10 open source LLMs that run locally in 2026

Ten open source LLMs now compete with proprietary models for local use in 2026.

Open source models are no longer a compromise for local AI. This list shows which ones are best for reasoning, coding, long context, agents, and smaller hardware, with benchmark scores and memory needs you can compare fast.

Item	Key strength	Notable benchmark or spec	Typical VRAM
Qwen 3 235B-A22B	Reasoning and coding	LiveCodeBench 89%, SWE-Bench 40.0%	~132 GB Q4
DeepSeek V4 Pro	Math and technical work	GSM8K 96.0%, LiveCodeBench 93.5%	~136 GB Q4
Kimi K2.6	Long-context workflows	2M token context window	80GB+ for full context
GLM-5 / GLM-5.1	Agentic AI	Tau2-Bench 89.7%	64GB+ VRAM
Llama 3.3 70B	Single-GPU all-rounder	MMLU 82%, HumanEval 86.0%	~40 GB Q4

1. Qwen 3 235B-A22B

Qwen 3 235B-A22B is the strongest overall pick if you want one model that can handle reasoning, coding, and long-form work at a very high level. Its mixture-of-experts design activates only 22B parameters per token, which helps keep compute more manageable than the raw size suggests.

The trade-off is hardware. The article’s benchmark table puts it at about 132 GB VRAM in Q4, so this is a serious workstation or server choice, not a casual laptop model. If you have the setup, though, it is one of the closest open models to frontier proprietary systems.

• LiveCodeBench: 89%
• SWE-Bench: 40.0%
• License: Apache 2.0
• Best for: enterprise agents and complex coding

2. DeepSeek V4 Pro

DeepSeek V4 Pro is the benchmark pick for math-heavy and technical reasoning tasks. The source cites 96.0% on GSM8K and 93.5% on LiveCodeBench, which makes it a strong choice when correctness matters more than convenience.

It is also one of the heaviest models in the list, with around 136 GB VRAM in Q4 and a 671B parameter MoE design. That means this is a model for high-end multi-GPU systems or enterprise hardware, not a budget local install.

• GSM8K: 96.0%
• SWE-Bench: 67.8%
• License: MIT
• Best for: math, research, competitive programming

3. Kimi K2.6

Kimi K2.6 is the clear pick for long-context work. With support for up to 2 million tokens, it is built for people who need to read large document sets, inspect long codebases, or keep extended conversations coherent.

The model’s benchmark profile is less about raw leaderboard dominance and more about practical memory of huge inputs. The article notes 85% LiveCodeBench and 43.8% on SWE-rebench, plus an Apache 2.0 license that keeps deployment flexible.

• Context window: 2M tokens
• LiveCodeBench: 85%
• License: Apache 2.0
• Best for: document analysis and multi-turn workflows

4. GLM-5 / GLM-5.1

GLM-5 and GLM-5.1 are the strongest choices for agentic AI, where the model needs to plan, call tools, and complete multi-step workflows. The article says GLM-5 Reasoning reached a Quality Index of 49.64 and scored 89.7% on Tau2-Bench.

If you are building autonomous assistants rather than a plain chat model, this family is worth a close look. It also posts 89% on LiveCodeBench, so coding support is not an afterthought.

• Tau2-Bench: 89.7%
• Quality Index: 49.64
• LiveCodeBench: 89%
• Best for: agents, planning, multi-step tasks

5. Llama 3.3 70B

Llama 3.3 70B is the most practical all-rounder for many local setups. It is widely supported, performs well across general tasks, and fits the common pattern of “strong enough for production, still possible on serious consumer hardware with quantization.”

The source gives it 82% on MMLU, 86.0% on HumanEval, and about 40 GB VRAM in Q4. That puts it in the sweet spot for people who want one model that can do a lot without demanding an enterprise cluster.

• MMLU: 82%
• HumanEval: 86.0%
• VRAM: ~40 GB Q4
• Best for: general-purpose use and fine-tuning

6. Gemma 3 27B

Gemma 3 27B is the mid-range model to beat if you want good quality without jumping into heavyweight infrastructure. It also supports vision, which gives it an edge for multimodal work on consumer hardware.

With about 16 GB VRAM in Q4, it is realistic for a strong single-GPU desktop or a MacBook Pro M4 Max. The article lists MMLU at roughly 78.6% and HumanEval at 87.8%, which makes it a very balanced option for cost-conscious builders.

• MMLU: ~78.6%
• HumanEval: 87.8%
• Multimodal: yes
• Best for: single-GPU and vision tasks

7. Mistral Small 3.1 24B

Mistral Small 3.1 24B is the best fit for 16 GB VRAM setups that still need long context and dependable instruction following. It is not the biggest model here, but it is one of the most practical.

The source calls out 128K context support and around 16 GB VRAM in Q4. That makes it a strong candidate for chatbots, retrieval-augmented generation, and document-heavy workflows where memory use has to stay under control.

• Context window: 128K tokens
• VRAM: ~16 GB Q4
• License: Apache 2.0
• Best for: RAG apps and long documents

8. Phi-4 14B

Phi-4 14B is the small model to watch if you care about reasoning efficiency more than sheer size. Microsoft positions it as a compact model with class-leading reasoning for its parameter count, and the article notes a 14B footprint with about 8 to 10 GB VRAM in Q4.

That makes it a strong option for edge deployment, smaller desktops, and commercial products where the MIT license matters. If you want a model that is easy to fit and still smart, this is one of the best bets.

• Model size: 14B
• VRAM: ~8-10 GB Q4
• License: MIT
• Best for: edge use and commercial apps

9. MiMo-V2.5-Pro

MiMo-V2.5-Pro, released as Hunter Alpha, is a specialist model for agentic coding and long-horizon reasoning. It is the kind of model that makes sense when you want automation that can keep track of a larger task rather than just answer a prompt.

The source describes it as competitive with top-tier coding models and useful for bilingual Chinese-English work. Because the hardware needs vary by variant, it is less predictable than some of the other picks, but the focus is clear.

• Focus: agentic coding
• Strength: long-horizon reasoning
• License: open weight
• Best for: automation and bilingual workflows

10. MiniMax M2.7

MiniMax M2.7 is the multimodal entry in this list, with support for text, vision, and audio. If your use case spans media types instead of pure text, that broad input support can matter more than a few benchmark points.

The article gives it 39.6% on SWE-rebench and says 64GB+ is recommended, so this is not a light install. It is better suited to creative workflows, richer assistants, and high-end systems that need more than a text-only model.

• Multimodal: text, vision, audio
• SWE-rebench: 39.6%
• VRAM: 64GB+ recommended
• Best for: creative and multimodal applications

How to decide

If you want the strongest overall model and have the hardware, start with Qwen 3 235B-A22B. If your work is math-heavy, DeepSeek V4 Pro is the sharper pick. For long documents and giant codebases, Kimi K2.6 is the easiest recommendation.

For most builders, the best practical choices are Llama 3.3 70B, Gemma 3 27B, or Mistral Small 3.1 24B, depending on your VRAM. If you are building agents, choose GLM-5.1. If you need a small commercial model, Phi-4 14B is the cleanest fit.