Qwen3.5

// Overview

Qwen3.5 is the flagship open-weight model family released by Alibaba's Qwen team on 16 February 2026. The headline model, Qwen3.5-397B-A17B, is a sparse mixture-of-experts network with 397 billion total parameters that activates only about 17 billion per token, giving it the capability of a very large model at the inference cost of a much smaller one. It ships under the Apache 2.0 license with weights on Hugging Face, GitHub, and ModelScope.

Architecturally, Qwen3.5 moves away from a pure softmax-attention transformer. Roughly three quarters of its layers use Gated DeltaNet linear attention, with the remaining quarter using conventional grouped-query softmax attention and RoPE. This hybrid design is what lets Alibaba claim large throughput gains over the dense Qwen3-Max at long context lengths while matching its quality. It is also the first open-weight Qwen model with native vision and video input, folding the previous text-only Qwen3 and vision-focused Qwen3-VL lines into one early-fusion backbone.

The flagship was followed by smaller open-weight variants — Qwen3.5-122B-A10B, 35B-A3B, and 27B on 24 February 2026, then 9B, 4B, 2B, and 0.8B on 2 March 2026 — covering everything from data-center MoE to edge-sized dense models. A hosted, proprietary Qwen3.5-Plus endpoint extends the context window to 1M tokens. The series supports 201 languages and dialects (up from 119 in Qwen3) and runs in both reasoning and non-reasoning modes.

// Benchmarks

1. GPQA Diamond88.4%
2. AIME 202691.3%
3. LiveCodeBench v683.6%
4. SWE-bench Verified76.4%
5. MMLU-Pro87.8%
6. IFBench76.5%
7. Tau2-Bench86.7%
8. MMMU85%
9. MMMU-Pro79%
10. OmniDocBench v1.590.8%
11. Video-MME87.5%
12. BrowseComp78.6%
13. Terminal-Bench 2.052.5%

Scores on a 0–100 scale (25-point gridlines); higher is better. Each benchmark links to its published source.

// Pricing

Third-party hosted pricing for the open-weight Qwen3.5-397B-A17B (FP8) on DeepInfra; the weights themselves are free to self-host under Apache 2.0. Alibaba's hosted Qwen3.5-Plus endpoint is priced separately.

Pricing source ↗

// Strengths

• Strong agentic and tool-use results: 86.7 on Tau2-Bench, 78.6 on BrowseComp, and 52.5 on Terminal-Bench 2.0
• Top-tier open-weight reasoning and coding — 88.4 GPQA Diamond, 91.3 AIME 2026, 83.6 LiveCodeBench v6, 76.4 SWE-bench Verified
• Native multimodality in an open model: text, images, video, and document understanding from one backbone (90.8 on OmniDocBench v1.5)
• Hybrid linear + softmax attention gives much higher long-context throughput than dense models at comparable quality
• Permissive Apache 2.0 license with a full ladder of sizes from 397B-A17B down to 0.8B for self-hosting
• Broad multilingual coverage across 201 languages and dialects

// Best for

• Self-hosted agentic systems that need open weights for tool use, browsing, and terminal/coding workflows
• Multimodal document, chart, and video understanding pipelines on private infrastructure
• Long-context retrieval and analysis up to 256K tokens (1M via the hosted Plus endpoint)
• On-device and edge deployments using the smaller 27B/9B/4B/2B/0.8B dense variants
• Cost-sensitive high-throughput inference where the MoE's 17B active footprint cuts serving cost
• Multilingual assistants and translation across 201 languages

// How to access

// Qwen (open-weight) — every version

The full lineage of the Qwen (open-weight) line, newest first. Every version has its own page — click any to compare specs, benchmarks and pricing.