AI Model2026-05-17
Llama (Meta)
Also known as: Llama (Meta) / Llama 4 / Meta Llama
Meta's open-weights large language model family. The Llama 4 generation adopts MoE architecture and ships under a commercial-friendly licence enabling local deployment and fine-tuning.
Overview
Llama 4 uses a Mixture-of-Experts design for efficient inference and is a leading local-LLM option alongside Gemma 4 and Qwen 3.5, easily run via Ollama. See Gemma 4 vs Llama 4 vs Qwen 3.5.
Ecosystem
Supported by Meta AI, Ray, vLLM, and many other frameworks, making it one of the most widely deployed open models in on-premises enterprise AI.
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Gemma 4 vs Llama 4 vs Qwen 3.5 Comparison — 2026 Local LLM Selection Guide
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.
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Following Anthropic Claude restrictions, comprehensive comparison of local LLMs including Qwen 3.5-9B, Mistral Small 4, DeepSeek R1, Gemma 4, and Llama 4. Detailed analysis of Japanese performance, hardware requirements, and use-case recommendations.
Mobile Development
[React Native ExecuTorch's `useLLM` Hook](https://docs.swmansion.com/react-native-executorch/docs/hooks/natural-language-processing/useLLM) Deep Dive — Run Qwen / Llama 3.2 / Hammer 2.1 / Phi 4 Mini / SmolLM 2 / LFM2.5 / Gemma 4 On-Device in React Native, With Tool Calling, Vision / Audio, and Structured Output
From Software Mansion, Two Modes (Managed / Functional), and Zod-Backed Schema Validation
**[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.
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Gemma 4 Performance Benchmark — Compared Against Llama 4, Qwen, Mistral, and DeepSeek on Quality, Speed, and Cost-Efficiency [2026 Open-Weights LLM Showdown]
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.
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