Kimi K2-Thinking-0905 vs Mistral Large 4
Mistral Large 4 leads the LLM Stats Score 46.2 to 35.7. Kimi K2-Thinking-0905 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 35.7, ranking #34 overall.
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, Kimi K2-Thinking-0905 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-Thinking-0905
- cost matters — it's about 1.2x cheaper per token
- 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 1 of 1 exact shared results
- 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.
Individual benchmarks
21 reported for Kimi K2-Thinking-0905 · 18 for Mistral Large 4
Kimi K2-Thinking-0905 outperforms in 0 benchmarks, while Mistral Large 4 is better at 1 benchmark (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-Thinking-0905 ($0.47/1M tokens) is 1.4x cheaper than Mistral Large 4 ($0.68/1M tokens).
For output processing, Kimi K2-Thinking-0905 ($2.00/1M tokens) is 1.0x cheaper than Mistral Large 4 ($2.09/1M tokens).
In conclusion, Mistral Large 4 is more expensive than Kimi K2-Thinking-0905.*
* 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-Thinking-0905, 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-Thinking-0905's 262,144 tokens. Only Kimi K2-Thinking-0905 specifies output context (262,144 tokens).
Input capabilities
Documented input modalities across available providers
Mistral Large 4 supports multimodal inputs, whereas Kimi K2-Thinking-0905 does not.
Mistral Large 4 can handle both text and other forms of data like images, making it suitable for multimodal applications.
Kimi K2-Thinking-0905
Mistral Large 4
License
Usage and distribution terms
Kimi K2-Thinking-0905 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-Thinking-0905 was released on 2025-09-05, while Mistral Large 4 was released on 2026-10-06.
Mistral Large 4 is 13 months newer than Kimi K2-Thinking-0905.
Sep 5, 2025
1.1 years ago
Oct 6, 2026
2 days ago
1.1yr 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-Thinking-0905 is available from DeepInfra, Novita, Fireworks. Mistral Large 4 is available from Mistral AI.
Kimi K2-Thinking-0905
Mistral Large 4
Outputs Comparison
Judge for yourself.
Run your own prompts against Kimi K2-Thinking-0905 and Mistral Large 4 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Kimi K2-Thinking-0905 vs Mistral Large 4.