Noometry

Model comparison

GLM-4.7-Flash vs Mixtral 8x22B

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 8x22B Mistral AI

27.1

Rank #333 Confirmed

Summary

  • They share 17 benchmarks with published results for both. GLM-4.7-Flash scores higher in 8 categories and Mixtral 8x22B 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 15.1.
  • The biggest single-benchmark swing is GPQA Diamond: 60.5% for GLM-4.7-Flash and 34.1% for Mixtral 8x22B.
  • GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $2 / $6 for Mixtral 8x22B.
  • GLM-4.7-Flash accepts more context: 200K tokens versus 64K.

Side by side

GLM-4.7-FlashMixtral 8x22B
ProviderZ.ai (Zhipu)Mistral AI
Noometry Index38.827.1
Released2026-01-192024-04-17
WeightsOpenOpen
Context window200K64K
Max output131K64K
Input $ / M tokens$0.06$2
Output $ / M tokens$0.40$6
Results tracked2134

Sponsored placements are available on pages like this one. Advertise on Noometry

Category by category

Coding GLM-4.7-Flash leads

GLM-4.7-Flash: 40.6 (#135), Mixtral 8x22B: 24.2 (#329)

BenchmarkGLM-4.7-FlashMixtral 8x22B
LMArena Coding13831166
WeirdML—3.2%
BigCodeBench Instruct—40.6%
BigCodeBench Complete—50.2%
HumanEval+—72%
MBPP+—64.3%

Agentic & Tool Use Not comparable

GLM-4.7-Flash: —, Mixtral 8x22B: 23.1 (#127)

BenchmarkGLM-4.7-FlashMixtral 8x22B
Cybench—7.5%

Reasoning GLM-4.7-Flash leads

GLM-4.7-Flash: 20.9 (#229), Mixtral 8x22B: 19.9 (#248)

BenchmarkGLM-4.7-FlashMixtral 8x22B
LMArena Hard Prompts13561150
Chess Puzzles0%—
DTBench—55.1%
Epoch Capabilities Index—122.03
ForecastBench—56.3

Math GLM-4.7-Flash leads

GLM-4.7-Flash: 36.1 (#173), Mixtral 8x22B: 22.9 (#275)

BenchmarkGLM-4.7-FlashMixtral 8x22B
LMArena Math13551184
OTIS Mock AIME 2024-202558.3%—
Omni-MATH—16.3%
MATH Level 5—24.2%

Knowledge GLM-4.7-Flash leads

GLM-4.7-Flash: 35.5 (#184), Mixtral 8x22B: 15.1 (#293)

BenchmarkGLM-4.7-FlashMixtral 8x22B
GPQA Diamond60.5%34.1%
LMArena Expert13571113
MMLU-Pro—46%
Vectara Hallucination Rate9.3%—
GPQA (HELM)—33.4%
MMLU—77.8%

Multilingual GLM-4.7-Flash leads

GLM-4.7-Flash: 46.5 (#158), Mixtral 8x22B: 32.8 (#255)

BenchmarkGLM-4.7-FlashMixtral 8x22B
LMArena Non-English13301128
LMArena Chinese14031116
LMArena French13321166
LMArena German13371141
LMArena Korean12831057
LMArena Russian13321158
LMArena Spanish13501151
LMArena Japanese—1037

Instruction Following GLM-4.7-Flash leads

GLM-4.7-Flash: 70.1 (#167), Mixtral 8x22B: 57.7 (#266)

BenchmarkGLM-4.7-FlashMixtral 8x22B
LMArena Instruction Following13271147
IFEval—72.4%

Long Context GLM-4.7-Flash leads

GLM-4.7-Flash: 40.9 (#148), Mixtral 8x22B: 34.7 (#247)

BenchmarkGLM-4.7-FlashMixtral 8x22B
LMArena Longer Query13451144

Writing & Preference GLM-4.7-Flash leads

GLM-4.7-Flash: 47.4 (#210), Mixtral 8x22B: 36.9 (#262)

BenchmarkGLM-4.7-FlashMixtral 8x22B
LMArena Text13511162
LMArena Creative Writing12971141
LMArena Multi-Turn13421130
EQ-Bench Creative Writing1125—
WildBench—71.1%

Frequently asked questions

Is GLM-4.7-Flash better than Mixtral 8x22B?

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 8x22B?

GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; Mixtral 8x22B lists at $2 and $6.

Is GLM-4.7-Flash or Mixtral 8x22B better for coding?

GLM-4.7-Flash scores higher on coding benchmarks: 40.6 versus 24.2 in the Noometry coding category.

Which has the bigger context window?

GLM-4.7-Flash does, with 200K tokens against 64K.

How many benchmarks do GLM-4.7-Flash and Mixtral 8x22B share?

17 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and Mixtral 8x22B has 34.

Related comparisons

Go deeper