アリババプロプライエタリ

Qwen3.6-27B

Name: Qwen3.6-27B
Author: アリババ

このモデルを比較

アリババ開発の高性能MoEモデル。多言語対応と高い推論能力を特徴とする。

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パラメータ

非公開

コンテキスト長

ライセンス

https://huggingface.co/Qwen/Qwen2.5-72B/blob/main/LICENSE

リリース日

2026-04-22

日本語性能

✅高品質日本語

多言語対応モデルのうち、日本語処理に優れた性能を持つモデル。

API料金

このモデルのAPI料金情報は現在未公開です

強み

弱み

活用例

深度分析

SWE-bench Verified

77.2%

Outperforms Qwen3.5-397B MoE (76.2%)

GPQA Diamond

87.8%

Top reasoning for a 27B model

Terminal-Bench 2.0

59.3%

Matches Claude 4.5 Opus (59.3%)

Context Window

262,144 tokens

Extendable to 1M via YaRN

Active Parameters

27B (dense)

All parameters active per token

Output Price (Hosted)

~$2.00/1M tokens

vs Claude Opus 4.8: $25/1M

強み

・Flagship agentic coding performance in a 27B dense model, surpassing its 397B MoE predecessor
・Runs on a single consumer GPU (e.g., RTX 4090 at ~43 t/s Q4 quantization) with easy local deployment
・Native 262K token context window with strong long-context retrieval and reasoning

弱み

・Inference speed is 3-4x slower than the sibling Qwen3.6-35B-A3B MoE model
・Maximum context at full precision is limited by VRAM; achieving 262K often requires quantization
・On the hardest agentic tasks (e.g., SWE-bench Pro), it still trails frontier closed models like Claude Opus

競合比較

Model	Arena	SWE	GPQA	Price
Qwen3.6-35B-A3B	N/A	73.4%	86.0%	Self-hosted
Qwen3.5-397B-A17B	N/A	76.2%	88.4%	Self-hosted
Claude 4.5 Opus	N/A	80.9%	87.0%	$15/$75 (Opus 4.8)

概要

Qwen3.6-27B is Alibaba's April 2026 release of a 27-billion parameter dense multimodal model under an Apache 2.0 license. Its headline achievement is delivering flagship-level agentic coding performance, outperforming Alibaba's own much larger Qwen3.5-397B MoE model on major benchmarks like SWE-bench Verified (77.2% vs 76.2%) while running on a single high-end consumer GPU. The model uses a hybrid architecture with Gated DeltaNet layers, supports native 262K token context, and is designed for real-world utility, with key features like "Thinking Preservation" for improved agent workflows. Positioning itself as a practical powerhouse, Qwen3.6-27B fills a critical niche: it offers near-frontier reasoning and coding capability with the data sovereignty, cost control, and offline operability of an open-weight model. It competes not directly with frontier APIs on raw capability (trailing models like Claude Opus on the hardest benchmarks) but on value, privacy, and deployability. Its dense architecture simplifies deployment and inference compared to complex MoE models, making it an ideal default for developers and teams with a single high-end GPU who need a versatile model for coding, research, and agentic tasks. Key innovations include its efficient dense architecture punching above its weight class, a massive native context window for whole-codebase reasoning, and strong multimodal support. The release emphasizes stability and productivity, with optimized sampling parameters and integration with popular agent frameworks like Qwen-Agent and OpenClaw. Community reception has been strong, with praise for its performance-per-dollar and local inference speed, though noted is its lower throughput compared to its MoE sibling for high-volume serving.

ベンチマーク＆性能

Qwen3.6-27B demonstrates remarkable performance for its size, particularly in coding and reasoning. According to the official benchmark card from Alibaba's HuggingFace README, it leads or is highly competitive across numerous agentic coding benchmarks. | Benchmark Category | Benchmark | Qwen3.6-27B | Qwen3.5-397B-A17B (MoE) | Notes | | :--- | :--- | :--- | :--- | :--- | | **Agentic Coding** | SWE-bench Verified | **77.2%** | 76.2% | Outperforms much larger predecessor | | | SWE-bench Pro | 53.5% | 50.9% | Higher difficulty subset | | | Terminal-Bench 2.0 | **59.3%** | 52.5% | Matches Claude 4.5 Opus (59.3%) | | | SkillsBench Avg5 | **48.2%** | 30.0% | +18.2 point improvement | | **Knowledge & Reasoning** | GPQA Diamond | **87.8%** | 88.4% | Top-tier STEM reasoning | | | MMLU-Pro | 86.2% | 87.8% | Strong general knowledge | | | AIME 2026 | 94.1% | 93.3% | Advanced math competition | | | LiveCodeBench v6 | 83.9% | 83.6% | Contamination-free code generation | *All benchmark settings and notes are from the official source.* **Third-Party Inference Benchmarks (Community Testing):** - **NVFP4 Quantization (DGX Spark):** ~30 tokens/sec single-session decode. MMLU accuracy of 0.8446, a modest quality hit from full precision. - **Quantization Comparison (RTX 6000 Pro Blackwell):** - BF16: ~59 t/s decode - FP8: ~100 t/s decode - NVFP4: ~163 t/s decode (2.86x speedup over BF16) - **Apple Silicon (M3 Ultra, MTPLX v2):** ~82.8 t/s decode with Optimized Speed build.

