GLM-5.2 vs Mistral Large 4
GLM-5.2 and Mistral Large 4 are closely matched at 44.9 and 46.2 on the LLM Stats Score. Mistral Large 4 is 1.1x cheaper per token.
Zhipu AI · Mistral AI · Updated for 2026
Which is better?
GLM-5.2 and Mistral Large 4 are closely matched on the overall LLM Stats Score at 44.9 and 46.2.
In the 1 individual benchmarks reported for both models, Mistral Large 4 wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, Mistral Large 4 is roughly 1.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GLM-5.2 also accepts a larger context window (1,048,576 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 GLM-5.2
- you process long inputs — it offers a 1,048,576 token context window
- you need open weights you can self-host or fine-tune
Choose Mistral Large 4
- your work emphasizes agents — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- cost matters — it's about 1.1x cheaper per token
- you want the most recent training data — it shipped Oct 2026
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
19 reported for GLM-5.2 · 18 for Mistral Large 4
GLM-5.2 outperforms in 0 benchmarks, while Mistral Large 4 is better at 1 benchmark (DeepSWE 1.1).
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, GLM-5.2 ($0.75/1M tokens) is 1.1x more expensive than Mistral Large 4 ($0.68/1M tokens).
For output processing, GLM-5.2 ($2.40/1M tokens) is 1.1x more expensive than Mistral Large 4 ($2.09/1M tokens).
In conclusion, GLM-5.2 is more expensive than Mistral Large 4.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Mistral Large 4 has 297.0B more parameters than GLM-5.2, making it 39.4% larger.
Context Window
Maximum input and output token capacity
GLM-5.2 accepts 1,048,576 input tokens compared to Mistral Large 4's 1,000,000 tokens. Only GLM-5.2 specifies output context (1,048,576 tokens).
Input capabilities
Documented input modalities across available providers
Mistral Large 4 supports multimodal inputs, whereas GLM-5.2 does not.
Mistral Large 4 can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-5.2
Mistral Large 4
License
Usage and distribution terms
GLM-5.2 is licensed under MIT, while Mistral Large 4 uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
GLM-5.2 was released on 2026-06-16, while Mistral Large 4 was released on 2026-10-06.
Mistral Large 4 is 4 months newer than GLM-5.2.
Jun 16, 2026
3 months ago
Oct 6, 2026
1 weeks ago
3mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
GLM-5.2 is available from DeepInfra, Fireworks, FriendliAI, Novita, Together, ZAI. Mistral Large 4 is available from Mistral AI.
GLM-5.2
Mistral Large 4
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.2 and Mistral Large 4 side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.2 vs Mistral Large 4.