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Codestral-22B vs Mistral Large 4

Mistral Large 4 leads the LLM Stats Score 46.4 to 0.0.

Mistral AI · Mistral AI · Updated for 2026

Which is better?

Mistral Large 4 leads the overall LLM Stats Score 46.4 to 0.0, ranking #32 overall.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose Codestral-22B

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

Choose Mistral Large 4

  • overall performance matters — it scores 46.4 and ranks #32 on LLM Stats
  • your work emphasizes reasoning and coding — it leads those capability indexes
  • you want the most recent training data — it shipped Oct 2026

At a glance

The differences that matter most.

Codestral-22B
Mistral AI
Mistral Large 4
Mistral AI
Core performance indexes
LLM Stats Score
0.0
#339
46.4
#32
Reasoning
-0.1
#331
44.1
#41
Coding
2.4
#237
36.7
#23
Cost, coverage & limits
Benchmark wins
—
—
Input price
— / M
$0.68 / M
Output price
— / M
$2.09 / M
Context window
—
1,000,000

Individual benchmarks

7 reported for Codestral-22B · 15 for Mistral Large 4

No common benchmarks found

Codestral-22B and Mistral Large 4don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

1027.8B diff

Mistral Large 4 has 1027.8B more parameters than Codestral-22B, making it 4629.7% larger.

Mistral AI
Codestral-22B
22.2Bparameters
Mistral AI
Mistral Large 4
1.1Tparameters
22.2B
Codestral-22B
1050.0B
Mistral Large 4

Context Window

Maximum input and output token capacity

Only Mistral Large 4 specifies input context (1,000,000 tokens).

Mistral AI
Codestral-22B
Input- tokens
Output- tokens
Mistral AI
Mistral Large 4
Input1,000,000 tokens
Output- tokens
Wed Oct 07 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Mistral Large 4 supports multimodal inputs, whereas Codestral-22B does not.

Mistral Large 4 can handle both text and other forms of data like images, making it suitable for multimodal applications.

Codestral-22B

Text
Images
Audio
Video

Mistral Large 4

Text
Images
Audio
Video

License

Usage and distribution terms

Codestral-22B is licensed under MNPL-0.1, while Mistral Large 4 uses a proprietary license.

License differences may affect how you can use these models in commercial or open-source projects.

Codestral-22B

MNPL-0.1

Open weights

Mistral Large 4

Proprietary

Closed source

Release Timeline

When each model was launched

Codestral-22B was released on 2024-05-29, while Mistral Large 4 was released on 2026-10-06.

Mistral Large 4 is 29 months newer than Codestral-22B.

Codestral-22B

May 29, 2024

2.4 years ago

Mistral Large 4

Oct 6, 2026

1 days ago

2.4yr 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

Outputs Comparison

Notice missing or incorrect data?

Judge for yourself.

Run your own prompts against Codestral-22B and Mistral Large 4 side-by-side, then vote on the output you prefer.

Codestral-22B
✓ Preferred
Mistral Large 4
Open in Playground

FAQ

Common questions about Codestral-22B vs Mistral Large 4.

Which is better, Codestral-22B or Mistral Large 4?

Mistral Large 4 leads the LLM Stats Score 46.4 to 0.0. Codestral-22B is made by Mistral 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 Codestral-22B compare to Mistral Large 4 in benchmarks?

Codestral-22B scores HumanEvalFIM-Average: 91.6%, HumanEval: 81.1%, MBPP: 78.2%, Spider: 63.5%, HumanEval-Average: 61.5%. Mistral Large 4 scores CyBench: 93.0%, SciCode: 91.8%, CyberGym: 82.0%, Finch (FinWorkBench): 67.4%, ChartQAPro: 63.1%.

What are the context window sizes for Codestral-22B and Mistral Large 4?

Codestral-22B supports an unknown number of 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 Codestral-22B and Mistral Large 4?

Key differences include LLM Stats Score (0.0 vs 46.4), multimodal support (no vs yes), licensing (MNPL-0.1 vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

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