Noometry

Model comparison

GLM-5.3-Flash vs Mixtral 8x22B

GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 27.1 on the Noometry Index.

Last verified . 19 shared benchmarks.

GLM-5.3-Flash Z.ai (Zhipu)

51.8

Rank #41 Confirmed

Mixtral 8x22B Mistral AI

27.1

Rank #333 Confirmed

Summary

  • They share 19 benchmarks with published results for both. GLM-5.3-Flash 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-Flash leads 58.4 to 15.1.
  • The biggest single-benchmark swing is GPQA Diamond: 90.2% for GLM-5.3-Flash and 34.1% for Mixtral 8x22B.
  • GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $2 / $6 for Mixtral 8x22B.
  • GLM-5.3-Flash accepts more context: 1M tokens versus 64K.

Side by side

GLM-5.3-FlashMixtral 8x22B
ProviderZ.ai (Zhipu)Mistral AI
Noometry Index51.827.1
Released2026-08-202024-04-17
WeightsOpenOpen
Context window1M64K
Max output131K64K
Input $ / M tokens$0.15$2
Output $ / M tokens$0.50$6
Results tracked4034

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Category by category

Coding GLM-5.3-Flash leads

GLM-5.3-Flash: 53.1 (#31), Mixtral 8x22B: 24.2 (#329)

BenchmarkGLM-5.3-FlashMixtral 8x22B
LMArena Coding15081166
DeepSWE63.4%—
FrontierCode31.8%—
CursorBench36.8%—
LMArena WebDev1609—
FrontierSWE18.1%—
SciCode51.6%—
WeirdML—3.2%
BigCodeBench Instruct—40.6%
BigCodeBench Complete—50.2%
ALE-Bench303.55—
HumanEval+—72%
MBPP+—64.3%

Agentic & Tool Use GLM-5.3-Flash leads

GLM-5.3-Flash: 34.2 (#47), Mixtral 8x22B: 23.1 (#127)

BenchmarkGLM-5.3-FlashMixtral 8x22B
APEX-Agents52.8%—
Cybench—7.5%
GDP.pdf14%—

Reasoning GLM-5.3-Flash leads

GLM-5.3-Flash: 48.0 (#42), Mixtral 8x22B: 19.9 (#248)

BenchmarkGLM-5.3-FlashMixtral 8x22B
LMArena Hard Prompts14911150
Epoch Capabilities Index151.88122.03
ARC-AGI-265.8%—
ARC-AGI-191%—
CritPt15.4%—
Chess Puzzles14%—
Mystery Game Puzzles8%—
DTBench—55.1%
Surface Evolver Bench52.5%—
Bench to the Future 30.15—
ForecastBench—56.3

Math GLM-5.3-Flash leads

GLM-5.3-Flash: 53.3 (#47), Mixtral 8x22B: 22.9 (#275)

BenchmarkGLM-5.3-FlashMixtral 8x22B
LMArena Math15001184
FrontierMath (Tiers 1-3)55.8%—
FrontierMath Tier 417.1%—
OTIS Mock AIME 2024-202593.9%—
ProofBench21%—
Omni-MATH—16.3%
MATH Level 5—24.2%

Knowledge GLM-5.3-Flash leads

GLM-5.3-Flash: 58.4 (#36), Mixtral 8x22B: 15.1 (#293)

BenchmarkGLM-5.3-FlashMixtral 8x22B
GPQA Diamond90.2%34.1%
LMArena Expert15131113
MMLU-Pro—46%
GPQA (HELM)—33.4%
MMLU—77.8%

Multimodal Not comparable

GLM-5.3-Flash: 42.8 (#27), Mixtral 8x22B: —

BenchmarkGLM-5.3-FlashMixtral 8x22B
LMArena Vision1296—

Multilingual GLM-5.3-Flash leads

GLM-5.3-Flash: 56.0 (#25), Mixtral 8x22B: 32.8 (#255)

BenchmarkGLM-5.3-FlashMixtral 8x22B
LMArena Non-English14621128
LMArena Chinese15271116
LMArena French14961166
LMArena German14701141
LMArena Japanese14291037
LMArena Korean14461057
LMArena Russian14691158
LMArena Spanish14711151

Instruction Following GLM-5.3-Flash leads

GLM-5.3-Flash: 77.5 (#20), Mixtral 8x22B: 57.7 (#266)

BenchmarkGLM-5.3-FlashMixtral 8x22B
LMArena Instruction Following14781147
IFEval—72.4%

Long Context GLM-5.3-Flash leads

GLM-5.3-Flash: 45.4 (#39), Mixtral 8x22B: 34.7 (#247)

BenchmarkGLM-5.3-FlashMixtral 8x22B
LMArena Longer Query14821144

Writing & Preference GLM-5.3-Flash leads

GLM-5.3-Flash: 65.3 (#50), Mixtral 8x22B: 36.9 (#262)

BenchmarkGLM-5.3-FlashMixtral 8x22B
LMArena Text14711162
LMArena Creative Writing14421141
LMArena Multi-Turn14671130
WildBench—71.1%

Frequently asked questions

Is GLM-5.3-Flash better than Mixtral 8x22B?

GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 27.1 on the Noometry Index.

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

GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; Mixtral 8x22B lists at $2 and $6.

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

GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 24.2 in the Noometry coding category.

Which has the bigger context window?

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

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

19 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and Mixtral 8x22B has 34.

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