詳細比較

**1. vs. Qwen3.6-35B-A3B (Mixture-of-Experts Sibling):** - **Trade-off:** Quality vs. Speed. The 27B dense model activates all parameters per token, yielding higher accuracy. The 35B-A3B MoE activates only ~3B per token, offering 3-4x faster generation. - **Benchmarks:** 27B wins every official benchmark (e.g., SWE-bench Verified: 77.2% vs 73.4%). - **Deployment:** 35B-A3B offers higher throughput and can serve more users. 27B is simpler to deploy (no expert routing) and may have better VRAM utilization for dense tasks. - **Use Case:** Choose 27B for maximum local quality on coding/reasoning. Choose 35B-A3B for a snappy local assistant or high-volume serving. **2. vs. Qwen3.5-397B-A17B (Previous Gen MoE Flagship):** - **Advantage:** Qwen3.6-27B beats this 397B-parameter model on all major agentic coding benchmarks while being a fraction of the size (27B vs 397B total). This represents a significant efficiency and data quality gain. - **Deployment:** Both are open-weight and self-hostable, but the 27B is far more accessible, running on a single consumer GPU versus requiring multi-GPU setups for the 397B model. **3. vs. Frontier Closed APIs (e.g., Claude Opus 4.8):** - **Capability:** Frontier models hold the absolute performance ceiling. On SWE-bench Pro (the only directly comparable benchmark), Claude Opus 4.8 scores 69.2% vs Qwen's 53.5%. - **Cost & Control:** Qwen3.6-27B is dramatically cheaper (hosted ~$2/1M output tokens vs $25/1M). It can be self-hosted for near-zero marginal cost, offering complete data privacy, no vendor lock-in, and offline use. - **Context:** Both offer very large context windows (262K vs 1M for Claude). - **Positioning:** The choice is between raw capability ceiling (Claude) vs. cost efficiency and control (Qwen). A common pattern is routing high-stakes tasks to frontier APIs and high-volume/sensitive tasks to self-hosted Qwen.

コミュニティ評価

Developer and researcher reactions have been overwhelmingly positive, focusing on the model's exceptional value proposition. - **"Flagship-Level Coding in a 27B Dense Model":** This is the consistent refrain. The community is impressed that a dense model of this size can surpass its own, much larger MoE predecessor on complex coding tasks. - **Local Inference Enthusiasm:** Extensive testing and reports (e.g., on r/LocalLLaMA) detail performance on consumer hardware like the RTX 4090, NVIDIA DGX Spark, and Apple Silicon Macs. Users highlight the practical workflow it enables for local development. - **Quantization Deep-Dives:** The community has actively benchmarked different quantizations (GGUF, NVFP4). Findings note that while NVFP4 offers massive speed boosts (~2.86x over BF16), it can introduce minor issues like looping in agent modes, making FP8 a safer default for coding. - **Ecosystem Integration:** Praise for its seamless integration with popular tools like **Claude Code** (via Anthropic API compatibility), **OpenClaw**, and the official **Qwen Code** agent. The `preserve_thinking` feature is noted as a significant upgrade for agent consistency. - **Cost-Ownership Analysis:** Community analyses calculate break-even points against frontier API pricing, concluding that for high-volume users, self-hosting Qwen3.6-27B can save 3-24 years of API costs, assuming a $6,000 desktop setup. **Adoption Patterns:** - Primary adoption is by developers and small teams using it as a **local coding assistant** and **agentic workhorse**. - Used for **RAG pipelines** and **long-document analysis** due to its large context window. - Increasingly integrated into **CI/CD pipelines** for automated code review, test generation, and fix drafting.

ユースケース

1. **Local Coding Assistant & Agent:** Ideal for developers using tools like **Qwen Code** or **OpenClaw** for integrated terminal-based coding. It handles repository-level understanding, frontend workflows, and iterative debugging with high accuracy. Choose it over the 35B-A3B MoE when code correctness on complex tasks outweighs the need for instant token generation speed. 2. **Agentic Workflow Orchestration:** Use as the "brain" for multi-step agents that require long-context reasoning and tool use. The `preserve_thinking` feature is crucial here, as it maintains reasoning consistency across agent steps. For example, an agent that reads a 200K-token codebase spec, plans a refactor, and executes it file-by-file. 3. **Multimodal Document Analysis:** Leverage its native vision capabilities for tasks like **document OCR, chart understanding, and video Q&A**. It can process images and long videos alongside text, making it suitable for analyzing visual reports, educational videos, or UI screenshots for automated testing. Its score of 87.7% on VideoMME is strong for this scale. 4. **Cost-Sensitive Production Inference:** For companies needing to serve AI capabilities at scale with strict data privacy requirements (healthcare, finance, defense). Self-hosting Qwen3.6-27B on-premise eliminates data egress costs, provides deterministic latency, and avoids vendor lock-in. It's a cost-effective backbone for internal chatbots, data extraction pipelines, and automated support systems.