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Model Detail

Qwen logo

Qwen3.8-27B

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Provider: QwenCategory: multimodalPipeline: image-text-to-textParameters: 27B
DB Score
53.4
Downloads
6.0M
Likes
14K
Day
+0.0%
Week
+0.0%
Month
+0.0%
Overview

Qwen3.8-27B is a multimodal model with 27B parameters released by Qwen. The model is registered under the image-text-to-text pipeline tag on Hugging Face, and supports text+image+video->text inputs, distributed under the permissive apache-2.0 license.

Pricing & Throughput

Qwen3.8-27B is priced at $0.4/M input tokens and $2.55/M output tokens. Operationally the model offers a 1000K-token context window, which matters when sizing it for prompt-heavy or latency-sensitive workloads. At this input rate the model sits in the commodity tier and is suitable for high-volume workloads where per-call cost dominates the decision.

Technical

Qwen3.8-27B ships with 27B parameters. Total weight footprint is approximately 27.8 GB, which is the relevant figure when planning local-inference VRAM. The apache-2.0 license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.

Use Cases

Qwen3.8-27B is best fit for mixed text-and-image reasoning tasks such as document understanding, high-volume batch jobs where per-call cost dominates the budget, and long-context tasks such as full-codebase analysis or book-length summarization (1000K tokens). Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.

Download History
Pricing
Input ($/M tokens)
$0.4
Output ($/M tokens)
$2.55
Context Window
1000K
Model Info
Licenseapache-2.0
Modalitytext+image+video->text
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Related News
arxiv4d ago

On the Design of Qwen3.8-Next Architecture: Evaluation, Efficiency, and Training Stability

arXiv:2608.30320v1 Announce Type: new Abstract: We describe the architecture and ablations of Qwen3.8-Flash-Next, a sparse mixture-of-experts model with 125B parameters, 6B activated per token, and additional 51B parameters of n-gram embedding tables held off the accelerator. On fourteen pre-trainin

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