Model Comparison

Codestral-22B vs Llama 4 Scout

Codestral-22B significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

1 benchmarks

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

Codestral-22B significantly outperforms across most benchmarks.

Sat Apr 18 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Sat Apr 18 2026 • llm-stats.com
Mistral AI
Codestral-22B
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Meta
Llama 4 Scout
Input tokens$0.08
Output tokens$0.30
Best providerDeepinfra
Notice missing or incorrect data?Start an Issue→

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
Sat Apr 18 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

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

1.9 years ago

Llama 4 Scout

Apr 5, 2025

1.0 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

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Key Takeaways

Mistral AI
Higher MBPP score (78.2% vs 67.8%)
Meta
Larger context window (10,000,000 tokens)
Supports multimodal inputs

Detailed Comparison

AI Model Comparison Table
Feature
Mistral AI
Codestral-22B
Meta
Llama 4 Scout

FAQ

Common questions about Codestral-22B vs Llama 4 Scout

Codestral-22B significantly outperforms across most benchmarks. 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 benchmark scores, pricing, and capabilities above.
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%.
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.
Key differences include 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.
Codestral-22B is developed by Mistral AI and Llama 4 Scout is developed by Meta.