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Kimi K2.7 Code vs Mistral Large 4

Kimi K2.7 Code and Mistral Large 4 are closely matched at 38.7 and 45.8 on the LLM Stats Score. Mistral Large 4 is 1.3x cheaper per token.

Moonshot AI · Mistral AI · Updated for 2026

Which is better?

Kimi K2.7 Code and Mistral Large 4 are closely matched on the overall LLM Stats Score at 38.7 and 45.8.

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 1.3x 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 K2.7 Code

  • you need open weights you can self-host or fine-tune

Choose Mistral Large 4

  • your work emphasizes reasoning — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 1 of 1 exact shared results
  • cost matters — it's about 1.3x 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.

Kimi K2.7 Code
Moonshot AI
Mistral Large 4
Mistral AI
Core performance indexes
LLM Stats Score
38.7
#79
45.8
#39
Reasoning
33.8
#112
43.9
#48
Coding
28.7
#70
35.0
#31
Agents
25.9
#66
33.8
#34
Cost, coverage & limits
Benchmark wins
0 of 1
1 of 1
Input price
$0.68 / M
$0.68 / M
Output price
$3.40 / M
$2.09 / M
Context window
262,144
1,000,000

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
Kimi K2.7 Code
Mistral Large 4
Tool use
23.0#50
28.1#23
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

9 reported for Kimi K2.7 Code · 18 for Mistral Large 4

1 shared

Kimi K2.7 Code outperforms in 0 benchmarks, while Mistral Large 4 is better at 1 benchmark (DeepSWE 1.1).

Mistral Large 4 significantly outperforms across most benchmarks.

Thu Oct 22 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Pricing Analysis

Price comparison per million tokens

Mistral Large 4 costs less

For input processing, Kimi K2.7 Code ($0.68/1M tokens) costs the same as Mistral Large 4 ($0.68/1M tokens).

For output processing, Kimi K2.7 Code ($3.40/1M tokens) is 1.6x more expensive than Mistral Large 4 ($2.09/1M tokens).

In conclusion, Kimi K2.7 Code is more expensive than Mistral Large 4.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Thu Oct 22 2026 • llm-stats.com
Moonshot AI
Kimi K2.7 Code
Input tokens$0.68
Output tokens$3.40
Best providerDeepinfra
Mistral AI
Mistral Large 4
Input tokens$0.68
Output tokens$2.09
Best providerMistral
Notice missing or incorrect data?

Model Size

Parameter count comparison

50.0B diff

Mistral Large 4 has 50.0B more parameters than Kimi K2.7 Code, making it 5.0% larger.

Moonshot AI
Kimi K2.7 Code
1.0Tparameters
Mistral AI
Mistral Large 4
1.1Tparameters
1000.0B
Kimi K2.7 Code
1050.0B
Mistral Large 4

Context Window

Maximum input and output token capacity

Mistral Large 4 accepts 1,000,000 input tokens compared to Kimi K2.7 Code's 262,144 tokens. Only Kimi K2.7 Code specifies output context (262,144 tokens).

Moonshot AI
Kimi K2.7 Code
Input262,144 tokens
Output262,144 tokens
Mistral AI
Mistral Large 4
Input1,000,000 tokens
Output- tokens
Thu Oct 22 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both Kimi K2.7 Code and Mistral Large 4 support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

Kimi K2.7 Code

Text
Images
Audio
Video

Mistral Large 4

Text
Images
Audio
Video

License

Usage and distribution terms

Kimi K2.7 Code is licensed under Modified MIT 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 K2.7 Code

Modified MIT License

Open weights

Mistral Large 4

Proprietary

Closed source

Release Timeline

When each model was launched

Kimi K2.7 Code was released on 2026-06-12, while Mistral Large 4 was released on 2026-10-06.

Mistral Large 4 is 4 months newer than Kimi K2.7 Code.

Kimi K2.7 Code

Jun 12, 2026

4 months ago

Mistral Large 4

Oct 6, 2026

1 weeks ago

3mo newer

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Provider Availability

Kimi K2.7 Code is available from DeepInfra, Fireworks, Moonshot AI, Novita, Together. Mistral Large 4 is available from Mistral AI.

Kimi K2.7 Code

deepinfra logo
Deepinfra
Input Price:Input: $0.68/1MOutput Price:Output: $3.40/1M
fireworks logo
Fireworks
Input Price:Input: $0.95/1MOutput Price:Output: $4.00/1M
moonshot logo
Unknown Organization
Input Price:Input: $0.95/1MOutput Price:Output: $4.00/1M
novita logo
Novita
Input Price:Input: $0.95/1MOutput Price:Output: $4.00/1M
together logo
Together
Input Price:Input: $0.95/1MOutput Price:Output: $4.00/1M

Mistral Large 4

mistral logo
Mistral
Input Price:Input: $0.68/1MOutput Price:Output: $2.09/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?

Judge for yourself.

Run your own prompts against Kimi K2.7 Code and Mistral Large 4 side-by-side, then vote on the output you prefer.

Kimi K2.7 Code
✓ Preferred
Mistral Large 4
Open in Playground

FAQ

Common questions about Kimi K2.7 Code vs Mistral Large 4.

Which is better, Kimi K2.7 Code or Mistral Large 4?

Kimi K2.7 Code and Mistral Large 4 are closely matched on the LLM Stats Score at 38.7 and 45.8. Kimi K2.7 Code is made by Moonshot AI and Mistral Large 4 is made by Mistral AI. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does Kimi K2.7 Code compare to Mistral Large 4 in benchmarks?

Kimi K2.7 Code scores MCP-Mark: 81.1%, MCP Atlas: 76.0%, LiveBench: 71.9%, Kimi Code Bench v2: 62.0%, Program Bench: 53.6%. Mistral Large 4 scores B3 AI Security Benchmark: 93.3%, CyBench: 93.0%, SciCode: 91.8%, KORABench: 84.5%, CyberGym: 82.0%.

Is Kimi K2.7 Code cheaper than Mistral Large 4?

Both models cost $0.68 per million input tokens.

What are the context window sizes for Kimi K2.7 Code and Mistral Large 4?

Kimi K2.7 Code supports 262K tokens and Mistral Large 4 supports 1.0M tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Kimi K2.7 Code and Mistral Large 4?

Key differences include LLM Stats Score (38.7 vs 45.8), context window (262K vs 1.0M), licensing (Modified MIT License vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes Kimi K2.7 Code and Mistral Large 4?

Kimi K2.7 Code is developed by Moonshot AI and Mistral Large 4 is developed by Mistral AI.

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