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Qwen's 27.8B dense vision-language model for long-context text and image inference, with configurable reasoning depth.
| Inference Provider | Sovereignty | Runs in | In / out per 1M | Status | Pin |
|---|---|---|---|---|---|
| L3 | €0.50 / 2.10 | Online | |||
| L3 | €0.60 / 3.30 | Online | |||
| L1 | €0.15 / 0.65 | Online | |||
| L1 | €0.36 / 2.20 | Online | |||
| L1 | €0.36 / 2.18 | Online | |||
| L1 | €0.40 / 2.70 | Online | |||
| L1 | €0.40 / 2.70 | Online | |||
| L1 | €0.75 / 3.00 | Online | |||
| L0 | €0.18 / 2.27 | Online | |||
| L0 | €0.40 / 3.00 | Online |
11 use cases classified by the curator. The note says why.
Combining tool-calling with extended thinking matches the basic loop an agentic coding workflow needs. No SWE-style benchmark or context window is published to justify a higher rating.
Extended thinking supports stepwise quantitative reasoning, and tool-calling allows delegation to code or query execution. No code-execution or numerical benchmarks are published.
Documented tool-calling support covers the core requirement for function-calling applications. Parameter count and reliability metrics are unpublished, which limits confidence beyond acceptable.
Extended-thinking mode is listed, which supports decomposing multi-step tasks before acting. Planning quality is not benchmarked in the card.
Extended-thinking support suits multi-source reasoning and synthesis tasks. Context window is unspecified, so long-corpus synthesis cannot be confirmed.
Tool-calling offers a route to schema-constrained JSON output for extraction pipelines. Extraction accuracy is not benchmarked in the card.
Document OCR requires image input, and no vision capability is listed. Use a vision-enabled model or a dedicated OCR pipeline.
No vision capability is listed for this model, so image question answering is outside its documented scope. Route visual tasks to a vision-capable model.
Interpreting screenshots requires vision input, which the capability list does not include. Route these workloads to a multimodal model.
Audio input is not listed among the model's capabilities, so speech-to-text is not supported. Use a dedicated speech recognition model.
Sourced numbers, not our own evaluations. Percentiles are against the whole Stav catalogue.
Computed from aa-intelligence-index, gpqa-diamond, aa-engineering-index, aa-coding-index, livecodebench, terminal-bench-v4, aa-lcr.
curl https://api.stav.ai/v1/chat/completions \
-H "Authorization: Bearer $STAV_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen/qwen3-8-27b",
"reasoning_effort": "high",
"messages": [{"role": "user", "content": "Hello!"}]
}'→ X-Stav-Request-Id · X-Stav-Model · X-Stav-Provider → X-Stav-Sovereignty-Level — the rung the request was served at