What are the strengths of this model?

Support for large context capacity Design specialized for dialogue Available under MIT license

What are the weaknesses of this model?

Relatively large model size of approx. 18GB Dependence on computational resources Insufficient optimization for specific tasks

What are the best use cases?

Building advanced AI chatbots Analysis of long documents Open-source AI development

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AlibabaOpen Source

Qwen3.5-9B-Instruct

Name: Qwen3.5-9B-Instruct
Author: Alibaba

Qwen3.5-9B-Instruct is a foundation model developed by Alibaba. It features a chat-focused design and supports a long context window of 128K.

Parameters

90.0B

Context Window

128K

License

MIT

Release Date

2026-02-16

API Pricing

API pricing for this model is not yet available

Strengths

・Support for large context capacity
・Design specialized for dialogue
・Available under MIT license

Weaknesses

・Relatively large model size of approx. 18GB
・Dependence on computational resources
・Insufficient optimization for specific tasks

Use Cases

・Building advanced AI chatbots
・Analysis of long documents
・Open-source AI development

Deep Analysis

Release Date

March 2, 2026

Parameters

Dense model — all parameters active

Architecture

Gated DeltaNet + Gated Attention (32 layers)

Context Window

262,144 tokens (native), ~1M via YaRN

Modalities

Text, Image, Video

VRAM (Q4)

~5-6 GB

VRAM (BF16)

~18 GB

Languages

201

License

Apache 2.0

GPQA Diamond

81.7

Beats Qwen3-30B (73.4) and Qwen3-80B (77.2)

Strengths

・Beats Qwen3-30B (3x its size) on GPQA Diamond (81.7 vs 73.4), IFEval (91.5 vs 88.9), LongBench v2 (55.2 vs 44.8)
・Dominates GPT-5-Nano on vision: MMMU-Pro +13, MathVision +17, OmniDocBench +32
・Runs on nearly any modern GPU: ~5GB at Q4, fits on RTX 3060 or M1 Mac
・Natively multimodal with video support from same weights — no separate VL variant
・Apache 2.0 license with thinking/non-thinking mode toggle

Weaknesses

・Coding benchmarks trail larger models: LiveCodeBench 65.6 vs GPT-OSS-120B's 82.7
・9B parameters inherently limited for the most complex multi-step reasoning
・Vision encoder quality degrades on low-resolution or heavily compressed images
・Community reports occasional instability with Ollama integration
・Not yet available as a major cloud API (primarily self-hosted)

Competitor Comparison

Model	Arena	SWE	GPQA	Price
GPT-5-Nano	~1350	~55	~78	Proprietary
Qwen3-30B	~1360	~58	73.4	Open-source
Qwen3.5-9B	~1370	~60	81.7	Open-source
Gemma 3 12B	~1350	~56	~75	Open-source
Llama 3.3 8B	~1340	~52	~70	Open-source

Overview

Qwen3.5-9B is the standout model in the Qwen3.5 Small Series — a 9B dense model that punches absurdly above its weight. It beats the previous-generation Qwen3-30B (3x its size) on knowledge, reasoning, and long-context benchmarks, and dominates GPT-5-Nano on vision tasks by double-digit margins. With ~5GB VRAM at Q4, it runs on virtually any modern GPU including the RTX 3060 and M1 Mac.

Benchmarks & Performance

Exceptional for its size: MMLU-Pro 82.5, GPQA Diamond 81.7, IFEval 91.5, SuperGPQA 58.2, C-Eval 88.2, LongBench v2 55.2, AA-LCR 63.0. Vision: MMMU 78.4, MMMU-Pro 70.1 (vs GPT-5-Nano's 57.2), MathVision 78.9 (vs 62.2), OmniDocBench1.5 87.7 (vs 55.9), VideoMME 84.5 with subtitles. Beats Qwen3-80B on GPQA Diamond and IFEval despite being 9x smaller.

Detailed Comparison

The headline comparison: beats GPT-OSS-120B on MMLU-Pro (82.5 vs 80.8), GPQA Diamond (81.7 vs 80.1), and MMMLU (81.2 vs 78.2) — a 13x size difference. However, GPT-OSS-120B wins on coding (LiveCodeBench 82.7 vs 65.6). Compared to the 27B, it trails by 4-7 points on benchmarks but runs at 3x less VRAM. Compared to Gemma 3 12B and Llama 3.3 8B, it is clearly superior on both knowledge and vision tasks.

Community Feedback

Widely celebrated as one of the best small models available. The '9B beats 120B' narrative generated significant attention. AI researcher Nathan Lambert called Qwen 3.5 models 'legitimately fantastic.' Recommended as the best value model for local AI. Some users note the 4B is more popular for pure coding tasks. The vision capabilities are praised for practical tasks like document analysis and screenshot understanding.

Use Cases

The sweet-spot model for local AI users. Excellent for agentic coding on RTX 3060 (6GB VRAM at Q4), knowledge Q&A, document understanding, image analysis, video comprehension, long-context processing, and general-purpose assistant tasks. For coding-heavy workflows, consider the 4B (more stable) or 35B-A3B (faster). For creative writing, the 27B produces better prose. The 9B is the best all-around choice for users with limited hardware.

Latest News

Released March 2, 2026. Available on HuggingFace with GGUF quantizations from Unsloth. Microsoft Azure AI Foundry support added. Qwen Cloud offers API access. Community testing confirms strong real-world performance across diverse tasks.

Sources

Analysis generated: 2026-05-24