Qwen3.8 27B
A solid pick for self-hosting and full data control.
Overview
Qwen3.8-27B is a dense 27-billion-parameter model (28B with its vision encoder), the successor to Qwen3.6-27B, built for agentic coding, tool use and long-horizon autonomous tasks, with a native 262,144-token context extendable to 1M via YaRN. The headline numbers on Terminal-Bench, SWE-bench Pro and OSWorld come solely from Alibaba, with no independent verification so far. Released under Apache 2.0, its open weights let you self-host on your own infrastructure - data never leaves it - and the Q4 quantised build runs on a single desktop workstation.
Skill profile
Not disclosed
Three independent measures
What people prefer, what the model can do, what the market uses. Each source has its biases and none is enough on its own: we show them side by side, never blended.
Strengths
- Open-source and self-hostable
Limitations
- API pricing not disclosed
Who is it for
- you build with a coding agent
- you want to control cost or self-host
- your data must stay in the EU
Ideal use cases
- Autonomous coding agents
- Office and document tasks
- Image and long-video analysis
- Secure on-premise development
Access & availability
Key specifications
What it costs per month
Estimate from sourced API prices and a hardware tier for self-hosting. Assumptions are editable.
Assumptions: 36-month amortisation, 0.25 €/kWh, 22 days/month, 1 $ = 0.92 €. Excludes labour, software licences, redundancy.
nAIvigate Trust Index
What the vendor offers and documents for professional use in Europe. An undocumented criterion counts as zero and stays visible: the score rises when the vendor publishes, not when we assume.
19/19 criteria documented · 100 % · 12 of them set by rule
See the criteria▾
Trust Index badge
Show this score in your docs, README or security page. It updates automatically.
[](https://naivigate.com/en/model/qwen3-8-27b)Data & compliance
What we know, line by line, with source and date. These lines feed the Data and Enterprise families of the Trust Index above. GDPR compliance depends on your processing, not on the model alone.
| EU data residency | Yes |
| Data processing agreement (DPA) | N/A· self-hosted: data never leaves |
| Transfer outside the EU | No |
| Training on your data | No |
| Configurable retention | N/A· self-hosted: data never leaves |
| Zero Data Retention | N/A· self-hosted: data never leaves |
| Self-hostable | Yes |
| Declared hosting | self |
Quality of this card’s data
This score measures the quality of the data on this card: price, context, Elo, licence, documented, sourced and fresh. It measures neither the model nor its compliance: for that, see the Trust Index above.
4/8 documented · 0/8 primary sources
| API pricingopen weights, no API pricing | missing | |
| Context window | nAIvigate assessment | |
| Arena Elo | missing | |
| Benchmarks (MMLU, GPQA, SWE-bench…) | missing | |
| Licence | nAIvigate assessment | |
| Data & complianceself-hosted: derived | derived | |
| Adoption (Hugging Face) | missing | |
| Hardware requirements (quantizations)computed from model size | derived |
Run it locally
| Quantization | Disk | RAM / VRAM | Typical hardware |
|---|---|---|---|
| Q4 · recommended | 15.4 GB | 19 GB | 32GB Mac / RTX 3090-4090 |
| Q8 · balanced | 28.9 GB | 34 GB | 64GB Mac / dual 24GB GPUs |
| FP16 · max quality | 54 GB | 61 GB | 96-128GB Mac Studio / 4× GPUs |
Estimates for a moderate context. Long contexts need more RAM (KV cache).
Deploy
Copy-ready commands generated from this card. Adjust context length and GPU count to your hardware.
OpenAI-compatible server for production on NVIDIA GPUs.
pip install vllm
vllm serve Qwen/Qwen3.8-27B \
--max-model-len 32768 \
--tensor-parallel-size 1 \
--dtype autoAdvanced data · for expertsArchitecture, modalities, detailed cost, full benchmarks▾
| Arena Elo | — |
| MMLU | — |
| GPQA | — |
| HumanEval | — |
| SWE-Bench | — |
| MATH | — |