Mistral Large 4 vs Qwen3-235B-A22B-Instruct-2507
Mistral Large 4 leads the LLM Stats Score 46.2 to 24.1. Qwen3-235B-A22B-Instruct-2507 is 5.0x cheaper per token.
Mistral AI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Mistral Large 4 leads the overall LLM Stats Score 46.2 to 24.1, ranking #34 overall.
On price, Qwen3-235B-A22B-Instruct-2507 is roughly 5.0x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Mistral Large 4 also accepts a larger context window (1,000,000 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 Mistral Large 4
- overall performance matters — it scores 46.2 and ranks #34 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you process long inputs — it offers a 1,000,000 token context window
- you want the most recent training data — it shipped Oct 2026
Choose Qwen3-235B-A22B-Instruct-2507
- cost matters — it's about 5.0x cheaper per token
- 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
18 reported for Mistral Large 4 · 25 for Qwen3-235B-A22B-Instruct-2507
Mistral Large 4 and Qwen3-235B-A22B-Instruct-2507don'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, Mistral Large 4 ($0.68/1M tokens) is 7.6x more expensive than Qwen3-235B-A22B-Instruct-2507 ($0.09/1M tokens).
For output processing, Mistral Large 4 ($2.09/1M tokens) is 3.8x more expensive than Qwen3-235B-A22B-Instruct-2507 ($0.55/1M tokens).
In conclusion, Mistral Large 4 is more expensive than Qwen3-235B-A22B-Instruct-2507.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Mistral Large 4 has 815.0B more parameters than Qwen3-235B-A22B-Instruct-2507, making it 346.8% larger.
Context Window
Maximum input and output token capacity
Mistral Large 4 accepts 1,000,000 input tokens compared to Qwen3-235B-A22B-Instruct-2507's 262,144 tokens. Only Qwen3-235B-A22B-Instruct-2507 specifies output context (262,144 tokens).
Input capabilities
Documented input modalities across available providers
Mistral Large 4 supports multimodal inputs, whereas Qwen3-235B-A22B-Instruct-2507 does not.
Mistral Large 4 can handle both text and other forms of data like images, making it suitable for multimodal applications.
Mistral Large 4
Qwen3-235B-A22B-Instruct-2507
License
Usage and distribution terms
Mistral Large 4 is licensed under a proprietary license, while Qwen3-235B-A22B-Instruct-2507 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
Mistral Large 4 was released on 2026-10-06, while Qwen3-235B-A22B-Instruct-2507 was released on 2025-07-22.
Mistral Large 4 is 15 months newer than Qwen3-235B-A22B-Instruct-2507.
Oct 6, 2026
2 days ago
1.2yr newerJul 22, 2025
1.2 years ago
Knowledge 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 4 is available from Mistral AI. Qwen3-235B-A22B-Instruct-2507 is available from DeepInfra, Fireworks, Novita.
Mistral Large 4
Qwen3-235B-A22B-Instruct-2507
Outputs Comparison
Judge for yourself.
Run your own prompts against Mistral Large 4 and Qwen3-235B-A22B-Instruct-2507 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Mistral Large 4 vs Qwen3-235B-A22B-Instruct-2507.