Noometry

Model comparison

GLM-4.6V vs Mistral Medium

GLM-4.6V is the stronger model overall, scoring 40.9 to 36.0 on the Noometry Index.

Last verified . 12 shared benchmarks.

GLM-4.6V Z.ai (Zhipu)

40.9

Rank #133 Confirmed

Mistral Medium Mistral AI

36.0

Rank #211 Confirmed

Summary

  • They share 12 benchmarks with published results for both. GLM-4.6V scores higher in 3 categories and Mistral Medium in 5 categories; 7 gaps are clear of the uncertainty.
  • The widest gap is in knowledge, where GLM-4.6V leads 37.4 to 24.9.
  • GLM-4.6V is cheaper at $0.30 / $0.90 per million input/output tokens, against $1.50 / $7.50 for Mistral Medium.
  • Mistral Medium accepts more context: 262K tokens versus 128K.

Side by side

GLM-4.6VMistral Medium
ProviderZ.ai (Zhipu)Mistral AI
Noometry Index40.936.0
Released2025-12-082023-12-11
WeightsOpenOpen
Context window128K262K
Max output33K262K
Input $ / M tokens$0.30$1.50
Output $ / M tokens$0.90$7.50
Results tracked1236

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Category by category

Coding GLM-4.6V leads

GLM-4.6V: 40.2 (#128), Mistral Medium: 33.8 (#241)

BenchmarkGLM-4.6VMistral Medium
LMArena Coding13901434
FrontierCode—8%
SciCode—40.2%
WeirdML—43.7%
ALE-Bench—763.98

Agentic & Tool Use Not comparable

GLM-4.6V: —, Mistral Medium: 28.1 (#84)

BenchmarkGLM-4.6VMistral Medium
Berkeley Function Calling Leaderboard—37.7%

Reasoning GLM-4.6V leads

GLM-4.6V: 26.5 (#117), Mistral Medium: 23.3 (#165)

BenchmarkGLM-4.6VMistral Medium
LMArena Hard Prompts13681426
Kagi LLM Benchmark—50%
CritPt—0%
DTBench—75.5%
LMCA—26.1%
Surface Evolver Bench—26.9%

Math Not comparable

GLM-4.6V: —, Mistral Medium: 27.2 (#238)

BenchmarkGLM-4.6VMistral Medium
OTIS Mock AIME 2024-2025—32.2%
ProofBench—9%
LMArena Math—1409
MATH Level 5—81.6%
FrontierMath (Feb 2025 set)—0.3%

Knowledge GLM-4.6V leads

GLM-4.6V: 37.4 (#148), Mistral Medium: 24.9 (#260)

BenchmarkGLM-4.6VMistral Medium
LMArena Expert13721408
GPQA Diamond—59.5%
Humanity's Last Exam—4.5%
Vectara Hallucination Rate—22.7%

Multimodal Too close to call

GLM-4.6V: 34.2 (#83), Mistral Medium: 34.6 (#82)

BenchmarkGLM-4.6VMistral Medium
LMArena Vision11641172

Multilingual Mistral Medium leads

GLM-4.6V: 48.8 (#135), Mistral Medium: 52.3 (#86)

BenchmarkGLM-4.6VMistral Medium
LMArena Non-English13591408
LMArena Chinese14261448
LMArena Russian13411411
LMArena French—1460
LMArena German—1433
LMArena Japanese—1378
LMArena Korean—1382
LMArena Spanish—1433

Instruction Following Mistral Medium leads

GLM-4.6V: 71.6 (#145), Mistral Medium: 73.9 (#111)

BenchmarkGLM-4.6VMistral Medium
LMArena Instruction Following13511398

Long Context Mistral Medium leads

GLM-4.6V: 41.3 (#138), Mistral Medium: 42.9 (#108)

BenchmarkGLM-4.6VMistral Medium
LMArena Longer Query13581406

Writing & Preference Mistral Medium leads

GLM-4.6V: 56.8 (#132), Mistral Medium: 60.2 (#97)

BenchmarkGLM-4.6VMistral Medium
LMArena Text13771424
LMArena Creative Writing13471391
LMArena Multi-Turn13591418
Short-Story Creative Writing—77.3%

Frequently asked questions

Is GLM-4.6V better than Mistral Medium?

GLM-4.6V is the stronger model overall, scoring 40.9 to 36.0 on the Noometry Index.

Which is cheaper, GLM-4.6V or Mistral Medium?

GLM-4.6V is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; Mistral Medium lists at $1.50 and $7.50.

Is GLM-4.6V or Mistral Medium better for coding?

GLM-4.6V scores higher on coding benchmarks: 40.2 versus 33.8 in the Noometry coding category.

Which has the bigger context window?

Mistral Medium does, with 262K tokens against 128K.

How many benchmarks do GLM-4.6V and Mistral Medium share?

12 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and Mistral Medium has 36.

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