Is qwen38-27b good for coding?

In our benchmarks, qwen38-27b scores 67.5/100 for coding. It runs at about 72.7 tok/s, so if you want more speed, ornith-ai/Ornith-1.5-35B-A3B-GGUF is faster (~90.8 tok/s) and still scores well for coding (83.8/100).

Is qwen38-27b good for agentic (tool-using) tasks?

In our benchmarks, qwen38-27b scores 77.3/100 for agentic workflows. It runs at about 72.7 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 qwen38-27b for local inference?

Across 6 community benchmark runs, qwen38-27b reaches up to 78.6 tok/s and averages 72.7 tok/s, with the fastest results on AMD Radeon RX 7900 XTX.

Which tools have been used to run qwen38-27b?

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

Model benchmarks

qwen38-27b local LLM performance

As of September 2026, qwen38-27b runs at up to 78.6 tok/s for local inference (best of 6 community benchmark runs across 1 GPU).

llama.cpp

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Model size

27B

Peak speed

78.6 tok/s

Average speed

72.7 tok/s

Avg PP

543.7 tok/s

Min memory

n/a

Max context

131,072 tokens

Avg output / run

29,157 tokens

Avg runtime / run

6m 55s

Avg quality

79.6

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 6 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	75.8	79.6	85.0
Agent Workflow	64.4	77.3	88.7
Code Generation	45.3	67.5	78.2
Role Play & Narrative	78.4	88.7	96.2
Research & Analysis	81.1	85.0	88.8

Performance by hardware and tool

Every hardware/tool/quantization combination qwen38-27b has been benchmarked on, ranked by peak token generation speed. Last updated September 2026.

Hardware	Tool	Quant	Best tok/s	Avg tok/s	Memory	Context	Quality	Runs
AMD Radeon RX 7900 XTX	llama.cpp	—	78.6 tok/s	72.7 tok/s	n/a	131,072 tokens	79.6	6

Benchmark runs

All 6 qwen38-27b runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.

Open run page →

Frequently asked questions

Is qwen38-27b good for coding?: In our benchmarks, qwen38-27b scores 67.5/100 for coding. It runs at about 72.7 tok/s, so if you want more speed, ornith-ai/Ornith-1.5-35B-A3B-GGUF is faster (~90.8 tok/s) and still scores well for coding (83.8/100).
Is qwen38-27b good for agentic (tool-using) tasks?: In our benchmarks, qwen38-27b scores 77.3/100 for agentic workflows. It runs at about 72.7 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 qwen38-27b for local inference?: Across 6 community benchmark runs, qwen38-27b reaches up to 78.6 tok/s and averages 72.7 tok/s, with the fastest results on AMD Radeon RX 7900 XTX.
Which tools have been used to run qwen38-27b?: Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.