Best Local LLMs in 2026 — Top Models Ranked
The definitive ranking of the best open-weight models you can run locally right now. Every model on this list runs offline, costs nothing, and keeps your data private.
Updated May 2026 · 10 min read
Quick Answer
The best overall local LLM in 2026 is Llama 3.3 70B for high-end hardware, and Mistral Small 3.1 22B for mid-range machines.
For coding specifically, see our best local LLM for coding guide.
How We Rank These Models
Each model is evaluated on four dimensions: benchmark performance (MMLU, HumanEval, MT-Bench), hardware efficiency (quality per GB of VRAM/RAM), license freedom (Apache 2.0 and MIT score highest), and real-world usability based on community feedback.
Top 8 Local LLMs for 2026
Gemma 4 31B
31B · Gemma ToSGoogle DeepMind's Gemma 4 31B is the strongest local model of 2026 in its VRAM class. It achieves MMLU-Pro 85.2%, LiveCodeBench 80.0%, and a staggering Codeforces ELO of 2150 — placing it firmly in expert territory for competitive programming. Vision support and a configurable thinking mode round out a remarkably complete package that runs in ~20 GB RAM at Q4.
MMLU-Pro
85.2%
Context
256K
Min RAM
20 GB
Vision
Yes
ollama run gemma4:31bQwen3.5 27B
27B (MoE) · Apache 2.0Alibaba's Qwen3.5 27B is the cutting edge of open-weight AI released in May 2026. It achieves SWE-bench Verified 72.4% (on par with frontier closed models), supports vision and audio natively, handles 262K tokens in context, and speaks 201 languages. Its sparse MoE architecture keeps RAM usage to ~18 GB at Q4 despite its depth.
MMLU-Pro
86.1%
Context
262K
Min RAM
18 GB
Modalities
Text/Vision/Audio
ollama run qwen3.5:27bQwen3 32B
32B · Apache 2.0Qwen3 32B introduced a landmark feature for local AI: hybrid thinking mode. Toggle between fast instruct responses and deep chain-of-thought reasoning in the same model using `/think` or `/no_think` in your prompts. Trained on 36 trillion tokens, it delivers frontier-class outputs with full Apache 2.0 commercial freedom and runs in ~20 GB RAM.
Release
May 2025
Context
128K
Min RAM
20 GB
License
Apache 2.0
ollama run qwen3:32bLlama 4 Scout
109B total (17B active MoE) · Llama 4 CommunityMeta's Llama 4 Scout is a massive 109B mixture-of-experts model that activates only 17B parameters per token. Its 10 million token context window is the largest of any local model — capable of ingesting entire codebases or book collections in a single prompt. Native image understanding and competitive MMLU-Pro (74.3%) make it a strong choice for long-context multimodal workflows.
MMLU-Pro
74.3%
Context
10M tokens
Min RAM
55 GB (Q4)
Vision
Yes
ollama run llama4:scoutPhi-4-reasoning 14B
14B · MITMicrosoft's Phi-4-reasoning rewrote the rules for small models. At just 14B parameters and ~9 GB RAM, it scores 75.3% on AIME 2024 and 81.3% on AIME 2025 with the reasoning-plus variant — outperforming DeepSeek-R1 70B on math competition problems. If you need a reasoning powerhouse on limited hardware, nothing else comes close.
AIME 2024
75.3%
HumanEval+
92.9%
Min RAM
9 GB
License
MIT
ollama run phi4-reasoningMistral Small 3.2 24B
24B · Apache 2.0Released in June 2026, Mistral Small 3.2 is a significant upgrade over 3.1 — Arena Hard score jumped from 19.6% to 43.1%, and HumanEval Plus Pass@5 improved to 92.9%. Vision capabilities are built in, and repetitive/infinite generation dropped by half. At ~14 GB RAM (Q4), it's the best balanced model for users who want quality, speed, and permissive licensing.
MMLU-Pro
69.1%
Context
128K
Min RAM
14 GB
Vision
Yes
ollama run mistral-small3.2DeepSeek-R1 32B (Distill)
32B · MITDeepSeek-R1 remains the most widely used reasoning model in the local AI community. The 32B distilled version runs in ~20 GB RAM and excels at math proofs, algorithm design, and systematic debugging by explicitly showing its chain-of-thought before answering. MIT licensed for unrestricted commercial use.
