Kimi K2.5 vs Mistral Large 4
Mistral Large 4 leads the LLM Stats Score 46.2 to 38.6. Mistral Large 4 is 1.2x cheaper per token.
Moonshot AI · Mistral AI · Updated for 2026
Which is better?
Mistral Large 4 leads the overall LLM Stats Score 46.2 to 38.6, ranking #34 overall.
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 1.2x 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 Kimi K2.5
- you need open weights you can self-host or fine-tune
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 value its reported benchmark strengths — it wins 2 of 2 exact shared results
- cost matters — it's about 1.2x 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
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
40 reported for Kimi K2.5 · 18 for Mistral Large 4
Kimi K2.5 outperforms in 0 benchmarks, while Mistral Large 4 is better at 2 benchmarks (CyberGym, SciCode).
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, Kimi K2.5 ($0.60/1M tokens) is 1.1x cheaper than Mistral Large 4 ($0.68/1M tokens).
For output processing, Kimi K2.5 ($3.00/1M tokens) is 1.4x more expensive than Mistral Large 4 ($2.09/1M tokens).
In conclusion, Kimi K2.5 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 50.0B more parameters than Kimi K2.5, making it 5.0% larger.
Context Window
Maximum input and output token capacity
Mistral Large 4 accepts 1,000,000 input tokens compared to Kimi K2.5's 262,100 tokens. Only Kimi K2.5 specifies output context (262,100 tokens).
Input capabilities
Documented input modalities across available providers
Both Kimi K2.5 and Mistral Large 4 support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Kimi K2.5
Mistral Large 4
License
Usage and distribution terms
Kimi K2.5 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
Kimi K2.5 was released on 2026-01-27, while Mistral Large 4 was released on 2026-10-06.
Mistral Large 4 is 8 months newer than Kimi K2.5.
Jan 27, 2026
8 months ago
Oct 6, 2026
2 days ago
8mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
Kimi K2.5 is available from Fireworks, Moonshot AI. Mistral Large 4 is available from Mistral AI.
Kimi K2.5
Mistral Large 4
Outputs Comparison
Judge for yourself.
Run your own prompts against Kimi K2.5 and Mistral Large 4 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Kimi K2.5 vs Mistral Large 4.