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Codestral-22B vs GLM-5.3

Comparing Codestral-22B and GLM-5.3 across benchmarks, pricing, and capabilities.

Mistral AI · Zhipu AI · Updated for 2026

Which is better?

Codestral-22B and GLM-5.3 trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.

Based on current benchmark, pricing, and model metadata for 2026.

Choose Codestral-22B

  • you need open weights you can self-host or fine-tune

Choose GLM-5.3

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

At a glance

The differences that matter most.

Codestral-22B
Mistral AI
GLM-5.3
Zhipu AI
Benchmark wins
—
—
Input price
— / M
$1.40 / M
Output price
— / M
$4.40 / M
Context window
—
1,000,000
Released
May 2024
Aug 2026
License
MNPL-0.1
Unknown

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

Codestral-22B and GLM-5.3don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Playground indexes and blind preference scores

Model Size

Parameter count comparison

730.8B diff

GLM-5.3 has 730.8B more parameters than Codestral-22B, making it 3291.9% larger.

Mistral AI
Codestral-22B
22.2Bparameters
Zhipu AI
GLM-5.3
753.0Bparameters
22.2B
Codestral-22B
753.0B
GLM-5.3

Context Window

Maximum input and output token capacity

Only GLM-5.3 specifies input context (1,000,000 tokens). Only GLM-5.3 specifies output context (128,000 tokens).

Mistral AI
Codestral-22B
Input- tokens
Output- tokens
Zhipu AI
GLM-5.3
Input1,000,000 tokens
Output128,000 tokens
Tue Aug 25 2026 • llm-stats.com

Release Timeline

When each model was launched

Codestral-22B was released on 2024-05-29, while GLM-5.3 was released on 2026-08-14.

GLM-5.3 is 27 months newer than Codestral-22B.

Codestral-22B

May 29, 2024

2.2 years ago

GLM-5.3

Aug 14, 2026

1 weeks ago

2.2yr 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→

Judge for yourself.

Run your own prompts against Codestral-22B and GLM-5.3 side-by-side, then vote on the output you prefer.

Codestral-22B
✓ Preferred
GLM-5.3
Open in Playground

FAQ

Common questions about Codestral-22B vs GLM-5.3.

Which is better, Codestral-22B or GLM-5.3?

Codestral-22B (Mistral AI) and GLM-5.3 (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-5.3 in benchmarks?

Codestral-22B scores HumanEvalFIM-Average: 91.6%, HumanEval: 81.1%, MBPP: 78.2%, Spider: 63.5%, HumanEval-Average: 61.5%. GLM-5.3 scores Terminal-Bench 2.1: 88.2%, CyberGym: 84.5%, FrontierSWE: 78.1%, Toolathlon: 73.0%, DeepSWE 1.1: 66.9%.

What are the context window sizes for Codestral-22B and GLM-5.3?

Codestral-22B supports an unknown number of tokens and GLM-5.3 supports 1.0M 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-5.3?

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

Who makes Codestral-22B and GLM-5.3?

Codestral-22B is developed by Mistral AI and GLM-5.3 is developed by Zhipu AI.

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