Devstral Medium vs Qwen3 VL 8B Thinking
Devstral Medium and Qwen3 VL 8B Thinking are closely matched at 13.6 and 16.2 on the LLM Stats Score. Qwen3 VL 8B Thinking is 1.2x cheaper per token.
Mistral AI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Devstral Medium and Qwen3 VL 8B Thinking are closely matched on the overall LLM Stats Score at 13.6 and 16.2.
On price, Qwen3 VL 8B Thinking is roughly 1.2x 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 Devstral Medium
- you want predictable pricing at $0.40/M input and $2.00/M output
Choose Qwen3 VL 8B Thinking
- cost matters — it's about 1.2x 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.
Individual benchmarks
1 reported for Devstral Medium · 50 for Qwen3 VL 8B Thinking
Devstral Medium and Qwen3 VL 8B Thinkingdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, Devstral Medium ($0.40/1M tokens) is 2.2x more expensive than Qwen3 VL 8B Thinking ($0.18/1M tokens).
For output processing, Devstral Medium ($2.00/1M tokens) is 1.0x cheaper than Qwen3 VL 8B Thinking ($2.09/1M tokens).
In conclusion, Devstral Medium 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 Devstral Medium's 128,000 tokens. Qwen3 VL 8B Thinking can generate longer responses up to 262,144 tokens, while Devstral Medium is limited to 128,000 tokens.
Input capabilities
Documented input modalities across available providers
Qwen3 VL 8B Thinking supports multimodal inputs, whereas Devstral Medium does not.
Qwen3 VL 8B Thinking can handle both text and other forms of data like images, making it suitable for multimodal applications.
Devstral Medium
Qwen3 VL 8B Thinking
License
Usage and distribution terms
Devstral Medium 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
Devstral Medium was released on 2025-07-10, while Qwen3 VL 8B Thinking was released on 2025-09-22.
Qwen3 VL 8B Thinking is 2 months newer than Devstral Medium.
Jul 10, 2025
1.2 years ago
Sep 22, 2025
12 months ago
2mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
Devstral Medium is available from Mistral AI. Qwen3 VL 8B Thinking is available from DeepInfra.
Devstral Medium
Qwen3 VL 8B Thinking
Outputs Comparison
Judge for yourself.
Run your own prompts against Devstral Medium and Qwen3 VL 8B Thinking side-by-side, then vote on the output you prefer.
FAQ
Common questions about Devstral Medium vs Qwen3 VL 8B Thinking.