Model comparison

Codestral vs GLM-5.3

GLM-5.3 is the stronger model overall, scoring 54.8 to 30.6 on the Noometry Index. Codestral costs 4.8× less per token, which makes it the better buy when GLM-5.3's lead doesn't matter for your workload.

Last verified . 1 shared benchmarks.

Codestral Mistral AI

30.6

Rank #290 Reported

GLM-5.3 Z.ai (Zhipu)

54.8

Rank #26 Confirmed

Summary

  • They share 1 benchmark with published results for both. Codestral scores higher in 0 categories and GLM-5.3 in 2 categories; 2 gaps are clear of the uncertainty.
  • The widest gap is in coding, where GLM-5.3 leads 59.5 to 27.3.
  • Codestral is cheaper at $0.30 / $0.90 per million input/output tokens, against $1.40 / $4.40 for GLM-5.3.
  • GLM-5.3 accepts more context: 1M tokens versus 256K.
  • GLM-5.3 has downloadable open weights; the other is API-only.

Side by side

Codestral and GLM-5.3 specifications
CodestralGLM-5.3
ProviderMistral AIZ.ai (Zhipu)
Noometry Index30.654.8
Released2024-05-292026-08-14
WeightsProprietaryOpen
Context window256K1M
Max output8K131K
Input $ / M tokens$0.30$1.40
Output $ / M tokens$0.90$4.40
Results tracked742

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Category by category

Coding GLM-5.3 leads

Codestral: 27.3 (#321), GLM-5.3: 59.5 (#14)

Coding benchmarks
BenchmarkCodestralGLM-5.3
ALE-Bench137.781,317
DeepSWE—69%
FrontierCode—40.1%
Aider Polyglot11.1%—
CursorBench—42.6%
LMArena WebDev—1622
FrontierSWE—30.2%
SciCode—59%
WeirdML—75.4%
BigCodeBench Instruct41.8%—
LMArena Coding—1496
BigCodeBench Complete52.5%—
HumanEval+73.8%—
MBPP+61.9%—

Agentic & Tool Use Not comparable

Codestral: —, GLM-5.3: 36.4 (#38)

Agentic & Tool Use benchmarks
BenchmarkCodestralGLM-5.3
APEX-Agents—56.6%
Vending-Bench 2—8,164

Reasoning GLM-5.3 leads

Codestral: 19.8 (#251), GLM-5.3: 46.1 (#46)

Reasoning benchmarks
BenchmarkCodestralGLM-5.3
Kagi LLM Benchmark32.5%—
NYT Connections (extended)—74.2%
CritPt—19.1%
Chess Puzzles—21%
LMArena Hard Prompts—1489
Mystery Game Puzzles—33%
DTBench—87.7%
LMCA—55.5%
Bench to the Future 3—0.15
Epoch Capabilities Index—155.61

Math Not comparable

Codestral: —, GLM-5.3: 62.3 (#33)

Math benchmarks
BenchmarkCodestralGLM-5.3
FrontierMath (Tiers 1-3)—68.8%
FrontierMath Tier 4—29.3%
OTIS Mock AIME 2024-2025—91.1%
ProofBench—49%
LMArena Math—1489

Knowledge Not comparable

Codestral: —, GLM-5.3: 58.3 (#37)

Knowledge benchmarks
BenchmarkCodestralGLM-5.3
GPQA Diamond—90.9%
SimpleQA Verified—41%
LMArena Expert—1516

Multilingual Not comparable

Codestral: —, GLM-5.3: 55.7 (#28)

Multilingual benchmarks
BenchmarkCodestralGLM-5.3
LMArena Non-English—1457
LMArena Chinese—1528
LMArena French—1499
LMArena German—1499
LMArena Japanese—1453
LMArena Korean—1472
LMArena Russian—1463
LMArena Spanish—1460

Instruction Following Not comparable

Codestral: —, GLM-5.3: 77.5 (#23)

Instruction Following benchmarks
BenchmarkCodestralGLM-5.3
LMArena Instruction Following—1477

Long Context Not comparable

Codestral: —, GLM-5.3: 45.4 (#41)

Long Context benchmarks
BenchmarkCodestralGLM-5.3
LMArena Longer Query—1482

Writing & Preference Not comparable

Codestral: —, GLM-5.3: 75.7 (#6)

Writing & Preference benchmarks
BenchmarkCodestralGLM-5.3
LMArena Text—1471
LMArena Creative Writing—1457
EQ-Bench Creative Writing—2075
LMArena Multi-Turn—1472

Frequently asked questions

Is Codestral better than GLM-5.3?

GLM-5.3 is the stronger model overall, scoring 54.8 to 30.6 on the Noometry Index. Codestral costs 4.8× less per token, which makes it the better buy when GLM-5.3's lead doesn't matter for your workload.

Which is cheaper, Codestral or GLM-5.3?

Codestral is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; GLM-5.3 lists at $1.40 and $4.40.

Is Codestral or GLM-5.3 better for coding?

GLM-5.3 scores higher on coding benchmarks: 59.5 versus 27.3 in the Noometry coding category.

Which has the bigger context window?

GLM-5.3 does, with 1M tokens against 256K.

How many benchmarks do Codestral and GLM-5.3 share?

1 benchmark has published results for both models. Codestral has 7 scored results on Noometry and GLM-5.3 has 42.

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