Model Comparison

GLM-4.6 vs Codestral-22B

Comparing GLM-4.6 and Codestral-22B across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

GLM-4.6 and Codestral-22B don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Model Size

Parameter count comparison

334.8B diff

GLM-4.6 has 334.8B more parameters than Codestral-22B, making it 1508.1% larger.

Zhipu AI
GLM-4.6
357.0Bparameters
Mistral AI
Codestral-22B
22.2Bparameters
357.0B
GLM-4.6
22.2B
Codestral-22B

Context Window

Maximum input and output token capacity

Only GLM-4.6 specifies input context (131,072 tokens). Only GLM-4.6 specifies output context (131,072 tokens).

Zhipu AI
GLM-4.6
Input131,072 tokens
Output131,072 tokens
Mistral AI
Codestral-22B
Input- tokens
Output- tokens
Sat May 30 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

GLM-4.6 supports multimodal inputs, whereas Codestral-22B does not.

GLM-4.6 can handle both text and other forms of data like images, making it suitable for multimodal applications.

GLM-4.6

Text
Images
Audio
Video

Codestral-22B

Text
Images
Audio
Video

License

Usage and distribution terms

GLM-4.6 is licensed under MIT, while Codestral-22B uses MNPL-0.1.

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

GLM-4.6

MIT

Open weights

Codestral-22B

MNPL-0.1

Open weights

Release Timeline

When each model was launched

GLM-4.6 was released on 2025-09-30, while Codestral-22B was released on 2024-05-29.

GLM-4.6 is 16 months newer than Codestral-22B.

GLM-4.6

Sep 30, 2025

8 months ago

1.3yr newer
Codestral-22B

May 29, 2024

2.0 years ago

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

Zhipu AI
Larger context window (131,072 tokens)
Supports multimodal inputs
Mistral AI

No standout differentiators in the data we have for this pair.

Detailed Comparison

AI Model Comparison Table
Feature
Zhipu AI
GLM-4.6
Mistral AI
Codestral-22B

FAQ

Common questions about GLM-4.6 vs Codestral-22B.

Which is better, GLM-4.6 or Codestral-22B?

GLM-4.6 (Zhipu AI) and Codestral-22B (Mistral AI) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does GLM-4.6 compare to Codestral-22B in benchmarks?

GLM-4.6 scores AIME 2025: 93.9%, LiveCodeBench v6: 82.8%, GPQA: 81.0%, SWE-Bench Verified: 68.0%, BrowseComp: 45.1%. Codestral-22B scores HumanEvalFIM-Average: 91.6%, HumanEval: 81.1%, MBPP: 78.2%, Spider: 63.5%, HumanEval-Average: 61.5%.

What are the context window sizes for GLM-4.6 and Codestral-22B?

GLM-4.6 supports 131K tokens and Codestral-22B supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between GLM-4.6 and Codestral-22B?

Key differences include multimodal support (yes vs no), licensing (MIT vs MNPL-0.1). See the full comparison above for benchmark-by-benchmark results.

Who makes GLM-4.6 and Codestral-22B?

GLM-4.6 is developed by Zhipu AI and Codestral-22B is developed by Mistral AI.