Noometry

Model comparison

Devstral Medium vs GLM-5.3

GLM-5.3 has enough public results to be ranked (#26); Devstral Medium does not yet, so treat this comparison as directional.

Last verified . 1 shared benchmarks.

Devstral Medium Mistral AI

30.1

Unranked Sparse

GLM-5.3 Z.ai (Zhipu)

54.8

Rank #26 Confirmed

Summary

  • They share 1 benchmark with published results for both. Devstral Medium 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 24.3.
  • Devstral Medium is cheaper at $0.40 / $2 per million input/output tokens, against $1.40 / $4.40 for GLM-5.3.
  • GLM-5.3 accepts more context: 1M tokens versus 262K.

Side by side

Devstral MediumGLM-5.3
ProviderMistral AIZ.ai (Zhipu)
Noometry Index30.154.8
Released2025-07-102026-08-14
WeightsOpenOpen
Context window262K1M
Max output262K131K
Input $ / M tokens$0.40$1.40
Output $ / M tokens$2$4.40
Results tracked242

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Category by category

Coding GLM-5.3 leads

Devstral Medium: 24.3, GLM-5.3: 59.5 (#14)

BenchmarkDevstral MediumGLM-5.3
LMArena WebDev10761622
DeepSWE—69%
FrontierCode—40.1%
CursorBench—42.6%
FrontierSWE—30.2%
SciCode—59%
WeirdML—75.4%
LMArena Coding—1496
ALE-Bench—1,317

Agentic & Tool Use Not comparable

Devstral Medium: —, GLM-5.3: 36.4 (#38)

BenchmarkDevstral MediumGLM-5.3
APEX-Agents—56.6%
Vending-Bench 2—8,164

Reasoning GLM-5.3 leads

Devstral Medium: 21.1, GLM-5.3: 46.1 (#46)

BenchmarkDevstral MediumGLM-5.3
Kagi LLM Benchmark37.7%—
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

Devstral Medium: —, GLM-5.3: 62.3 (#33)

BenchmarkDevstral MediumGLM-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

Devstral Medium: —, GLM-5.3: 58.3 (#37)

BenchmarkDevstral MediumGLM-5.3
GPQA Diamond—90.9%
SimpleQA Verified—41%
LMArena Expert—1516

Multilingual Not comparable

Devstral Medium: —, GLM-5.3: 55.7 (#28)

BenchmarkDevstral MediumGLM-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

Devstral Medium: —, GLM-5.3: 77.5 (#23)

BenchmarkDevstral MediumGLM-5.3
LMArena Instruction Following—1477

Long Context Not comparable

Devstral Medium: —, GLM-5.3: 45.4 (#41)

BenchmarkDevstral MediumGLM-5.3
LMArena Longer Query—1482

Writing & Preference Not comparable

Devstral Medium: —, GLM-5.3: 75.7 (#6)

BenchmarkDevstral MediumGLM-5.3
LMArena Text—1471
LMArena Creative Writing—1457
EQ-Bench Creative Writing—2075
LMArena Multi-Turn—1472

Frequently asked questions

Is Devstral Medium better than GLM-5.3?

GLM-5.3 has enough public results to be ranked (#26); Devstral Medium does not yet, so treat this comparison as directional.

Which is cheaper, Devstral Medium or GLM-5.3?

Devstral Medium is cheaper. It lists at $0.40 per million input tokens and $2 per million output tokens; GLM-5.3 lists at $1.40 and $4.40.

Is Devstral Medium or GLM-5.3 better for coding?

GLM-5.3 scores higher on coding benchmarks: 59.5 versus 24.3 in the Noometry coding category.

Which has the bigger context window?

GLM-5.3 does, with 1M tokens against 262K.

How many benchmarks do Devstral Medium and GLM-5.3 share?

1 benchmark has published results for both models. Devstral Medium has 2 scored results on Noometry and GLM-5.3 has 42.

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