The best local LLM models for iPhone in 2026 span a range from 622MB to 5.6GB. Qwen 3.5 4B is the recommended starting point for most users — it weighs 2.9GB, runs smoothly on any iPhone 12 or later, and handles general conversation, writing, and reasoning without a cloud connection. If you need specialist coding ability, Phi-4 Mini from Microsoft is the sharper tool. For the smallest footprint possible, Qwen 3.5 0.8B at 622MB fits on any device made in the last six years.
This guide covers seven models currently available in Cloaked — all running on-device via Apple’s MLX framework, all completely private. For background on how on-device inference works, see The Complete Guide to On-Device AI and What Is Apple MLX?.
How to Read These Specs
Every model listed here runs through Apple MLX, the framework Apple built for efficient matrix computation on its Neural Engine and GPU. Quantization compresses model weights — Q4 quantization shrinks a model to roughly 4 bits per parameter, trading a small amount of accuracy for a major reduction in storage and RAM.
Token speed is measured on an iPhone 15 Pro in Cloaked at room temperature. Real-world speeds vary based on prompt length, device thermal state, and concurrent background processes. As a rule of thumb: 20+ tokens per second feels conversational; below 10 starts to feel slow.
Across all seven models tested, average response quality improved significantly from their 2024 equivalents — a pattern consistent with the broader finding that open-source models roughly doubled in capability every 8–10 months throughout 2024 and 2025.
The Recommended Models, Ranked by Use Case
Qwen 3.5 4B — Best All-Around (Recommended)
Size: 2.9GB | RAM required: ~3.5GB | Speed: ~38 tok/s on iPhone 15 Pro
Qwen 3.5 4B is the model most users should download first. Alibaba’s Qwen 3.5 series landed in early 2026 with meaningful jumps in instruction-following, multilingual quality, and structured output. The 4B variant hits a sweet spot: capable enough for complex reasoning tasks, compact enough to fit on any iPhone 12 or later without crowding storage.
It handles general Q&A, long-form writing, summarization, code explanation, and translation comfortably. It supports 32 languages natively, which makes it the obvious pick for non-English users. Hugging Face model page.
Qwen 3.5 9B — Flagship Performance
Size: 5.6GB | RAM required: ~7GB | Speed: ~18 tok/s on iPhone 15 Pro Max
The 9B variant is the most capable model available for on-device iPhone use as of April 2026. It outperforms GPT-3.5-level cloud models on most benchmarks and approaches GPT-4-class quality on creative and analytical tasks. The tradeoff is clear: it requires an iPhone 15 Pro or Pro Max (8GB RAM) and will fill roughly a third of a 16GB device’s storage.
On iPad Pro with M4 chip, it generates closer to 35 tokens per second — nearly conversational. For users who want the absolute ceiling of what runs locally, this is it. Hugging Face model page.
Qwen 3 4B — Best for Reasoning with Thinking Mode
Size: 2.1GB | RAM required: ~2.8GB | Speed: ~42 tok/s on iPhone 15 Pro
Qwen 3 4B (note: this is the earlier Qwen 3 series, not 3.5) introduced a thinking mode — a chain-of-thought reasoning approach where the model explicitly works through a problem step by step before answering. For logic puzzles, multi-step math, and structured analysis, this mode produces noticeably better results than a standard forward pass.
At 2.1GB, it’s lighter than Qwen 3.5 4B while still delivering strong reasoning. Users who frequently work through complex problems and want to see the model’s reasoning process should strongly consider this one. Hugging Face model page.
Llama 3.2 3B — Reliable General Purpose
Size: 2.1GB | RAM required: ~2.6GB | Speed: ~45 tok/s on iPhone 15 Pro
Meta’s Llama 3.2 3B is the most widely tested local model in this list — it’s been running on-device since late 2024, and the ecosystem of tools, fine-tunes, and benchmarks around it is extensive. That maturity matters: it behaves predictably, handles system prompts reliably, and works well in structured workflows.
It’s slightly behind Qwen 3 4B on multilingual tasks and reasoning, but ahead on instruction-following consistency. If you already have system prompts or workflows tuned for Llama, this is the model to stick with. Hugging Face model page.
Among the seven models listed here, Llama 3.2 3B and Qwen 3 4B share the same storage footprint (2.1GB) while taking different approaches to reasoning — making them easy to compare side by side in Cloaked’s model library.
Phi-4 Mini — Best for Coding and Technical Reasoning
Size: 2.5GB | RAM required: ~3.2GB | Speed: ~35 tok/s on iPhone 15 Pro
Microsoft trained Phi-4 Mini specifically on high-quality synthetic data — math problems, code, and structured reasoning tasks — rather than scraped web text. The result is a model that punches well above its weight on technical tasks. In coding benchmarks, Phi-4 Mini scores close to models twice its size.
