Model Comparison

GLM-5 vs Codestral-22BWhich is better in 2026?

Comparing GLM-5 and Codestral-22B across benchmarks, pricing, and capabilities.

Verdict: GLM-5 vs Codestral-22B — which is better?

GLM-5 (by Zhipu AI) and Codestral-22B (by Mistral 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 GLM-5 if…

  • you want the most recent training data — it shipped Feb 2026

Choose Codestral-22B if…

  • you are already invested in the Mistral AI ecosystem
Want to compare interactively?Try the playground

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

GLM-5 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

721.8B diff

GLM-5 has 721.8B more parameters than Codestral-22B, making it 3251.4% larger.

Zhipu AI
GLM-5
744.0Bparameters
Mistral AI
Codestral-22B
22.2Bparameters
744.0B
GLM-5
22.2B
Codestral-22B

Context Window

Maximum input and output token capacity

Only GLM-5 specifies input context (200,000 tokens). Only GLM-5 specifies output context (128,000 tokens).

Zhipu AI
GLM-5
Input200,000 tokens
Output128,000 tokens
Mistral AI
Codestral-22B
Input- tokens
Output- tokens
Sat Jun 06 2026 • llm-stats.com

License

Usage and distribution terms

GLM-5 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-5

MIT

Open weights

Codestral-22B

MNPL-0.1

Open weights

Release Timeline

When each model was launched

GLM-5 was released on 2026-02-11, while Codestral-22B was released on 2024-05-29.

GLM-5 is 21 months newer than Codestral-22B.

GLM-5

Feb 11, 2026

3 months ago

1.7yr 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

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Zhipu AI
Larger context window (200,000 tokens)
Mistral AI

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

Detailed Comparison

AI Model Comparison Table
Feature
Zhipu AI
GLM-5
Mistral AI
Codestral-22B

FAQ

Common questions about GLM-5 vs Codestral-22B.

Which is better, GLM-5 or Codestral-22B?

GLM-5 (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-5 compare to Codestral-22B in benchmarks?

GLM-5 scores t2-bench: 89.7%, SWE-Bench Verified: 77.8%, BrowseComp: 75.9%, MCP Atlas: 67.8%, Terminal-Bench 2.0: 56.2%. 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-5 and Codestral-22B?

GLM-5 supports 200K 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-5 and Codestral-22B?

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

Who makes GLM-5 and Codestral-22B?

GLM-5 is developed by Zhipu AI and Codestral-22B is developed by Mistral AI.

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