ministral-3:3b

mistral3 · 3.8B · Q4_K_M

MacBook Air (Apple M4)

32 GB · macOS 26.3

Tested on March 6, 2026

Top 42% Compare

Global Score

71 /100

Good

Hardware Fit

99/100

Quality

59/100

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Ollama

ollama pull ministral-3:3b

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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: 42.2
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: 189 ms
Time to first token How fast the model starts responding — lower is better: 189 ms
Load time Time to load the model into memory before inference: 1.2 s
Memory usage RAM consumed during inference — percentage of total system memory: 7.0 GB (22%)
Total tokens Total number of tokens generated across all benchmark prompts: 3859

Score breakdown

Speed

50/50

Time to first token

20/20

Memory

29/30

Quality

Reasoning

5/20

Coding

16/20

Instruction following

11/20

Structured output

14/15

Math

4/15

Multilingual

9/10

Category levels

Reasoning: Weak 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: 814b0d2a0d6652740fd7d8937bad76058bdc23df6a752e2ad39b69df6b49e0d0

Interpretation

Hardware fit: 99/100. Overall suitability: GOOD (Global 71/100). Category profile: Reasoning: Weak, Coding: Strong, Instruction Following: Adequate, Structured Output: Strong, Math: Weak, Multilingual: Strong.

Bench Environment

Power: AC CPU load: avg 5% (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