Qwen 3.5 Tops Global Open-Source LLM Top 4

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⚛️Read original on 量子位

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💡Open-source Qwen 3.5 crushes coding benchmarks: 10min vs 5hr pro task, 1B+ downloads

⚡ 30-Second TL;DR

What Changed

Ranks in top 4 of global open-source LLMs

Why It Matters

This benchmark dominance boosts open-source AI adoption, challenging closed models and enabling faster developer workflows with superior coding efficiency.

What To Do Next

Download Qwen 3.5 from Hugging Face and test it on your intermediate coding benchmarks.

Who should care:Developers & AI Engineers

Key Points

•Ranks in top 4 of global open-source LLMs
•Completes 5-hour coding task for intermediate programmer in 10 minutes
•Cumulative downloads exceed 1 billion
•Over 200,000 derivative models created

🧠 Deep Insight

Background and context from public sources — not the original article. 6 sources cited.

🔑 Enhanced Key Takeaways

•Qwen3.5-397B-A17B is a Mixture-of-Experts model with 397 billion total parameters and only 17 billion activated parameters per token[1][2][5].
•Features a native context length of 262k tokens, enabled by Gated DeltaNet + Gated Attention hybrid mechanism[1][4].
•Achieves top GPQA Diamond score of 88.4 among open-source models and excels in embodied reasoning with 67.5 on ERQA benchmark[2][3].
•Released on February 16, 2026, under Apache 2.0 license as a native multimodal model processing text and images[4][5].

📊 Competitor Analysis▸ Show

Feature/Benchmark	Qwen 3.5	Kimi K2.5	GLM-5	Claude Opus 4.5
GPQA Diamond	88.4[3]	87.6[3]	86.0[3]	-
IFEval	92.6[3]	94.0[3]	-	-
SWE-Bench (Verified/Pro)	On par[1]	-	On par[1]	Slightly above[1]
Parameters (Total/Active)	397B/17B[5]	1T/32B[3]	-	Closed
Context Length	262k[1][4]	262k[3]	-	-

🛠️ Technical Deep Dive

•Mixture-of-Experts (MoE) architecture: 397B total parameters, 17B active per token; 3x smaller than prior 235B-A22B but with 4x more experts plus a shared expert[1][5].
•Attention mechanism: Gated DeltaNet + Gated Attention hybrid, supporting native 262k token context (vs. 32k/131k in prior models)[1].
•Vocabulary size: 250k tokens with multi-token prediction, reducing costs by 10-60% across 201 languages[2].
•Multimodal capabilities: Native text+image processing with early fusion for video; excels in document recognition (90.8% OmniDocBench)[2][5].
•Efficiency: 19x faster decoding on 256k contexts than Qwen3-Max; 8.6x faster for standard tasks without performance loss[2].

🔮 Future ImplicationsAI analysis grounded in cited sources

Qwen3.5 will accelerate open-source adoption in agentic coding agents

Its SWE-Bench performance matches closed models like Claude Opus 4.5 while being efficient for local deployment at 51GB RAM[1].

Efficiency gains will lower inference costs for multimodal apps by 10-60%

Multi-token prediction and large vocabulary reduce token usage across 201 languages, combined with MoE sparsity[2].

Qwen3.5 sets new open-weight benchmark for long-context reasoning

262k native context and top GPQA Diamond score enable advanced RAG and multi-step tasks previously limited to closed models[1][3].

⏳ Timeline

2026-02-16

Qwen3.5 series released, including 397B-A17B MoE model

📎 Sources (6)

Factual claims are grounded in the sources below. Forward-looking analysis is AI-generated interpretation.

⚛️Read original article on 量子位

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量子位•Aug 22

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