Mistral Large 4 vs Qwen2.5-Coder 32B Instruct
Mistral Large 4 leads the LLM Stats Score 46.2 to 2.0. Qwen2.5-Coder 32B Instruct is 11.5x 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 2.0, ranking #34 overall.
On price, Qwen2.5-Coder 32B Instruct is roughly 11.5x 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 Qwen2.5-Coder 32B Instruct
- cost matters — it's about 11.5x cheaper per token
- you need open weights you can self-host or fine-tune
At a glance
The differences that matter most.
Individual benchmarks
18 reported for Mistral Large 4 · 15 for Qwen2.5-Coder 32B Instruct
Mistral Large 4 and Qwen2.5-Coder 32B Instructdon'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 Qwen2.5-Coder 32B Instruct ($0.09/1M tokens).
For output processing, Mistral Large 4 ($2.09/1M tokens) is 23.2x more expensive than Qwen2.5-Coder 32B Instruct ($0.09/1M tokens).
In conclusion, Mistral Large 4 is more expensive than Qwen2.5-Coder 32B Instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Mistral Large 4 has 1018.0B more parameters than Qwen2.5-Coder 32B Instruct, making it 3181.3% larger.
Context Window
Maximum input and output token capacity
Mistral Large 4 accepts 1,000,000 input tokens compared to Qwen2.5-Coder 32B Instruct's 128,000 tokens. Only Qwen2.5-Coder 32B Instruct specifies output context (128,000 tokens).
Input capabilities
Documented input modalities across available providers
Mistral Large 4 supports multimodal inputs, whereas Qwen2.5-Coder 32B Instruct 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
Qwen2.5-Coder 32B Instruct
License
Usage and distribution terms
Mistral Large 4 is licensed under a proprietary license, while Qwen2.5-Coder 32B Instruct 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 Qwen2.5-Coder 32B Instruct was released on 2024-09-19.
Mistral Large 4 is 25 months newer than Qwen2.5-Coder 32B Instruct.
Oct 6, 2026
2 days ago
2.0yr newerSep 19, 2024
2.1 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. Qwen2.5-Coder 32B Instruct is available from Lambda, DeepInfra, Hyperbolic, Fireworks.
Mistral Large 4
Qwen2.5-Coder 32B Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against Mistral Large 4 and Qwen2.5-Coder 32B Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about Mistral Large 4 vs Qwen2.5-Coder 32B Instruct.