Qwen3.8-27B Is Now Available on DeepInfra

DeepInfra has introduced Qwen3.8-27B, a 27-billion-parameter open-weight vision-language model designed for software engineering, agentic workflows, long-context reasoning, and multimodal inputs including images, documents, and video. Its hybrid 64-layer architecture combines Gated DeltaNet linear attention with standard gated attention, supporting a native 262,144-token context window that can extend to one million tokens through YaRN scaling. DeepInfra reports substantial benchmark improvements over earlier Qwen models, including scores of 61.7 on SWE-bench Pro, 79.0 on QwenSWEBench, and 84.3 on OSWorld-Verified, with results presented as competitive with or exceeding some larger closed models on coding, visual, and GUI-agent tasks. The model provides adjustable reasoning controls, allowing users to toggle thinking mode, set reasoning effort, and preserve prior reasoning context to balance response quality and latency. Available through DeepInfra’s OpenAI-compatible API, it supports function calling, JSON output, multimodal inputs, and private deployments, with token-based pricing, zero data retention, and SOC 2 and ISO 27001 certifications.

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