Noometry

Model comparison

GLM-4.7-Flash vs Ministral 8B

GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 28.2 on the Noometry Index.

Last verified . 14 shared benchmarks.

GLM-4.7-Flash Z.ai (Zhipu)

38.8

Rank #180 Confirmed

Ministral 8B Mistral AI

28.2

Rank #325 Confirmed

Summary

  • They share 14 benchmarks with published results for both. GLM-4.7-Flash scores higher in 8 categories and Ministral 8B 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 12.6.
  • The biggest single-benchmark swing is GPQA Diamond: 60.5% for GLM-4.7-Flash and 27.1% for Ministral 8B.
  • Both cost about the same: $0.06 input and $0.40 output per million tokens.
  • Ministral 8B accepts more context: 262K tokens versus 200K.

Side by side

GLM-4.7-FlashMinistral 8B
ProviderZ.ai (Zhipu)Mistral AI
Noometry Index38.828.2
Released2026-01-192024-10-01
WeightsOpenOpen
Context window200K262K
Max output131K262K
Input $ / M tokens$0.06$0.15
Output $ / M tokens$0.40$0.15
Results tracked2117

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Category by category

Coding GLM-4.7-Flash leads

GLM-4.7-Flash: 40.6 (#135), Ministral 8B: 35.0 (#230)

BenchmarkGLM-4.7-FlashMinistral 8B
LMArena Coding13831202

Agentic & Tool Use Not comparable

GLM-4.7-Flash: —, Ministral 8B: 16.4 (#148)

BenchmarkGLM-4.7-FlashMinistral 8B
Berkeley Function Calling Leaderboard—11.1%

Reasoning GLM-4.7-Flash leads

GLM-4.7-Flash: 20.9 (#229), Ministral 8B: 18.4 (#281)

BenchmarkGLM-4.7-FlashMinistral 8B
LMArena Hard Prompts13561191
Chess Puzzles0%—
DTBench—45.7%

Math GLM-4.7-Flash leads

GLM-4.7-Flash: 36.1 (#173), Ministral 8B: 25.7 (#267)

BenchmarkGLM-4.7-FlashMinistral 8B
LMArena Math13551188
OTIS Mock AIME 2024-202558.3%—
MATH Level 5—14.9%

Knowledge GLM-4.7-Flash leads

GLM-4.7-Flash: 35.5 (#184), Ministral 8B: 12.6 (#297)

BenchmarkGLM-4.7-FlashMinistral 8B
GPQA Diamond60.5%27.1%
Vectara Hallucination Rate9.3%7.4%
LMArena Expert13571170

Multilingual GLM-4.7-Flash leads

GLM-4.7-Flash: 46.5 (#158), Ministral 8B: 35.1 (#247)

BenchmarkGLM-4.7-FlashMinistral 8B
LMArena Non-English13301165
LMArena Chinese14031193
LMArena Russian13321195
LMArena French1332—
LMArena German1337—
LMArena Korean1283—
LMArena Spanish1350—

Instruction Following GLM-4.7-Flash leads

GLM-4.7-Flash: 70.1 (#167), Ministral 8B: 60.5 (#250)

BenchmarkGLM-4.7-FlashMinistral 8B
LMArena Instruction Following13271161

Long Context GLM-4.7-Flash leads

GLM-4.7-Flash: 40.9 (#148), Ministral 8B: 36.7 (#227)

BenchmarkGLM-4.7-FlashMinistral 8B
LMArena Longer Query13451212

Writing & Preference GLM-4.7-Flash leads

GLM-4.7-Flash: 47.4 (#210), Ministral 8B: 39.6 (#246)

BenchmarkGLM-4.7-FlashMinistral 8B
LMArena Text13511191
LMArena Creative Writing12971175
LMArena Multi-Turn13421166
EQ-Bench Creative Writing1125—

Frequently asked questions

Is GLM-4.7-Flash better than Ministral 8B?

GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 28.2 on the Noometry Index.

Which is cheaper, GLM-4.7-Flash or Ministral 8B?

GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; Ministral 8B lists at $0.15 and $0.15.

Is GLM-4.7-Flash or Ministral 8B better for coding?

GLM-4.7-Flash scores higher on coding benchmarks: 40.6 versus 35.0 in the Noometry coding category.

Which has the bigger context window?

Ministral 8B does, with 262K tokens against 200K.

How many benchmarks do GLM-4.7-Flash and Ministral 8B share?

14 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and Ministral 8B has 17.

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