QwenQwen 3.8 27B
Qwen 3.8 27B is an open-source, dense vision-language chat model developed by Qwen, capable of processing both text and image inputs. It is designed for complex, multi-step agentic tasks and features a 1,000,000 token context window.
Input
Output
Context
1M
Max Output
66K
Parameters
27.8B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Benchmarks
Resources & Links
Estimated GPU requirements for Qwen 3.8 27B
Estimates based on INT8 quantization at up to 32K context. A count above one assumes tensor parallelism across the cards. Actual requirements vary by framework and configuration.
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Frequently asked questions about Qwen 3.8 27B
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Qwen 3.8 27B advertises a context window of 1,000,000 tokens, with a maximum output of 65,536 tokens in a single response. The figure is the creator's published maximum; a given provider may serve less, and the gateway routes on what each provider actually serves.
Yes. Qwen 3.8 27B is an open-weight model released under the Apache 2.0 license, so the weights can be downloaded and self-hosted within that license's terms.
Qwen 3.8 27B accepts Text and Image and produces Text. The capabilities card on this page lists which API features each deployment honours, such as function calling and structured output, with the source each was checked against.
Qwen 3.8 27B has published results from Artificial Analysis, shown by suite in the benchmarks card above exactly as the publisher reported them. Scores are not combined across suites, and a suite that has not measured Qwen 3.8 27B is shown as not available rather than estimated.
Not on the managed pool today; Qwen 3.8 27B is listed for reference and comparison. Connect your own provider key or endpoint that serves it and the gateway runs it on your account, with routing decisions recorded the same way.
About 27.8 GB at INT8 for the weights and a default context, from the catalog's 27.8B parameter count; FP16 needs roughly twice that, and long contexts or many concurrent requests add KV cache on top. The GPU section on this page lists cards that hold it, and the capacity planner sizes it for your context length and traffic.
Fields collected from public registries, provider APIs and benchmark publishers, each tagged with its source.
Last updated: Nov 6, 2026
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