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Model Comparison

Codestral-22B vs GLM-4.5Which is better in 2026?

Comparing Codestral-22B and GLM-4.5 across benchmarks, pricing, and capabilities.

Verdict: Codestral-22B vs GLM-4.5 — which is better?

Codestral-22B (by Mistral AI) and GLM-4.5 (by Zhipu AI) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.

Choose Codestral-22B if…

  • you are already invested in the Mistral AI ecosystem

Choose GLM-4.5 if…

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

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

Codestral-22B and GLM-4.5don'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

332.8B diff

GLM-4.5 has 332.8B more parameters than Codestral-22B, making it 1499.1% larger.

Mistral AI
Codestral-22B
22.2Bparameters
Zhipu AI
GLM-4.5
355.0Bparameters
22.2B
Codestral-22B
355.0B
GLM-4.5

Context Window

Maximum input and output token capacity

Only GLM-4.5 specifies input context (131,072 tokens). Only GLM-4.5 specifies output context (131,072 tokens).

Mistral AI
Codestral-22B
Input- tokens
Output- tokens
Zhipu AI
GLM-4.5
Input131,072 tokens
Output131,072 tokens
Fri Aug 07 2026 • llm-stats.com

License

Usage and distribution terms

Codestral-22B is licensed under MNPL-0.1, while GLM-4.5 uses MIT.

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

Codestral-22B

MNPL-0.1

Open weights

GLM-4.5

MIT

Open weights

Release Timeline

When each model was launched

Codestral-22B was released on 2024-05-29, while GLM-4.5 was released on 2025-07-28.

GLM-4.5 is 14 months newer than Codestral-22B.

Codestral-22B

May 29, 2024

2.2 years ago

GLM-4.5

Jul 28, 2025

1.0 years ago

1.2yr 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→

Key Takeaways

Mistral AI

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

Zhipu AI
Larger context window (131,072 tokens)

Detailed Comparison

Interactive Arena

Judge for yourself.

Run your own prompts against Codestral-22B and GLM-4.5 side-by-side, then vote on the output you prefer.

Codestral-22B
✓ Preferred
GLM-4.5
Open in Playground
AI Model Comparison Table
Feature
Mistral AI
Codestral-22B
Zhipu AI
GLM-4.5

FAQ

Common questions about Codestral-22B vs GLM-4.5.

Which is better, Codestral-22B or GLM-4.5?

Codestral-22B (Mistral AI) and GLM-4.5 (Zhipu 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 Codestral-22B compare to GLM-4.5 in benchmarks?

Codestral-22B scores HumanEvalFIM-Average: 91.6%, HumanEval: 81.1%, MBPP: 78.2%, Spider: 63.5%, HumanEval-Average: 61.5%. GLM-4.5 scores MATH-500: 98.2%, AIME 2024: 91.0%, MMLU-Pro: 84.6%, TAU-bench Retail: 79.7%, GPQA: 79.1%.

What are the context window sizes for Codestral-22B and GLM-4.5?

Codestral-22B supports an unknown number of tokens and GLM-4.5 supports 131K 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 GLM-4.5?

Key differences include licensing (MNPL-0.1 vs MIT). See the full comparison above for benchmark-by-benchmark results.

Who makes Codestral-22B and GLM-4.5?

Codestral-22B is developed by Mistral AI and GLM-4.5 is developed by Zhipu AI.

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