Codestral-22B vs Llama 4 Scout Comparison
Comparing Codestral-22B and Llama 4 Scout across benchmarks, pricing, and capabilities.
Performance Benchmarks
Comparative analysis across standard metrics
Codestral-22B outperforms in 1 benchmarks (MBPP), while Llama 4 Scout is better at 0 benchmarks.
Codestral-22B significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
Cost data unavailable.
Model Size
Parameter count comparison
Llama 4 Scout has 86.8B more parameters than Codestral-22B, making it 391.0% larger.
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).
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
Llama 4 Scout
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.
MNPL-0.1
Open weights
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.
May 29, 2024
1.8 years ago
Apr 5, 2025
11 months ago
10mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Outputs Comparison
Key Takeaways
Codestral-22B
View detailsMistral AI
Detailed Comparison
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