Noometry

Model comparison

GLM-5.3 vs Mixtral 8x22B

GLM-5.3 is the stronger model overall, scoring 54.8 to 27.1 on the Noometry Index.

Last verified . 21 shared benchmarks.

GLM-5.3 Z.ai (Zhipu)

54.8

Rank #26 Confirmed

Mixtral 8x22B Mistral AI

27.1

Rank #333 Confirmed

Summary

  • They share 21 benchmarks with published results for both. GLM-5.3 scores higher in 9 categories and Mixtral 8x22B in 0 categories; 9 gaps are clear of the uncertainty.
  • The widest gap is in knowledge, where GLM-5.3 leads 58.3 to 15.1.
  • The biggest single-benchmark swing is WeirdML: 75.4% for GLM-5.3 and 3.2% for Mixtral 8x22B.
  • GLM-5.3 is cheaper at $1.40 / $4.40 per million input/output tokens, against $2 / $6 for Mixtral 8x22B.
  • GLM-5.3 accepts more context: 1M tokens versus 64K.

Side by side

GLM-5.3Mixtral 8x22B
ProviderZ.ai (Zhipu)Mistral AI
Noometry Index54.827.1
Released2026-08-142024-04-17
WeightsOpenOpen
Context window1M64K
Max output131K64K
Input $ / M tokens$1.40$2
Output $ / M tokens$4.40$6
Results tracked4234

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Category by category

Coding GLM-5.3 leads

GLM-5.3: 59.5 (#14), Mixtral 8x22B: 24.2 (#329)

BenchmarkGLM-5.3Mixtral 8x22B
WeirdML75.4%3.2%
LMArena Coding14961166
DeepSWE69%—
FrontierCode40.1%—
CursorBench42.6%—
LMArena WebDev1622—
FrontierSWE30.2%—
SciCode59%—
BigCodeBench Instruct—40.6%
BigCodeBench Complete—50.2%
ALE-Bench1,317—
HumanEval+—72%
MBPP+—64.3%

Agentic & Tool Use GLM-5.3 leads

GLM-5.3: 36.4 (#38), Mixtral 8x22B: 23.1 (#127)

BenchmarkGLM-5.3Mixtral 8x22B
APEX-Agents56.6%—
Cybench—7.5%
Vending-Bench 28,164—

Reasoning GLM-5.3 leads

GLM-5.3: 46.1 (#46), Mixtral 8x22B: 19.9 (#248)

BenchmarkGLM-5.3Mixtral 8x22B
LMArena Hard Prompts14891150
DTBench87.7%55.1%
Epoch Capabilities Index155.61122.03
NYT Connections (extended)74.2%—
CritPt19.1%—
Chess Puzzles21%—
Mystery Game Puzzles33%—
LMCA55.5%—
Bench to the Future 30.15—
ForecastBench—56.3

Math GLM-5.3 leads

GLM-5.3: 62.3 (#33), Mixtral 8x22B: 22.9 (#275)

BenchmarkGLM-5.3Mixtral 8x22B
LMArena Math14891184
FrontierMath (Tiers 1-3)68.8%—
FrontierMath Tier 429.3%—
OTIS Mock AIME 2024-202591.1%—
ProofBench49%—
Omni-MATH—16.3%
MATH Level 5—24.2%

Knowledge GLM-5.3 leads

GLM-5.3: 58.3 (#37), Mixtral 8x22B: 15.1 (#293)

BenchmarkGLM-5.3Mixtral 8x22B
GPQA Diamond90.9%34.1%
LMArena Expert15161113
SimpleQA Verified41%—
MMLU-Pro—46%
GPQA (HELM)—33.4%
MMLU—77.8%

Multilingual GLM-5.3 leads

GLM-5.3: 55.7 (#28), Mixtral 8x22B: 32.8 (#255)

BenchmarkGLM-5.3Mixtral 8x22B
LMArena Non-English14571128
LMArena Chinese15281116
LMArena French14991166
LMArena German14991141
LMArena Japanese14531037
LMArena Korean14721057
LMArena Russian14631158
LMArena Spanish14601151

Instruction Following GLM-5.3 leads

GLM-5.3: 77.5 (#23), Mixtral 8x22B: 57.7 (#266)

BenchmarkGLM-5.3Mixtral 8x22B
LMArena Instruction Following14771147
IFEval—72.4%

Long Context GLM-5.3 leads

GLM-5.3: 45.4 (#41), Mixtral 8x22B: 34.7 (#247)

BenchmarkGLM-5.3Mixtral 8x22B
LMArena Longer Query14821144

Writing & Preference GLM-5.3 leads

GLM-5.3: 75.7 (#6), Mixtral 8x22B: 36.9 (#262)

BenchmarkGLM-5.3Mixtral 8x22B
LMArena Text14711162
LMArena Creative Writing14571141
LMArena Multi-Turn14721130
EQ-Bench Creative Writing2075—
WildBench—71.1%

Frequently asked questions

Is GLM-5.3 better than Mixtral 8x22B?

GLM-5.3 is the stronger model overall, scoring 54.8 to 27.1 on the Noometry Index.

Which is cheaper, GLM-5.3 or Mixtral 8x22B?

GLM-5.3 is cheaper. It lists at $1.40 per million input tokens and $4.40 per million output tokens; Mixtral 8x22B lists at $2 and $6.

Is GLM-5.3 or Mixtral 8x22B better for coding?

GLM-5.3 scores higher on coding benchmarks: 59.5 versus 24.2 in the Noometry coding category.

Which has the bigger context window?

GLM-5.3 does, with 1M tokens against 64K.

How many benchmarks do GLM-5.3 and Mixtral 8x22B share?

21 benchmarks have published results for both models. GLM-5.3 has 42 scored results on Noometry and Mixtral 8x22B has 34.

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