Model benchmarks

qwen3.6:35b local LLM performance

As of May 2026, qwen3.6:35b runs at up to 19.6 tok/s for local inference (best of 3 community benchmark runs across 1 GPU).

OllamaQ4_K_M
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Model size

36.0B

Peak speed

19.6 tok/s

Average speed

16.4 tok/s

Min memory

26.1 GB

Max context

65,536 tokens

Avg quality

77.5

Benchmark runs

3

GPUs tested

1

Quality by task

Average LLM-judged quality (0–100) with the run-to-run spread shown as a P5–P95 band, overall and for each benchmark task, across all 3 runs. The low and high columns show how much the judge’s score varies between runs, and need at least two runs to display.

TaskP5 (low)AvgP95 (high)
Overall77.177.577.8
Agent Workflow66.573.781.5
Code Generation68.872.775.2
Role Play & Narrative84.085.486.6
Research & Analysis75.678.480.3

Performance by hardware and tool

Every hardware/tool/quantization combination qwen3.6:35b has been benchmarked on, ranked by peak token generation speed. Last updated May 2026.

HardwareToolQuantBest tok/sAvg tok/sMemoryContextQualityRuns
Apple M5 MaxOllamaQ4_K_M19.6 tok/s16.4 tok/s26.1 GB65,536 tokens77.53

Frequently asked questions

Is qwen3.6:35b good for coding?
In our benchmarks, qwen3.6:35b scores 72.7/100 for coding. It runs at about 16.4 tok/s, so if you want more speed, Gemma4:E2B/QAT-MTP@131K is faster (~303.9 tok/s) and still scores well for coding (66.9/100).
Is qwen3.6:35b good for agentic (tool-using) tasks?
In our benchmarks, qwen3.6:35b scores 73.7/100 for agentic workflows. It runs at about 16.4 tok/s, so if you want more speed, Gemma4:E2B/QAT-MTP@131K is faster (~303.9 tok/s) and still scores well for agentic workflows (71.7/100).
How fast is qwen3.6:35b for local inference?
Across 3 community benchmark runs, qwen3.6:35b reaches up to 19.6 tok/s and averages 16.4 tok/s, with the fastest results on Apple M5 Max.
How much memory does qwen3.6:35b need?
The leanest observed configuration used about 26.1 GB of memory (quantizations tested: Q4_K_M).
Which tools have been used to run qwen3.6:35b?
Benchmarks were submitted using Ollama. Results are community-contributed and updated as new runs arrive.