qwen3.5:4b

qwen35 · 4.7B · Q4_K_M

MacBook Pro (Apple M1 Pro)

16 GB · macOS 26.6.2

Tested on August 25, 2026 · Submitted by AAA

Top 29% Compare

Global Score

83 /100

Excellent

Hardware Fit

99/100

Quality

76/100

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Ollama

ollama pull qwen3.5:4b

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ollama.com/library/qwen3.5

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GGUF versions & quantizations

Hardware

Machine: MacBook Pro
CPU: Apple M1 Pro
Cores: 10 total (8 perf + 2 eff)
Frequency: 2.4 GHz
RAM: 16 GB LPDDR5
GPU: Apple M1 Pro
OS: macOS 26.6.2
Arch: arm64
Power Mode: balanced

Performance

Tokens/sec Tokens generated per second — higher is better: 36.6
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: 204 ms
Time to first token How fast the model starts responding — lower is better: 204 ms
Load time Time to load the model into memory before inference: 2.7 s
Memory usage RAM consumed during inference — percentage of total system memory: 5.3 GB (33%)
Total tokens Total number of tokens generated across all benchmark prompts: 1078

Score breakdown

Speed

50/50

Time to first token

20/20

Memory

29/30

Quality

Reasoning

16/20

Coding

14/20

Instruction following

15/20

Structured output

15/15

Math

7/15

Multilingual

9/10

Category levels

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

Metadata

Spec version: 0.2.1
Runtime: Ollama 0.32.15
Model format: GGUF
Hardware profile: ENTRY
Result hash: 84c87434438952535430d3c29baee999d79a1c9effc88dd8419aeff0cf98776f

Interpretation

Hardware fit: 99/100. Overall suitability: EXCELLENT (Global 83/100). Category profile: Reasoning: Strong, Coding: Adequate, Instruction Following: Strong, Structured Output: Strong, Math: Weak, Multilingual: Strong.

Bench Environment

Power: AC CPU load: avg 36% (peak 49%)

Run yours now

$ npm install -g metrillm@latest

$ metrillm

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Or run without installing: npx metrillm@latest