Noometry

Model comparison

GLM-4.7-Flash vs Mixtral 8x7B

GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 27.1 on the Noometry Index.

Last verified . 17 shared benchmarks.

GLM-4.7-Flash Z.ai (Zhipu)

38.8

Rank #180 Confirmed

Mixtral 8x7B Mistral AI

27.1

Rank #334 Confirmed

Summary

  • They share 17 benchmarks with published results for both. GLM-4.7-Flash scores higher in 8 categories and Mixtral 8x7B in 0 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in knowledge, where GLM-4.7-Flash leads 35.5 to 11.0.
  • The biggest single-benchmark swing is GPQA Diamond: 60.5% for GLM-4.7-Flash and 30.6% for Mixtral 8x7B.
  • GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $0.70 / $0.70 for Mixtral 8x7B.
  • GLM-4.7-Flash accepts more context: 200K tokens versus 32K.

Side by side

GLM-4.7-FlashMixtral 8x7B
ProviderZ.ai (Zhipu)Mistral AI
Noometry Index38.827.1
Released2026-01-192023-12-11
WeightsOpenOpen
Context window200K32K
Max output131K32K
Input $ / M tokens$0.06$0.70
Output $ / M tokens$0.40$0.70
Results tracked2138

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Category by category

Coding GLM-4.7-Flash leads

GLM-4.7-Flash: 40.6 (#135), Mixtral 8x7B: 32.8 (#269)

BenchmarkGLM-4.7-FlashMixtral 8x7B
LMArena Coding13831126
HumanEval+—39.6%
MBPP+—49.7%

Reasoning GLM-4.7-Flash leads

GLM-4.7-Flash: 20.9 (#229), Mixtral 8x7B: 18.2 (#285)

BenchmarkGLM-4.7-FlashMixtral 8x7B
LMArena Hard Prompts13561115
Chess Puzzles0%—
DTBench—49.6%
Adversarial NLI—55.2%
Epoch Capabilities Index—118.47
ForecastBench—56.3
HellaSwag—86.7%
PIQA—83.6%
WinoGrande—77.2%

Math GLM-4.7-Flash leads

GLM-4.7-Flash: 36.1 (#173), Mixtral 8x7B: 18.8 (#289)

BenchmarkGLM-4.7-FlashMixtral 8x7B
LMArena Math13551147
OTIS Mock AIME 2024-202558.3%—
Omni-MATH—10.5%
MATH Level 5—10%
GSM8K—74.4%

Knowledge GLM-4.7-Flash leads

GLM-4.7-Flash: 35.5 (#184), Mixtral 8x7B: 11.0 (#301)

BenchmarkGLM-4.7-FlashMixtral 8x7B
GPQA Diamond60.5%30.6%
LMArena Expert13571088
MMLU-Pro—33.5%
Vectara Hallucination Rate9.3%—
GPQA (HELM)—29.6%
ARC (AI2) Challenge—87.3%
MMLU—70.6%
OpenBookQA—85.8%
TriviaQA—82.2%

Multilingual GLM-4.7-Flash leads

GLM-4.7-Flash: 46.5 (#158), Mixtral 8x7B: 29.6 (#266)

BenchmarkGLM-4.7-FlashMixtral 8x7B
LMArena Non-English13301077
LMArena Chinese14031055
LMArena French13321166
LMArena German13371114
LMArena Korean1283968
LMArena Russian13321090
LMArena Spanish13501111
LMArena Japanese—931

Instruction Following GLM-4.7-Flash leads

GLM-4.7-Flash: 70.1 (#167), Mixtral 8x7B: 51.0 (#297)

BenchmarkGLM-4.7-FlashMixtral 8x7B
LMArena Instruction Following13271109
IFEval—57.5%

Long Context GLM-4.7-Flash leads

GLM-4.7-Flash: 40.9 (#148), Mixtral 8x7B: 33.4 (#260)

BenchmarkGLM-4.7-FlashMixtral 8x7B
LMArena Longer Query13451103

Writing & Preference GLM-4.7-Flash leads

GLM-4.7-Flash: 47.4 (#210), Mixtral 8x7B: 34.2 (#270)

BenchmarkGLM-4.7-FlashMixtral 8x7B
LMArena Text13511132
LMArena Creative Writing12971109
LMArena Multi-Turn13421115
EQ-Bench Creative Writing1125—
WildBench—67.3%

Frequently asked questions

Is GLM-4.7-Flash better than Mixtral 8x7B?

GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 27.1 on the Noometry Index.

Which is cheaper, GLM-4.7-Flash or Mixtral 8x7B?

GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; Mixtral 8x7B lists at $0.70 and $0.70.

Is GLM-4.7-Flash or Mixtral 8x7B better for coding?

GLM-4.7-Flash scores higher on coding benchmarks: 40.6 versus 32.8 in the Noometry coding category.

Which has the bigger context window?

GLM-4.7-Flash does, with 200K tokens against 32K.

How many benchmarks do GLM-4.7-Flash and Mixtral 8x7B share?

17 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and Mixtral 8x7B has 38.

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