Is qwen3.8:latest good for coding?

In our benchmarks, qwen3.8:latest scores 76.5/100 for coding. It runs at about 39.7 tok/s, so if you want more speed, ornith-1.5-35b-a3b-apex-i-quality is faster (~75.9 tok/s) and still scores well for coding (82.9/100).

Is qwen3.8:latest good for agentic (tool-using) tasks?

In our benchmarks, qwen3.8:latest scores 82.1/100 for agentic workflows. It runs at about 39.7 tok/s, so if you want more speed, Nex-N2.5-mini-APEX-Mini is faster (~88.2 tok/s) and still scores well for agentic workflows (91.3/100).

How fast is qwen3.8:latest for local inference?

Across 12 community benchmark runs, qwen3.8:latest reaches up to 80.5 tok/s and averages 39.7 tok/s, with the fastest results on AMD Radeon RX 7900 XTX.

How much memory does qwen3.8:latest need?

The leanest observed configuration used about 16.1 GB of memory (quantizations tested: Q4_K_M).

Which tools have been used to run qwen3.8:latest?

Benchmarks were submitted using Ollama. Results are community-contributed and updated as new runs arrive.

Model benchmarks

qwen3.8:latest local LLM performance

As of November 2026, qwen3.8:latest runs at up to 80.5 tok/s for local inference (best of 12 community benchmark runs across 4 GPUs).

OllamaQ4_K_M

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Model size

27.3B

Peak speed

80.5 tok/s

Average speed

39.7 tok/s

Avg PP

619.9 tok/s

Min memory

16.1 GB

Max context

163,840 tokens

Avg output / run

16,032 tokens

Avg runtime / run

11m 3s

Avg quality

82.7

Benchmark runs

GPUs tested

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 12 runs. The low and high columns show how much the judge’s score varies between runs, and need at least two runs to display.

Task	P5 (low)	Avg	P95 (high)
Overall	80.1	82.7	85.6
Agent Workflow	72.6	82.1	89.0
Code Generation	68.8	76.5	80.8
Role Play & Narrative	76.3	86.4	92.7
Research & Analysis	82.6	85.8	89.2

Performance by hardware and tool

Every hardware/tool/quantization combination qwen3.8:latest has been benchmarked on, ranked by peak token generation speed. Last updated November 2026.

Hardware	Tool	Quant	Best tok/s	Avg tok/s	Memory	Context	Quality	Runs
AMD Radeon RX 7900 XTX	Ollama	Q4_K_M	80.5 tok/s	44.9 tok/s	16.1 GB	163,840 tokens	82.7	9
NVIDIA GeForce RTX 5060 Ti	Ollama	Q4_K_M	42.0 tok/s	42.0 tok/s	17.1 GB	8,192 tokens	81.6	1
NVIDIA GeForce RTX 5080	Ollama	Q4_K_M	23.6 tok/s	23.6 tok/s	24.2 GB	8,192 tokens	82.4	1
NVIDIA GeForce RTX 4070 Ti	Ollama	Q4_K_M	6.4 tok/s	6.4 tok/s	20.2 GB	65,536 tokens	84.6	1

Benchmark runs

All 12 qwen3.8:latest runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.

Open run page →

Frequently asked questions

Is qwen3.8:latest good for coding?: In our benchmarks, qwen3.8:latest scores 76.5/100 for coding. It runs at about 39.7 tok/s, so if you want more speed, ornith-1.5-35b-a3b-apex-i-quality is faster (~75.9 tok/s) and still scores well for coding (82.9/100).
Is qwen3.8:latest good for agentic (tool-using) tasks?: In our benchmarks, qwen3.8:latest scores 82.1/100 for agentic workflows. It runs at about 39.7 tok/s, so if you want more speed, Nex-N2.5-mini-APEX-Mini is faster (~88.2 tok/s) and still scores well for agentic workflows (91.3/100).
How fast is qwen3.8:latest for local inference?: Across 12 community benchmark runs, qwen3.8:latest reaches up to 80.5 tok/s and averages 39.7 tok/s, with the fastest results on AMD Radeon RX 7900 XTX.
How much memory does qwen3.8:latest need?: The leanest observed configuration used about 16.1 GB of memory (quantizations tested: Q4_K_M).
Which tools have been used to run qwen3.8:latest?: Benchmarks were submitted using Ollama. Results are community-contributed and updated as new runs arrive.