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Model garden Router · warm Dedicated · available

Qwen3.8 27B

Instantly via the EU router or as a dedicated GPU deployment. Data stays in Europe.

[!Note] This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, TokenSpeed, etc.

Start for free ← All models
Qwen/Qwen3.8-27B
text+image->text · Qwen · sovereign EU
Runs on EU infrastructure operated by European companies; US marketplace capacity is never part of this chain. Full chain
28B
Parameters
262K
Context window
80GB
Minimum VRAM
POST /api/v1/chat/completions 200 OK

Specifications

Parameters 28B
Context window 262,144 tokens
Minimum VRAM 80 GB
Architecture Qwen3_5ForConditionalGeneration (vLLM)
License apache-2.0
Modality text+image->text
Released August 2026
Publisher Qwen ↗

Pricing

Shared router · per token
€0.35
Input (per 1M tokens)
€2.53
Output (per 1M tokens)
€0.12 per million cached input tokens
Dedicated GPU · per hour
from €2,56 per hour
Your own vLLM instance on European cloud (80 GB VRAM), billed hourly.

Shared EU router, pay-per-token, scale-to-zero. Dedicated GPU deployments are billed hourly, see pricing.

Bench index

Bench is our quality index for every model, 0 to 100. It rests on public measurements (Epoch AI with about 40 benchmarks, LiveBench, LMArena and more) and is refreshed every morning. Where nobody measures, it shows an estimate with a range.

74.2 / 100 · Measured

Measured by Epoch AI across 4 benchmarks, with a range.

Range 71.6 to 76.4 · Rank 58 of 274 at Epoch AI

Sources: Epoch AI ↗ · LiveBench ↗ · LMArena ↗ · Refreshed daily, last on 08-10-2026

LiveBench · 75.3 · Rank 38 of 69
Reasoning 80.0
Coding 75.7
Agentic coding 61.4
Mathematics 86.2
Data analysis 76.6
Language 74.4
Instruction following 72.7
Every benchmark (4)
DTBench 80.0
LMCA 48.7
Surface Evolver Bench 45.0
ProofBench 16.0
LiveBench · All tasks
Reasoning
theory of mind 59.6
zebra puzzle 96.5
spatial 96.0
logic with navigation 68.0
Coding
code generation 77.5
code completion 73.9
Agentic coding
javascript 59.1
typescript 50.0
python 75.0
Mathematics
AMPS Hard 98.0
integrals with game 65.0
math comp 94.1
olympiad 87.7
Data analysis
consecutive events 84.9
tablejoin 48.8
tablereformat 96.1
Language
connections 90.5
plot unscrambling 56.5
typos 76.0
Instruction following
paraphrase 74.9
simplify 66.7
story generation 73.1
summarize 75.9

Call it now

Drop-in replacement for OpenAI: change only the base URL and API key. The Anthropic format (/v1/messages) is supported too.

curl https://hostyourai.com/api/v1/chat/completions \
  -H "Authorization: Bearer hyai-..." \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen/Qwen3.8-27B",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Frequently asked questions

Can I run Qwen3.8 27B in the EU?

Yes. HostYourAI runs Qwen3.8 27B on GPUs in European datacenters via vLLM. Prompts and outputs never leave the EU and there is no US cloud provider in the chain.

Is hosting Qwen3.8 27B GDPR-compliant?

Yes. All processing happens inside the EU, a Data Processing Agreement (DPA) is available and the subprocessor list is public. Open-source weights also mean: no training on your data.

How much does Qwen3.8 27B cost?

Via the shared EU router you pay €0.35 per million input tokens and €2.53 per million output tokens, with no fixed costs. For high volume or isolation you can also run Qwen3.8 27B as a dedicated hourly GPU instance.

Is the API OpenAI-compatible?

Yes. You use the standard OpenAI SDKs with a custom base URL (https://hostyourai.com/api/v1). The Anthropic Messages API is supported as a drop-in as well.

More models from Qwen

Qwen Image 2.1 PE T2I

We are excited to open-source Qwen-Image-2.1, a unified text-to-image generation and image editing model in the Qwen family. With just 7B parameters in its visual generation component (32 Single-Stream DiT layers), Qwen-Image-2.1 balances generation quality, inference efficiency, and versatility.

9.4B 262K context View model →
Qwen Image 2.1

We are excited to open-source Qwen-Image-2.1, a unified text-to-image generation and image editing model in the Qwen family. With just 7B parameters in its visual generation component (32 Single-Stream DiT layers), Qwen-Image-2.1 balances generation quality, inference efficiency, and versatility.

7.1B View model →
Qwen3.8 Flash Next

[!Note] This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, TokenSpeed, etc.

180B 262K context View model →
Qwen3 ASR 0.6B hf

The Qwen3-ASR family includes Qwen3-ASR-1.7B and Qwen3-ASR-0.6B, which support language identification and ASR for 52 languages and dialects. Both leverage large-scale speech training data and the strong audio understanding capability of their foundation model, Qwen3-Omni. The 1.7B version achieves state-of-the-art performance among open-source ASR models and is competitive with the strongest proprietary commercial APIs.

0.8B 66K context View model →
Qwen3 ASR 1.7B hf

The Qwen3-ASR family includes Qwen3-ASR-1.7B and Qwen3-ASR-0.6B, which support language identification and ASR for 52 languages and dialects. Both leverage large-scale speech training data and the strong audio understanding capability of their foundation model, Qwen3-Omni. The 1.7B version achieves state-of-the-art performance among open-source ASR models and is competitive with the strongest proprietary commercial APIs.

2B 66K context View model →
Qwen3.6 27B

[!Note] This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc.

28B 262K context View model →

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