Model comparison

GLM-5 vs Pixtral Large

GLM-5 is the stronger model overall, scoring 46.1 to 32.2 on the Noometry Index.

Last verified . 1 shared benchmarks.

GLM-5 Z.ai (Zhipu)

46.1

Rank #66 Confirmed

Pixtral Large Mistral AI

32.2

Rank #259 Reported

Summary

  • They share 1 benchmark with published results for both. GLM-5 scores higher in 2 categories and Pixtral Large in 0 categories; 2 gaps are clear of the uncertainty.
  • The widest gap is in writing & preference, where GLM-5 leads 66.0 to 32.9.
  • GLM-5 is cheaper at $1 / $3.20 per million input/output tokens, against $2 / $6 for Pixtral Large.
  • GLM-5 accepts more context: 205K tokens versus 128K.

Side by side

GLM-5 and Pixtral Large specifications
GLM-5Pixtral Large
ProviderZ.ai (Zhipu)Mistral AI
Noometry Index46.132.2
Released2026-02-112024-11-01
WeightsOpenOpen
Context window205K128K
Max output131K128K
Input $ / M tokens$1$2
Output $ / M tokens$3.20$6
Results tracked453

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Category by category

Coding Not comparable

GLM-5: 49.0 (#52), Pixtral Large: —

Coding benchmarks
BenchmarkGLM-5Pixtral Large
SWE-bench Verified72.1%—
SWE-bench Verified (bash only)72.8%—
LMArena WebDev1434—
SWE-bench Multilingual69.7%—
WeirdML48.2%—
LMArena Coding1461—
ALE-Bench765.62—

Agentic & Tool Use Not comparable

GLM-5: 31.1 (#71), Pixtral Large: —

Agentic & Tool Use benchmarks
BenchmarkGLM-5Pixtral Large
Terminal-Bench52.4%—
τ²-bench Airline82.5%—
τ²-bench Banking9.8%—
τ²-bench Retail73.7%—
τ²-bench Telecom86.8%—
Vending-Bench 24,432—

Reasoning GLM-5 leads

GLM-5: 27.6 (#116), Pixtral Large: 21.7 (#218)

Reasoning benchmarks
BenchmarkGLM-5Pixtral Large
ARC-AGI-24.9%—
SimpleBench53.2%—
Kagi LLM Benchmark75%—
NYT Connections (extended)74.8%—
ARC-AGI-144.7%—
Chess Puzzles10%—
EnigmaEval—0.8%
LMArena Hard Prompts1452—
Epoch Capabilities Index145.83—
ForecastBench61—

Math Not comparable

GLM-5: 46.4 (#71), Pixtral Large: —

Knowledge Not comparable

GLM-5: 52.3 (#64), Pixtral Large: —

Knowledge benchmarks
BenchmarkGLM-5Pixtral Large
GPQA Diamond87.8%—
Vectara Hallucination Rate10.1%—
LMArena Expert1454—

Multimodal Not comparable

GLM-5: —, Pixtral Large: 30.6 (#111)

Multimodal benchmarks
BenchmarkGLM-5Pixtral Large
LMArena Vision—1089

Multilingual Not comparable

GLM-5: 53.7 (#58), Pixtral Large: —

Multilingual benchmarks
BenchmarkGLM-5Pixtral Large
LMArena Non-English1430—
LMArena Chinese1511—
LMArena French1455—
LMArena German1445—
LMArena Japanese1416—
LMArena Korean1423—
LMArena Russian1436—
LMArena Spanish1454—

Instruction Following Not comparable

GLM-5: 75.2 (#67), Pixtral Large: —

Instruction Following benchmarks
BenchmarkGLM-5Pixtral Large
LMArena Instruction Following1428—

Long Context Not comparable

GLM-5: 44.7 (#60), Pixtral Large: —

Long Context benchmarks
BenchmarkGLM-5Pixtral Large
CL-bench18.7%—
LMArena Longer Query1446—

Writing & Preference GLM-5 leads

GLM-5: 66.0 (#38), Pixtral Large: 32.9 (#278)

Writing & Preference benchmarks
BenchmarkGLM-5Pixtral Large
EQ-Bench Creative Writing1601988
LMArena Text1446—
LMArena Creative Writing1439—
LMArena Multi-Turn1456—

Frequently asked questions

Is GLM-5 better than Pixtral Large?

GLM-5 is the stronger model overall, scoring 46.1 to 32.2 on the Noometry Index.

Which is cheaper, GLM-5 or Pixtral Large?

GLM-5 is cheaper. It lists at $1 per million input tokens and $3.20 per million output tokens; Pixtral Large lists at $2 and $6.

Which has the bigger context window?

GLM-5 does, with 205K tokens against 128K.

How many benchmarks do GLM-5 and Pixtral Large share?

1 benchmark has published results for both models. GLM-5 has 45 scored results on Noometry and Pixtral Large has 3.

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