Model Comparison
Mistral Large 2 vs Qwen3 VL 4B InstructWhich is better in 2026?
Mistral Large 2 significantly outperforms across most benchmarks. Qwen3 VL 4B Instruct is 13.3x cheaper per token.
Verdict: Mistral Large 2 vs Qwen3 VL 4B Instruct — which is better?
Mistral Large 2 (by Mistral AI) 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.
Mistral Large 2 outperforms in 1 benchmarks (MMLU), while Qwen3 VL 4B Instruct is better at 0 benchmarks. Mistral Large 2 significantly outperforms across most benchmarks.
On price, Qwen3 VL 4B Instruct is roughly 13.3x 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 Mistral Large 2 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 13.3x 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
Performance Benchmarks
Comparative analysis across standard metrics
Mistral Large 2 outperforms in 1 benchmarks (MMLU), while Qwen3 VL 4B Instruct is better at 0 benchmarks.
Mistral Large 2 significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, Mistral Large 2 ($2.00/1M tokens) is 20.0x more expensive than Qwen3 VL 4B Instruct ($0.10/1M tokens).
For output processing, Mistral Large 2 ($6.00/1M tokens) is 10.0x more expensive than Qwen3 VL 4B Instruct ($0.60/1M tokens).
In conclusion, Mistral Large 2 is more expensive than Qwen3 VL 4B Instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Mistral Large 2 has 119.0B more parameters than Qwen3 VL 4B Instruct, making it 2975.0% larger.
Context Window
Maximum input and output token capacity
Qwen3 VL 4B Instruct accepts 262,144 input tokens compared to Mistral Large 2's 128,000 tokens. Qwen3 VL 4B Instruct can generate longer responses up to 262,144 tokens, while Mistral Large 2 is limited to 128,000 tokens.
Input Capabilities
Supported data types and modalities
Qwen3 VL 4B Instruct supports multimodal inputs, whereas Mistral Large 2 does not.
Qwen3 VL 4B Instruct can handle both text and other forms of data like images, making it suitable for multimodal applications.
Mistral Large 2
Qwen3 VL 4B Instruct
License
Usage and distribution terms
Mistral Large 2 is licensed under Mistral Research 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.
Mistral Research License
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
Mistral Large 2 was released on 2024-07-24, while Qwen3 VL 4B Instruct was released on 2025-09-22.
Qwen3 VL 4B Instruct is 14 months newer than Mistral Large 2.
Jul 24, 2024
2.0 years ago
Sep 22, 2025
10 months ago
1.2yr newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
Mistral Large 2 is available from Google, Mistral AI. Qwen3 VL 4B Instruct is available from DeepInfra.
Mistral Large 2
Qwen3 VL 4B Instruct
Outputs Comparison
Key Takeaways
Mistral Large 2
View detailsMistral AI
Qwen3 VL 4B Instruct
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against Mistral Large 2 and Qwen3 VL 4B Instruct side-by-side, then vote on the output you prefer.
| Feature | | |
|---|
FAQ
Common questions about Mistral Large 2 vs Qwen3 VL 4B Instruct.