Qwen 3.5 9B

ReasoningVisionFunction CallingWeb Searchfp8private
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Quick reference
Qwen 3.5 9B — TLDR
  • 🆕 9B dense model in Alibaba's Qwen 3.5 series
  • 🔧 Hybrid Gated DeltaNet attention architecture for efficient long context
  • 📏 262K native context per the model card, extendable toward 1M
  • 🌐 Supports 201 languages
  • 🧠 Thinking/reasoning mode for step-by-step problem solving
  • 👁️ Accepts text and image inputs
  • 🔧 Native function calling for agentic workflows
  • 🔒 Apache 2.0 licensed, open weights
💰 Pricing
$0.100 / $0.150
per 1M · input / output
📏 Context
256K tokens
📅 On Venice since
Mar 5, 2026
136 days ago
Provider

Alibaba Group is a Chinese multinational technology company founded in 1999 and headquartered in Hangzhou, Zhejiang. Originally built around e-commerce and cloud computing, Alibaba has become one of the most prolific contributors to open-weight AI research,…

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51 models on Venice
20 video · 18 text · 5 image · 4 inpaint · 2 embedding · 2 tts
Since Jan 11, 2025

About this model

Qwen 3.5 9B is a compact model from Alibaba's Qwen team, part of the Qwen 3.5 series and published under the Apache 2.0 license in early 2026. It is built around a hybrid architecture that pairs Gated DeltaNet linear attention with full-attention layers, a design aimed at efficient processing across very long inputs. The model card lists a 262K native context window, extendable toward roughly 1M tokens, alongside support for 201 languages.

Within the same family, Qwen 3.5 9B sits below the mixture-of-experts releases Qwen 3.5 35B A3B and Qwen 3.5 397B, offering a small dense alternative for users who prefer a single-path model. Compared with the earlier dense Qwen 3.6 27B and prior Qwen generations, the 9B variant trades raw scale for a lighter footprint while retaining the series' core features.

Functionally, the model includes a thinking mode for explicit reasoning traces, native function calling for tool use and agentic pipelines, and image input support. It is distributed in FP8 in this catalog, and community quantizations such as 4-bit GGUF builds make local deployment more accessible. As with other open-weight Qwen releases, the weights and model card are hosted directly by the Qwen team on Hugging Face.

This About section is AI-generated from public sources (Claude Opus 4.8), with no human editing. It may contain inaccuracies — verify critical details against the sources listed above.

Data sources: Venice API · HuggingFace · Wikipedia — enrichment updated 4d ago