Mistral Large 4 vs Qwen3.8 Max
Mistral Large 4 and Qwen3.8 Max are closely matched at 46.2 and 51.1 on the LLM Stats Score. Mistral Large 4 is 2.4x cheaper per token.
Mistral AI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Mistral Large 4 and Qwen3.8 Max are closely matched on the overall LLM Stats Score at 46.2 and 51.1.
In the 2 individual benchmarks reported for both models, Mistral Large 4 wins 2; this is a narrower head-to-head signal than the composite indexes.
On price, Mistral Large 4 is roughly 2.4x 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
- you value its reported benchmark strengths — it wins 2 of 2 exact shared results
- cost matters — it's about 2.4x cheaper per token
- 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 Qwen3.8 Max
- 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 · 42 for Qwen3.8 Max
Mistral Large 4 outperforms in 2 benchmarks (AutomationBench, DeepSWE 1.1), while Qwen3.8 Max is better at 0 benchmarks.
Mistral Large 4 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, Mistral Large 4 ($0.68/1M tokens) is 2.4x cheaper than Qwen3.8 Max ($1.65/1M tokens).
For output processing, Mistral Large 4 ($2.09/1M tokens) is 2.4x cheaper than Qwen3.8 Max ($4.95/1M tokens).
In conclusion, Qwen3.8 Max is more expensive than Mistral Large 4.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Qwen3.8 Max has 1350.0B more parameters than Mistral Large 4, making it 128.6% larger.
Context Window
Maximum input and output token capacity
Mistral Large 4 accepts 1,000,000 input tokens compared to Qwen3.8 Max's 256,000 tokens. Only Qwen3.8 Max specifies output context (256,000 tokens).
Input capabilities
Documented input modalities across available providers
Both Mistral Large 4 and Qwen3.8 Max support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Mistral Large 4
Qwen3.8 Max
License
Usage and distribution terms
Mistral Large 4 is licensed under a proprietary license, while Qwen3.8 Max uses Qwen3.8-Max License.
License differences may affect how you can use these models in commercial or open-source projects.
Proprietary
Closed source
Qwen3.8-Max License
Open weights
Release Timeline
When each model was launched
Mistral Large 4 was released on 2026-10-06, while Qwen3.8 Max was released on 2026-08-02.
Mistral Large 4 is 2 months newer than Qwen3.8 Max.
Oct 6, 2026
1 days ago
2mo newerAug 2, 2026
2 months 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. Qwen3.8 Max is available from DeepInfra, Fireworks, Novita, Together.
Mistral Large 4
Qwen3.8 Max
Outputs Comparison
Judge for yourself.
Run your own prompts against Mistral Large 4 and Qwen3.8 Max side-by-side, then vote on the output you prefer.
FAQ
Common questions about Mistral Large 4 vs Qwen3.8 Max.