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Codestral-22B vs Llama 4 Scout

Codestral-22B and Llama 4 Scout are closely matched at 0.0 and 7.8 on the LLM Stats Score.

Mistral AI · Meta · Updated for 2026

Which is better?

Codestral-22B and Llama 4 Scout are closely matched on the overall LLM Stats Score at 0.0 and 7.8.

In the 1 individual benchmarks reported for both models, Codestral-22B wins 1; this is a narrower head-to-head signal than the composite indexes.

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

Choose Codestral-22B

  • you value its reported benchmark strengths — it wins 1 of 1 exact shared results

Choose Llama 4 Scout

  • you want the most recent training data — it shipped Apr 2025

At a glance

The differences that matter most.

Core performance indexes
0.0
#326
7.8
#283
-0.1
#318
6.6
#286
2.5
#225
0.4
#240
Cost, coverage & limits
Benchmark wins
1 of 1
0 of 1
Input price
— / M
$0.08 / M
Output price
— / M
$0.30 / M
Context window
10,000,000

Individual benchmarks

7 reported for Codestral-22B · 12 for Llama 4 Scout

1 shared

Codestral-22B outperforms in 1 benchmarks (MBPP), while Llama 4 Scout is better at 0 benchmarks.

Codestral-22B significantly outperforms across most benchmarks.

Tue Sep 15 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

86.8B diff

Llama 4 Scout has 86.8B more parameters than Codestral-22B, making it 391.0% larger.

Mistral AI
Codestral-22B
22.2Bparameters
Meta
Llama 4 Scout
109.0Bparameters
22.2B
Codestral-22B
109.0B
Llama 4 Scout

Context Window

Maximum input and output token capacity

Only Llama 4 Scout specifies input context (10,000,000 tokens). Only Llama 4 Scout specifies output context (10,000,000 tokens).

Mistral AI
Codestral-22B
Input- tokens
Output- tokens
Meta
Llama 4 Scout
Input10,000,000 tokens
Output10,000,000 tokens
Tue Sep 15 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Llama 4 Scout supports multimodal inputs, whereas Codestral-22B does not.

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

Codestral-22B

Text
Images
Audio
Video

Llama 4 Scout

Text
Images
Audio
Video

License

Usage and distribution terms

Codestral-22B is licensed under MNPL-0.1, while Llama 4 Scout uses Llama 4 Community License Agreement.

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

Codestral-22B

MNPL-0.1

Open weights

Llama 4 Scout

Llama 4 Community License Agreement

Open weights

Release Timeline

When each model was launched

Codestral-22B was released on 2024-05-29, while Llama 4 Scout was released on 2025-04-05.

Llama 4 Scout is 10 months newer than Codestral-22B.

Codestral-22B

May 29, 2024

2.3 years ago

Llama 4 Scout

Apr 5, 2025

1.4 years ago

10mo 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?Start an Issue discussion

Judge for yourself.

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

Codestral-22B
✓ Preferred
Llama 4 Scout
Open in Playground

FAQ

Common questions about Codestral-22B vs Llama 4 Scout.

Which is better, Codestral-22B or Llama 4 Scout?

Codestral-22B and Llama 4 Scout are closely matched on the LLM Stats Score at 0.0 and 7.8. Codestral-22B is made by Mistral AI and Llama 4 Scout is made by Meta. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does Codestral-22B compare to Llama 4 Scout in benchmarks?

Codestral-22B scores HumanEvalFIM-Average: 91.6%, HumanEval: 81.1%, MBPP: 78.2%, Spider: 63.5%, HumanEval-Average: 61.5%. Llama 4 Scout scores DocVQA: 94.4%, MGSM: 90.6%, ChartQA: 88.8%, MMLU: 79.6%, MMLU-Pro: 74.3%.

What are the context window sizes for Codestral-22B and Llama 4 Scout?

Codestral-22B supports an unknown number of tokens and Llama 4 Scout supports 10.0M 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 Llama 4 Scout?

Key differences include LLM Stats Score (0.0 vs 7.8), multimodal support (no vs yes), licensing (MNPL-0.1 vs Llama 4 Community License Agreement). See the full comparison above for benchmark-by-benchmark results.

Who makes Codestral-22B and Llama 4 Scout?

Codestral-22B is developed by Mistral AI and Llama 4 Scout is developed by Meta.