ministral-3:8b

mistral3 · 8.9B · Q4_K_M

MacBook Air (Apple M4)

32 GB · macOS 26.3

Tested on March 6, 2026

Top 29% Compare

Global Score

76 /100

Good

Hardware Fit

89/100

Quality

71/100

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Ollama

ollama pull ministral-3:8b

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ollama.com/library/ministral-3

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GGUF versions & quantizations

Hardware

Machine: MacBook Air
CPU: Apple M4
Cores: 10 total (4 perf + 6 eff)
Frequency: 2.4 GHz
RAM: 32 GB LPDDR5
GPU: Apple M4
OS: macOS 26.3
Arch: arm64
Power Mode: balanced

Performance

Tokens/sec Tokens generated per second — higher is better: 19.5
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±0.1
First chunk latency Delay before the first streamed chunk arrives from runtime: 328 ms
Time to first token How fast the model starts responding — lower is better: 328 ms
Load time Time to load the model into memory before inference: 1.5 s
Memory usage RAM consumed during inference — percentage of total system memory: 10.6 GB (33%)
Total tokens Total number of tokens generated across all benchmark prompts: 3879

Score breakdown

Speed

39/50

Time to first token

20/20

Memory

30/30

Quality

Reasoning

12/20

Coding

18/20

Instruction following

12/20

Structured output

13/15

Math

6/15

Multilingual

10/10

Category levels

Reasoning: Adequate Coding: Strong Instruction Following: Adequate Structured Output: Strong Math: Weak Multilingual: Strong

Metadata

Spec version: 0.2.1
Runtime: Ollama 0.17.6
Model format: GGUF
Hardware profile: BALANCED
Result hash: ac5a61d477fdca1daf0839aec12515d82926cea35cc985d26b5bddf4acbc7003

Interpretation

Hardware fit: 89/100. Overall suitability: GOOD (Global 76/100). Category profile: Reasoning: Adequate, Coding: Strong, Instruction Following: Adequate, Structured Output: Strong, Math: Weak, Multilingual: Strong.

Bench Environment

Power: AC CPU load: avg 7% (peak 11%)

Run yours now

$ npm install -g metrillm@latest

$ metrillm

Requires Node 20+ and Ollama or LM Studio running

Or run without installing: npx metrillm@latest