ministral-3:8b

mistral3 · 8.9B · Q4_K_M

Mac mini (Apple M4 Pro)

24 GB · macOS 15.6.1

Tested on June 25, 2026 · Submitted by Tram

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Global Score

79 /100

Good

Hardware Fit

98/100

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: Mac mini
CPU: Apple M4 Pro
Cores: 12 total (8 perf + 4 eff)
Frequency: 2.4 GHz
RAM: 24 GB LPDDR5
GPU: Apple M4 Pro
OS: macOS 15.6.1
Arch: arm64
Power Mode: balanced

Performance

Tokens/sec Tokens generated per second — higher is better: 41.4
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±0.2
First chunk latency Delay before the first streamed chunk arrives from runtime: 257 ms
Time to first token How fast the model starts responding — lower is better: 257 ms
Load time Time to load the model into memory before inference: 5.2 s
Memory usage RAM consumed during inference — percentage of total system memory: 9.0 GB (38%)
Total tokens Total number of tokens generated across all benchmark prompts: 3908

Score breakdown

Speed

50/50

Time to first token

20/20

Memory

28/30

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 0.30.10
Model format: GGUF
Hardware profile: BALANCED
Result hash: 884943b73b067a436be9acb861608618e08e36d2acf507bdf56b79b49d5274ac

Interpretation

Hardware fit: 98/100. Overall suitability: GOOD (Global 79/100). 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 10%)

Run yours now

$ npm install -g metrillm@latest

$ metrillm

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Or run without installing: npx metrillm@latest