Kimi K3 vs Mistral Large 4
Kimi K3 and Mistral Large 4 are closely matched at 52.1 and 46.2 on the LLM Stats Score. Mistral Large 4 is 5.5x cheaper per token.
Moonshot AI · Mistral AI · Updated for 2026
Which is better?
Kimi K3 and Mistral Large 4 are closely matched on the overall LLM Stats Score at 52.1 and 46.2.
In the 3 individual benchmarks reported for both models, Kimi K3 wins 2; this is a narrower head-to-head signal than the composite indexes.
On price, Mistral Large 4 is roughly 5.5x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Kimi K3 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 Kimi K3
- you value its reported benchmark strengths — it wins 2 of 3 exact shared results
- 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
- cost matters — it's about 5.5x 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
31 reported for Kimi K3 · 18 for Mistral Large 4
Kimi K3 outperforms in 2 benchmarks (AA-Briefcase, DeepSWE 1.1), while Mistral Large 4 is better at 1 benchmark (AutomationBench).
Kimi K3 shows notably better performance in the majority of benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, Kimi K3 ($2.85/1M tokens) is 4.2x more expensive than Mistral Large 4 ($0.68/1M tokens).
For output processing, Kimi K3 ($14.25/1M tokens) is 6.8x more expensive than Mistral Large 4 ($2.09/1M tokens).
In conclusion, Kimi K3 is more expensive than Mistral Large 4.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Kimi K3 has 1750.0B more parameters than Mistral Large 4, making it 166.7% larger.
Context Window
Maximum input and output token capacity
Kimi K3 accepts 1,048,576 input tokens compared to Mistral Large 4's 1,000,000 tokens. Only Kimi K3 specifies output context (1,048,576 tokens).
Input capabilities
Documented input modalities across available providers
Both Kimi K3 and Mistral Large 4 support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Kimi K3
Mistral Large 4
License
Usage and distribution terms
Kimi K3 is licensed under Kimi K3 License, while Mistral Large 4 uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
Kimi K3 License
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
Kimi K3 was released on 2026-07-16, while Mistral Large 4 was released on 2026-10-06.
Mistral Large 4 is 3 months newer than Kimi K3.
Jul 16, 2026
2 months ago
Oct 6, 2026
6 days ago
2mo 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 K3 is available from DeepInfra, Fireworks, Moonshot AI, Novita, Together. Mistral Large 4 is available from Mistral AI.
Kimi K3
Mistral Large 4
Outputs Comparison
Judge for yourself.
Run your own prompts against Kimi K3 and Mistral Large 4 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Kimi K3 vs Mistral Large 4.