Noometry

Model comparison

GLM-4.7-Flash vs Mistral 7B

GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 23.0 on the Noometry Index.

Last verified . 18 shared benchmarks.

GLM-4.7-Flash Z.ai (Zhipu)

38.8

Rank #180 Confirmed

Mistral 7B Mistral AI

23.0

Rank #351 Confirmed

Summary

  • They share 18 benchmarks with published results for both. GLM-4.7-Flash scores higher in 8 categories and Mistral 7B 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 7.4.
  • The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 58.3% for GLM-4.7-Flash and 0.3% for Mistral 7B.
  • GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $0.25 / $0.25 for Mistral 7B.
  • GLM-4.7-Flash accepts more context: 200K tokens versus 8K.

Side by side

GLM-4.7-FlashMistral 7B
ProviderZ.ai (Zhipu)Mistral AI
Noometry Index38.823.0
Released2026-01-192023-09-27
WeightsOpenOpen
Context window200K8K
Max output131K8K
Input $ / M tokens$0.06$0.25
Output $ / M tokens$0.40$0.25
Results tracked2137

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Category by category

Coding GLM-4.7-Flash leads

GLM-4.7-Flash: 40.6 (#135), Mistral 7B: 26.4 (#326)

BenchmarkGLM-4.7-FlashMistral 7B
LMArena Coding13831082
BigCodeBench Instruct—19.5%
BigCodeBench Complete—27.3%
HumanEval+—36%
MBPP+—42.1%

Reasoning GLM-4.7-Flash leads

GLM-4.7-Flash: 20.9 (#229), Mistral 7B: 13.1 (#336)

BenchmarkGLM-4.7-FlashMistral 7B
Chess Puzzles0%0%
LMArena Hard Prompts13561067
DTBench—42.5%
Adversarial NLI—47.1%
BIG-Bench Hard—56.1%
Epoch Capabilities Index—112.21
HellaSwag—81%
PIQA—83%
WinoGrande—75.3%

Math GLM-4.7-Flash leads

GLM-4.7-Flash: 36.1 (#173), Mistral 7B: 8.1 (#325)

BenchmarkGLM-4.7-FlashMistral 7B
OTIS Mock AIME 2024-202558.3%0.3%
LMArena Math13551085
MATH Level 5—3.7%
GSM8K—54.4%

Knowledge GLM-4.7-Flash leads

GLM-4.7-Flash: 35.5 (#184), Mistral 7B: 7.4 (#311)

BenchmarkGLM-4.7-FlashMistral 7B
GPQA Diamond60.5%15.2%
LMArena Expert13571036
Vectara Hallucination Rate9.3%—
ARC (AI2) Challenge—78.6%
BoolQ—87.4%
MMLU—62.5%
OpenBookQA—79.8%
TriviaQA—75.2%

Multilingual GLM-4.7-Flash leads

GLM-4.7-Flash: 46.5 (#158), Mistral 7B: 25.8 (#283)

BenchmarkGLM-4.7-FlashMistral 7B
LMArena Non-English13301012
LMArena Chinese14031009
LMArena French13321037
LMArena German1337987
LMArena Russian13321018
LMArena Spanish13501026
LMArena Japanese—878
LMArena Korean1283—

Instruction Following GLM-4.7-Flash leads

GLM-4.7-Flash: 70.1 (#167), Mistral 7B: 54.2 (#280)

BenchmarkGLM-4.7-FlashMistral 7B
LMArena Instruction Following13271060

Long Context GLM-4.7-Flash leads

GLM-4.7-Flash: 40.9 (#148), Mistral 7B: 32.2 (#271)

BenchmarkGLM-4.7-FlashMistral 7B
LMArena Longer Query13451060

Writing & Preference GLM-4.7-Flash leads

GLM-4.7-Flash: 47.4 (#210), Mistral 7B: 30.7 (#286)

BenchmarkGLM-4.7-FlashMistral 7B
LMArena Text13511090
LMArena Creative Writing12971068
LMArena Multi-Turn13421062
EQ-Bench Creative Writing1125—

Frequently asked questions

Is GLM-4.7-Flash better than Mistral 7B?

GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 23.0 on the Noometry Index.

Which is cheaper, GLM-4.7-Flash or Mistral 7B?

GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; Mistral 7B lists at $0.25 and $0.25.

Is GLM-4.7-Flash or Mistral 7B better for coding?

GLM-4.7-Flash scores higher on coding benchmarks: 40.6 versus 26.4 in the Noometry coding category.

Which has the bigger context window?

GLM-4.7-Flash does, with 200K tokens against 8K.

How many benchmarks do GLM-4.7-Flash and Mistral 7B share?

18 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and Mistral 7B has 37.

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