For developers who want on-device help with code review, debugging, or algorithm explanation, Phi-4 Mini is the specialist pick. It’s weaker on open-ended creative tasks and less capable in languages other than English, but for technical work it’s the best 3B-class option available. Hugging Face model page.
DeepSeek R1 1.5B — Chain-of-Thought Reasoning in 1.2GB
Size: 1.2GB | RAM required: ~1.6GB | Speed: ~55 tok/s on iPhone 14
DeepSeek’s R1 series popularized visible chain-of-thought reasoning — the model shows its work in a <think> block before delivering its final answer. The 1.5B distillation brings this to a model small enough to run on any iPhone made after 2020, including older devices with 4GB RAM.
At 1.2GB, it won’t replace a 4B model for general use. But for step-by-step reasoning on focused questions — logic problems, short proofs, structured analysis — it’s surprisingly capable. It’s also the fastest model in this list, generating tokens quickly enough that the thinking block doesn’t feel like a delay. Hugging Face model page.
Qwen 3.5 0.8B — Ultra-Compact for Any Device
Size: 622MB | RAM required: ~900MB | Speed: ~80 tok/s on iPhone 12
At 622MB, Qwen 3.5 0.8B is the only model in this list that runs comfortably on older devices with 3GB RAM — including iPhone 11 and iPhone SE (2nd generation). It generates tokens faster than any other model here, which makes it feel instant for short tasks.
Quality limitations are real: it struggles with complex multi-step reasoning and long-context tasks. But for quick Q&A, short summaries, brainstorming, and text editing, it’s genuinely useful. For users on older hardware, or anyone who wants a fast, lightweight assistant for simple tasks, this is the right choice. Hugging Face model page.
Quick Comparison Table
| Model | Size | RAM | Speed (tok/s) | Best For |
|---|---|---|---|---|
| Qwen 3.5 4B | 2.9GB | 3.5GB | ~38 | All-around (recommended) |
| Qwen 3.5 9B | 5.6GB | 7GB | ~18 | Maximum capability |
| Qwen 3 4B | 2.1GB | 2.8GB | ~42 | Step-by-step reasoning |
| Llama 3.2 3B | 2.1GB | 2.6GB | ~45 | Reliable general use |
| Phi-4 Mini | 2.5GB | 3.2GB | ~35 | Coding and math |
| DeepSeek R1 1.5B | 1.2GB | 1.6GB | ~55 | Chain-of-thought |
| Qwen 3.5 0.8B | 622MB | 900MB | ~80 | Older devices, fast tasks |
Which Model Should You Download First?
Start with Qwen 3.5 4B unless you have a specific reason to do otherwise. It covers the widest range of tasks, fits comfortably on any recent iPhone, and represents the best balance of capability and size available today.
Download Phi-4 Mini instead if most of your use is coding, debugging, or structured technical work. It outperforms Qwen 3.5 4B on those specific tasks despite being similar in size.
Download Qwen 3.5 9B if you have an iPhone 15 Pro or Pro Max and want the best possible quality regardless of storage and RAM cost.
Start with Qwen 3.5 0.8B or DeepSeek R1 1.5B if you’re on an older device with limited storage, or if you want a fast, lightweight option for quick tasks alongside a larger model.
One practical advantage of running models in Cloaked: you can switch between them instantly. Download two or three models and use the right one for the task. The 4B for general conversation, Phi-4 Mini when writing code, DeepSeek R1 1.5B when you want to see the reasoning process. None of it touches the cloud.
For a deeper look at how these models actually run on-device, read The Complete Guide to On-Device AI.
Download Cloaked on the App Store to run any of these models on your iPhone — no account, no subscription, no data leaving your device.
Frequently Asked Questions
What is the best LLM model for iPhone?
Qwen 3.5 4B is the best all-around local LLM for iPhone in 2026. At 2.9GB, it delivers strong general reasoning, fast responses, and fits comfortably on any iPhone 12 or later with 4GB RAM.
Can I run a 7B model on iPhone?
Yes, on iPhone 15 Pro and Pro Max, which have 8GB RAM, you can run quantized 7B models. Mistral 7B at Q4 quantization fits in about 4GB of RAM. Expect slower token generation — roughly 10–15 tokens per second — compared to 40+ tokens per second for 3B models.
What is the smallest LLM that still works well?
Qwen 3.5 0.8B at 622MB is the best sub-1GB option. It handles simple Q&A, summarization, and text editing well. For more demanding reasoning tasks, step up to DeepSeek R1 1.5B at 1.2GB.
Does model size affect battery life?
Yes. Larger models draw more from the Neural Engine and require more RAM bandwidth. A 3B model generating a 500-word response typically uses 2–4% battery. A 9B model on the same task uses 6–9%. All on-device inference is more efficient than sending data to the cloud.