-22.
Qwen3 VL 8B Thinking is 13 months newer than o1-mini.
Sep 12, 2024
2.0 years ago
Sep 22, 2025
11 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 8B Thinking is available from DeepInfra.
o1-mini
Qwen3 VL 8B Thinking
Outputs Comparison
Judge for yourself.
Run your own prompts against o1-mini and Qwen3 VL 8B Thinking side-by-side, then vote on the output you prefer.
FAQ
Common questions about o1-mini vs Qwen3 VL 8B Thinking.