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Model details

Qwen3.7 Plus

Qwen3.7 Plus sits in Alibaba's Qwen3.7 family as the vision-enabled sibling of the flagship text model. Built on top of the Qwen3.7 text backbone, it adds native image and video perception so that vision and language are processed jointly from the earliest layers of the network, rather than as a bolt-on captioning module. The result is a perception-only model: it ingests text, images, and short video clips, and replies in text, making it well suited to understanding screens, reasoning about scenes, reading diagrams, and turning visual references such as mockups or screenshots into executable code. Its design intent is what Alibaba calls a multimodal interactive hybrid agent, meaning it can ground itself in graphical interfaces, navigate mobile apps end to end, and combine that perception with coding, reasoning, and tool-calling skills inherited from the broader Qwen3.7 line.

Because Qwen3.7 Plus extends an existing text backbone rather than standing alone, it carries forward the family strengths in coding, long-running reasoning, and structured tool use, while expanding the input surface to include video and image tokens alongside text. Early-fusion training means vision and language representations are learned together, which is what enables GUI grounding and direct code generation from visual references instead of relying on a separate OCR step. Practically, the model is positioned for agentic productivity work: reading a UI, clicking through it, answering questions about a short clip, and writing the code that ties those actions together, all within an extended context window that supports long documents and multi-step tasks. It is best understood as the perception half of a larger agent stack, where it handles the seeing and reasoning while other Alibaba models handle pure text or generative media workloads.

Alibaba Token Plan (China)qwen3.7-plusqwen

Quick Info

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Provider
Alibaba Token Plan (China)
Model key
qwen3.7-plus
Release date
Jun 2, 2026
Last updated
Jun 2, 2026
Knowledge cutoff
2025-04
Input modalities
Output modalities
Capabilities

Cost

A provider subscription or plan supersedes token-based pricing for this model.

Limits

Output tokens
65,536 tokens
Context window
1,000,000 tokens

Latest news about Qwen3.7 Plus

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Coverage

MLQ News reports that Alibaba's Qwen team released Qwen3.7-Plus on June 2 as a multimodal agent model that accepts text, images, and video as input but outputs only text, combining visual perception, GUI control, and code generation in a single autonomous loop. The article claims the model leads Alibaba's results on Sc The report notes Alibaba's Hong Kong-listed shares rose as much as 6.84% on the announcement before giving back gains. It frames Qwen3.7-Plus as a "perception and action" model designed to read screens, navigate apps, write code from visual templates, and invoke external tools without human intervention, though it trai

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CoverageBenchmark

The BenchLM.ai model page for Qwen3.7 Plus provides third-party benchmark aggregation covering Agentic (rank 133 of 154, 36.7 score), Coding (rank 48 of 154, 52.6 score), Reasoning (74.0), Knowledge (rank 49 of 184, 56.2 score), and Math (78.2) categories, with all listed rows verified against published sources. The pa Multilingual performance is highlighted as a strong point with rank 3 in that category, and the page presents 52 published benchmark rows overall as a decision snapshot for prospective users. Coverage is split across 11 Agentic, 7 Coding, 2 Reasoning, 7 Knowledge, 3 Math, 5 Multilingual, 15 Multimodal, and 2 Instructio

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CoverageBenchmark

The llm-stats.com Qwen3.7-Plus page places the model at rank 49 overall in its composite LLM Stats Score, with capability tiers noting top-2% standing in Legal (2 of 211) and Healthcare (2 of 245), and top-10% in Chat (7 of 133), Long Context (9 of 119), and Math (18 of 327). Vision (22 of 210), Reasoning (43 of 366), Headline benchmark scores include IFEval 0.95 (rank 2), MMLU-Redux 0.94 (rank 3), HMMT Feb 26 0.93 (rank 4), and MRCR v2 at 128k context scoring 0.92 (rank 1), with 66 additional benchmark entries listed in the scorecard drawn from qwen.ai and other published sources. The Quality Tracker shows category-specific sigma d

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