Specialty
Reasoning
Context
128K
Min RAM
20 GB
License
MIT
ollama run deepseek-r1:32bgpt-oss 20B
20B (MoE) · Apache 2.0OpenAI's first open-weight model release. gpt-oss 20B is a compact mixture-of-experts model that runs in 16 GB RAM (MXFP4 quantized) and supports full chain-of-thought, configurable reasoning effort (low/medium/high), and function calling. The Apache 2.0 license makes it one of the most commercially permissive frontier-class local models available.
License
Apache 2.0
Context
128K
Min RAM
16 GB
Tool Calling
Yes
ollama run gpt-oss:20bFull Comparison Table
| Model | MMLU | Min RAM | Context | License |
|---|---|---|---|---|
| Qwen3.5 27B | 86.1% | 18 GB | 262K | Apache 2.0 |
| Gemma 4 31B ★ | 85.2% | 20 GB | 256K | Gemma ToS |
| Qwen3 32B | ~83% | 20 GB | 128K | Apache 2.0 |
| Llama 4 Scout | 74.3% | 55 GB | 10M | Llama 4 |
| Phi-4-reasoning 14B | ~72% | 9 GB | 32K | MIT |
| Mistral Small 3.2 24B | 69.1% | 14 GB | 128K | Apache 2.0 |
| DeepSeek-R1 32B | ~72% | 20 GB | 128K | MIT |
| gpt-oss 20B | ~75% | 16 GB | 128K | Apache 2.0 |
Best Local LLM by Use Case
Best overall (20 GB RAM)
Gemma 4 31B
MMLU-Pro 85.2%, coding ELO 2150, vision — strongest all-rounder in its weight class.
Coding & agentic tasks
Qwen3.5 27B
SWE-bench 72.4% — on par with frontier closed models for real-world software engineering.
Reasoning & math
Phi-4-reasoning 14B
AIME 2024 75.3% at only 14B params — beats DeepSeek-R1 70B on math competitions.
Limited hardware (8 GB)
Qwen3 8B (thinking mode)
Best quality under 8 GB RAM; thinking mode unlocks reasoning depth on modest hardware.
Long-context / multimodal
Llama 4 Scout
10M token context window — ingest entire codebases; built-in vision, 55 GB RAM.
Commercial projects
Qwen3 (Apache 2.0) or gpt-oss
Both are Apache 2.0 — fully unrestricted commercial use, fine-tunable.
Multilingual (201 languages)
Qwen3.5 27B
Covers 201 languages/dialects, including audio input. Best multilingual local model.
Edge / embedded (4 GB)
Phi-4-mini-reasoning 3.8B
MATH-500 94.6% at 3.8B — best tiny reasoning model, runs in ~2.5 GB RAM.
Frequently Asked Questions
Which local LLM is closest to GPT-4 in 2026?
In 2026, Qwen3.5 27B and Gemma 4 31B are the closest to frontier closed models. Qwen3.5 27B achieves SWE-bench Verified 72.4% — matching GPT-4-level software engineering capability — while running locally in ~18 GB RAM. Gemma 4 31B leads on coding benchmarks with Codeforces ELO 2150.
What is the best local LLM for a MacBook in 2026?
Apple Silicon Macs are still the best consumer hardware for local LLMs. An M3 Pro with 36 GB memory runs Gemma 4 31B or Qwen3 32B smoothly. Ollama v0.24+ includes a reworked MLX sampler specifically for Apple Silicon, and Gemma 4 31B on M-series supports speculative decoding (MTP) for 2× faster generation.
Does RAM or GPU matter more for local LLMs?
For models over 20B, GPU VRAM is the main bottleneck. But Qwen3.5 and Gemma 4 use MoE or efficient architectures that let you run strong models in surprisingly modest RAM. Apple Silicon is uniquely powerful because its unified memory is shared between CPU and GPU — a 36 GB M3 Pro outperforms many discrete GPUs for local LLMs.
Are open-weight models safe and legal to use commercially?
Yes — Qwen3 family (Apache 2.0), gpt-oss (Apache 2.0), Phi-4-reasoning (MIT), DeepSeek-R1 (MIT), and Mistral Small 3.2 (Apache 2.0) all allow unrestricted commercial use. Gemma 4 uses the Gemma Terms of Service (more permissive than many think, but read before deploying). Llama 4 has the Llama 4 Community License allowing commercial use up to 700M monthly users.
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