Qwen (Alibaba)

Qwen 3.6-27B Dense from Alibaba's Qwen Team, released April 22, 2026: 77.2 on SWE-bench Verified, 59.3 on Terminal-Bench 2.0 (matching Claude 4.5 Opus), 262K-to-1M context, Apache 2.0 license, and 40 tok/s on an RTX 3090 with Q4_K_M — summarized from official sources.

Comprehensive guide to Qwen3.5-9B: Ollama setup instructions, hybrid Gated DeltaNet + Sparse MoE architecture, 262K context window, GPQA 81.7 and IFBench 76.5 (beating GPT-5.2's 75.4), comparison with GPT-4o-mini and Claude Haiku, and practical business use cases. Runs on just 5GB RAM.

Comprehensive comparison of Gemma 4, Llama 4, and Qwen 3.5 local LLMs. Detailed analysis of benchmark performance, licensing, Japanese support, hardware requirements, and use case selection criteria.

**[Software Mansion's React Native ExecuTorch](https://docs.swmansion.com/react-native-executorch/) shipped a `useLLM` hook** that gives React Native apps **native on-device LLM integration**. **Supported models**: quantized Qwen (2.5 / 3 / 3.5), Llama 3.2, Hammer 2.1, Phi 4 Mini, SmolLM 2, LFM2.5 (vision), and **[Gemma 4](../columns/gemma-4-technical-report-2026-07) (vision + audio)**. **Two operating modes**: **Functional/Stateless** (developers manage conversation history via `generate()` + `response`; tool calling and chat config don't apply) and **Managed/Stateful** (`sendMessage()` maintains conversation state, parses tool calls, and runs callbacks automatically). **Key features**: token batching (groups tokens before re-render), tool calling (model invokes external functions via tool schemas with automatic parsing and callbacks), vision-language / audio multimodal inputs, generation control (temperature / top-p / repetition penalty / mid-stream interruption), and **JSONSchema- or Zod-backed structured output**. **Use cases**: on-device chatbots without server dependencies, privacy-first conversation UIs, in-app function calling (calendar events, flashlight, etc.), multimodal features (image analysis, audio transcription). **Positioning**: implementation-level evidence that **cutting-edge open-weight LLMs like [Gemma 4's encoder-free 12B](../columns/gemma-4-technical-report-2026-07) and [Qwen 3.6-35B](../columns/qwen36-35b-a3b-uncensored-abliterated-2026-07) natively fit into iOS / Android apps** — a major deliverable from the React Native ecosystem.

A 2026 Q2 performance benchmark of Gemma 4 (E2B / E4B / 26B MoE / 31B Dense) against the major open-weights peers — Llama 4, Qwen 3.5, Mistral, and DeepSeek — across MMLU-Pro, GPQA, HumanEval, MATH-500, and MT-Bench. Adds throughput (tokens / s), memory efficiency (quality per GB VRAM), cost per million tokens, Japanese-language performance, native function calling, and Apache 2.0 / MIT / commercial-use licensing as of May 2026, plus a use-case selection matrix for in-house LLM, edge AI, coding assistants, and RAG.