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Model details

Qwen3.8 27B

Qwen3.8 27B is an open-weight dense vision-language model released as part of the Qwen 3.8 family, with the broader family planned to be published as open weights according to an August 2026 announcement. Its model weights are hosted on Hugging Face under the Qwen organization, making it available for self-hosting and downstream fine-tuning rather than being locked behind a closed API. The "27B" designation indicates a dense transformer in the tens-of-billions-of-parameters range, positioned as a mid-sized option within the 3.8 lineup that balances capability with deployability on accessible hardware.

The model is positioned for coding, professional workflows, research, multimodal interaction, and long-running agent tasks, and includes a flexible thinking mode that can be toggled on or off depending on whether step-by-step reasoning is desired. As a vision-language model, it accepts image inputs alongside text and produces text outputs, which broadens its usefulness to document understanding, visual question answering, and grounded agent workflows that combine screen or image content with natural language instructions. For practitioners choosing a deployment target, the model's open-weight availability and multimodal support make it a practical fit for teams that want to run inference locally or on private infrastructure while still benefiting from a recent general-purpose Qwen release.

Berget.AIQwen/Qwen3.8-27B-FP8qwen

Quick Info

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Provider
Berget.AI
Model key
Qwen/Qwen3.8-27B-FP8
Release date
Aug 14, 2026
Last updated
Sep 1, 2026
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$0.46
Output token cost
$3.48

Limits

Output tokens
65,536 tokens
Context window
262,144 tokens

Transparent token rates

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Rates are shown per one million tokens. Combined means one million input plus one million output tokens.

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Latest news about Qwen3.8 27B

Berget.AI

CoverageBenchmark

Qubrid AI's August 31, 2026 benchmark roundup reports Qwen3.8-27B scoring 52 on the Artificial Analysis Intelligence Index at maximum reasoning effort and 61.7 on SWE-bench Pro per Qwen's own evaluation. The model leads its comparison set on agentic coding and computer-use benchmarks while trailing frontier models on H Qubrid highlights the unusually large agentic-coding jump over Qwen3.6-27B (DeepSWE 1.1 from 13.3 to 42.2, QwenSWEBench from 49.3 to 79.0) and notes that the 27B beats closed-weight Qwen3.7-Plus on most agentic and coding rows while trailing on Humanity's Last Exam and GPQA Diamond. Vision-language benchmarks from the

Berget.AI

Coverage

Rost Glukhov wrote on Medium on August 5, 2026 that Alibaba's announcement of open-weight releases for Qwen3.8-Max and Qwen3.8-27B was the most significant local-AI news of the week, with weights subsequently published on Hugging Face and ModelScope under Qwen/Qwen3.8-27B and Qwen/Qwen3.8-2.4T-A95B. The article frames The piece highlights the open-weight rollout as the real story for the local-AI community, emphasizing that Qwen3.8-Max is the technological spectacle but Qwen3.8-27B is the model many practitioners will actually run, with both checkpoints now available for download. Glukhov describes the 27B variant as the open-weight

Berget.AI

Coverage

DEV Community published a beginner's guide to Qwen3.8-27B on August 15, 2026, summarizing the model's architecture as a 27-billion-parameter dense causal language model with an integrated vision component built on the Qwen3.5 foundation, supporting native image and video understanding. The guide specifies 64 layers, a The guide highlights that Qwen3.8-27B ships in thinking mode by default, which generates explicit reasoning chains and adds token overhead, so users must configure parameters to disable it for cost-efficient direct responses. Reported benchmarks include 61.7% on SWE-bench Pro, 73.0% on Terminal-Bench 2.1, 42.2% on Deep

Berget.AI

CoverageBenchmark

A New Stack article dated August 14, 2026, titled "Alibaba's new model promises Opus 4.6-level performance on your laptop," covers the launch of Qwen3.8-27B as a local-inference-oriented release positioned to deliver near-frontier coding and reasoning quality on consumer hardware. The article frames the model as enabli The New Stack piece emphasizes the practical implications for developers running inference locally rather than via cloud APIs, highlighting that Qwen3.8-27B's size and open-weight availability (under Apache 2.0) make it directly deployable on a single high-end workstation. This aligns with broader coverage of the relea

Berget.AI

CoverageBenchmark

Alibaba's Qwen team released Qwen3.8-27B on August 14, 2026 at 15:00 UTC. According to the Kingy.ai launch-day review, the checkpoint contains 27.78 billion parameters, accepts text, images and video, ships under Apache 2.0, and has a native 262,144-token context window. The official model card reports large improvemen Kingy.ai examined the model card, configuration, license, repository metadata, official BF16 and FP8 artifacts, and third-party GGUF file inventory on launch day, recalculating benchmark deltas and memory lower bounds without running inference because the managed Qwen Cloud endpoint was marked "coming soon" and a 55.6G

Berget.AI

Coverage

A NVIDIA developer-forum thread dated August 8, 2026 previewed Qwen3.8-27B as an upcoming open-weights release, citing an announcement originally made on LinkedIn and pointing to the Qwen blog at qwen.ai/blog?id=qwen3.8. The thread, posted by user "wentbackward," framed the 27B as more relevant to the DGX Spark / GB10 The same NVIDIA forum index surfaces multiple follow-on Qwen3.8-27B threads showing rapid community work on quantization and serving variants shortly after launch. Qwen3.8-Flash-Next, a sibling 3.8-family open-weight model, drew 31.9k views and 286 replies, and a technical blog entry documented running a 176B Qwen3.8-F

Berget.AI

CoverageAnalysis

Local AI Zone published a paper-level technical analysis of Qwen3.8-27B, released by Alibaba's Tongyi Lab on August 14, 2026 under Apache 2.0, characterizing the checkpoint as a 27.78-billion-parameter dense multimodal model (27,781,427,952 parameters) with a native 256K-token context extended to 1M tokens via YaRN, a The piece details architectural innovations including multi-token prediction (MTP) for speculative decoding, an attention output gate, and a fused q_proj of 12,288 by 5,120 dimensions, noting that these design choices reduce computational complexity from O(n²) to O(n) on most layers. It positions the model as a milesto

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