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Qwen3.5

Qwen3.5
2 min read
Qwen3.5

Qwen3.5, which Alibaba released on February 16, 2026, is an Apache 2.0 open-weights multimodal model, Qwen3.5-397B-A17B, built for agent work, with a hosted version called Qwen3.5-Plus.

  • Model: Qwen3.5-397B-A17B (Qwen/Qwen3.5-397B-A17B)

  • Maker: Alibaba

  • Ship date: February 16, 2026

  • Pricing: Free (open weights)

  • Context: 256K tokens native, extensible to about 1M

  • Weights: Open (Apache 2.0)

  • Where you can use it:

  • When to reach for it:

    • Open agentic coding and tool use

    • Image, video and document understanding

    • Skip it for local use; weights are ~807GB

  • How it compares:

    • Trades blows with Kimi K2.5 on Qwen's tests

    • Behind GPT-5.2 and Claude Opus 4.5 on code

    • Other open peers: GLM-5, DeepSeek V3.2

  • What makes it different:

    • 397B parameters, 17B active per token

    • Linear-attention hybrid (Gated DeltaNet)

    • Native vision; 201 languages and dialects

  • Early reactions: Hacker News readers liked a strong open multimodal flagship but raised Qwen's reputation for "benchmaxing" and said it is too big to run on most local hardware.

  • Read more: Qwen3.5: Towards Native Multimodal Agents, model card

The benchmarks

Qwen's own runs from the launch-day model card, against GPT-5.2, Claude Opus 4.5, Gemini 3 Pro, Qwen3-Max-Thinking and Kimi K2.5, with no reasoning effort stated for the rivals (Qwen3.5 runs in thinking mode by default); BrowseComp uses Qwen's simple context-folding setup, and with the discard-all strategy that Kimi K2.5 used, Qwen3.5 scored 78.6% to Kimi K2.5's 74.9%.

Alibaba's numbers

  • SWE-bench Verified (real GitHub fixes): 76.4%. −3.6 under GPT-5.2 (80.0%), −4.5 under Claude Opus 4.5 (80.9%), +0.2 over Gemini 3 Pro (76.2%), −0.4 under Kimi K2.5 (76.8%).

  • Terminal-Bench 2.0 (agentic terminal tasks): 52.5%. −1.5 under GPT-5.2 (54.0%), −6.8 under Claude Opus 4.5 (59.3%), −1.7 under Gemini 3 Pro (54.2%), +1.7 over Kimi K2.5 (50.8%).

  • BrowseComp (web research, context folding): 69.0%. +3.2 over GPT-5.2 (65.8%), +1.2 over Claude Opus 4.5 (67.8%), +9.8 over Gemini 3 Pro (59.2%).

  • IFBench (instruction following): 76.5%. +1.1 over GPT-5.2 (75.4%), +18.5 over Claude Opus 4.5 (58.0%), +6.1 over Gemini 3 Pro (70.4%), +6.3 over Kimi K2.5 (70.2%).

  • GPQA (grad-level science): 88.4%. −4.0 under GPT-5.2 (92.4%), +1.4 over Claude Opus 4.5 (87.0%), −3.5 under Gemini 3 Pro (91.9%), +0.8 over Kimi K2.5 (87.6%).

  • Humanity's Last Exam (no tools) (expert questions): 28.7%. −6.8 under GPT-5.2 (35.5%), −2.1 under Claude Opus 4.5 (30.8%), −8.8 under Gemini 3 Pro (37.5%), −1.4 under Kimi K2.5 (30.1%).

  • LiveCodeBench (coding problems, v6): 83.6%. −4.1 under GPT-5.2 (87.7%), −1.2 under Claude Opus 4.5 (84.8%), −7.1 under Gemini 3 Pro (90.7%), −1.4 under Kimi K2.5 (85.0%).

  • MMMU (multimodal reasoning): 85.0%. −1.7 under GPT-5.2 (86.7%), +4.3 over Claude Opus 4.5 (80.7%), −2.2 under Gemini 3 Pro (87.2%), +0.7 over Kimi K2.5 (84.3%).

Independent numbers

  • Artificial Analysis Intelligence Index (current v4.3.2): 18.4. −5.1 under Kimi K2.5 (23.5), −9.5 under GLM-5 (27.9), −3.1 under DeepSeek V3.2 (21.5).

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