Best Local LLMs (November 2026)

Best Local LLMs (November 2026): The Ranking That Actually Matters

🔎 Fall 2026 Changes the Game

Local AI is no longer a hobby for geeks with battle-station servers. Between September 1 and November 7, 2026, 21 open weights models were released, several of which literally crush what existed six months ago (GPUwerk). The leader of the pack? Kimi K3.1 Pro from Moonshot AI, released on November 2, which becomes the first open weights model to break the 46-point barrier on the AA Intelligence Index v5.0.1.

Concretely, this means that a model you download from Hugging Face and run at home — via Ollama, llama.cpp, or vLLM — now rivals cloud models that cost several dollars per million tokens as recently as a year ago.

But be careful: the landscape has also grown more complex. Between the 1,561 GB monsters that require multi-GPU servers and the small 3.5 GB models that run on a phone, choosing the right model for your hardware has never been more important. This article sorts it all out, with the November 2026 numbers.

If you were looking for an older comparison, our previous editions remain useful for measuring progress: Best Local LLMs (May 2026), Best Local LLMs (June 2026), and Best Local LLMs (September 2026).

The Essentials

• Kimi K3.1 Pro (Moonshot AI) is the best local LLM of November 2026: 46.5 intelligence index, 1M token context, 28 t/s — but it weighs 1,561 GB natively in MXFP4, reserved for multi-GPU servers.
• The best quality-to-hardware ratio remains Gemma 4 31B IT: #1 Overall (99.3) on the OpenWeights Bench for configurations from 24 to 128 GB.
• For a 16 GB laptop: Gemma 4 12B IT (~8.9 GB, score 98.6). For 8 GB: Gemma4 E4B IT (95.5).
• Best open weights API value: GLM 5.3 Flash (56.9 at $0.238/M) and GLM 5.3 at $0.07/M — unbeatable.
• For local coding: Qwen3-Coder-Next GGUF, with JetBrains' Mellum2.1-12B as a lightweight alternative.
• The best closed model (Gemini 4 Carbon, 60) keeps a ~13-point lead over the best open weights. The gap is narrowing, but it exists.

Recommended Tools

My advice: start with Ollama to confirm that a model runs well on your machine, then move on to vLLM or SGLang if multiple people need to use it simultaneously. This is also the recommendation of Rüb Studio's pragmatic guide.

What is the best local LLM in November 2026?

Kimi K3.1 Pro by Moonshot AI, without question. The Modelgrep ranking places it at the top with a score of 46.5 in intelligence index, ahead of DeepSeek V4.1 Flash 1017 (32.6) and GLM 5.3 (31.7). It offers a 1 million token context and outputs 28 tokens/second.

The catch: with 2.8T total parameters (104B active, MoE architecture), its native MXFP4 weight reaches 1,561 GB. This model is therefore reserved for multi-GPU servers—or Moonshot AI's API at $2/M input tokens. It remains "local" in the open weights sense: you can download and run it at home, but not on your laptop.

The BenchLeader ranking (November 9, 2026, 167 qualified models) confirms the hierarchy with a nuance: Kimi K3 (max) ranks #1 at 62.1, followed by Kimi K3.1 Pro (61.8) and GLM-5 thinking (60.2).

The Modelgrep ranking of November 2026

Note that this ranking is limited to downloadable and runnable open weights models—closed frontier models of 400B+ are excluded.

The best local LLM for your hardware

The "best" model depends first on your RAM and GPU. That's the number one criterion, even before benchmarks. Here's the breakdown I recommend, based on the OpenWeights Bench (218 models tested on 270 questions across 13 categories, run r20261102, speeds measured on a laptop) and the hardware guide from Pinggy.

Why Gemma 4 31B dominates the mid-range

Gemma 4 31B IT is #1 in Overall score (99.3) and #1 for all tiers from 24 to 128 GB. It's simple: few open weights models offer this level of quality in a format that fits on a single consumer GPU. In 4-bit quantization, count on 17 to 20 GB — the Unsloth GGUFs with Google's QAT requantization preserve quality remarkably well (Pinggy).

On a 32 GB machine, two alternatives are worth a look according to GPUwerk: Nanbeige4.3-33B (Q4_K_M, 20.8 GB, Apache 2.0 license) and Arete-27B from Nous Research, a fine-tune of Qwen3.8-27B (Q4_K_M, 16.5 GB, Apache 2.0).

Small models have made crazy progress

Two gems to know about:

• LFM3 1.7B from Liquid AI: 3.5 GB in BF16, runs on any GPU and even on a phone. Perfect for embedded systems and prototyping.
• Gemma 4 12B IT: ~8.9 GB, score of 98.6 on the OpenWeights Bench. It's the obvious choice for a 16 GB laptop — the quality-to-size ratio is exceptional.

Kimi K3.1 Pro: What's Really Changing This Month

Kimi K3.1 Pro is the first open weights model to seriously approach closed models. With 46.5 on the AA Intelligence Index, it reduces the gap with Gemini 4 Carbon (60), the best closed model currently available, to about 13 points. In May 2026, this gap was more than double—compare this to our May 2026 edition of mai 2026 to measure the progress made.

Three things to know:

1. Massive MoE Architecture: 2.8T total parameters, 104B active per token. This is what explains both the quality and the monstrous size.
2. 1M Token Context: Enough capacity to load entire codebases or documents hundreds of pages long.
3. Decent but Not Record Speed: 28 t/s. If speed is your absolute priority, DeepSeek V4.1 Flash 1017 (157 t/s on Modelgrep, 153 t/s in max mode according to BenchLeader) is much faster for a score of 32.6.

My take: for 99% of readers, Kimi K3.1 Pro is a model to consume via its API ($2/M input tokens, $4 according to BenchLeader for total cost), not to host. The real benefit of open weights is the availability of the weights—not the obligation to run them in your living room.

The best local LLM for coding

Qwen3-Coder-Next in GGUF is currently the best high-end local coding-agent solution, according to the consensus emerging on the Hugging Face forum for full web development without coding. GLM- (Zhipu's GLM family) comes in a close second.

Two other options depending on your profile:

• JetBrains' Mellum2.1-12B: 24.3 GB in BF16, specialized in code. It's the natural choice if you already use JetBrains tools — the model was designed for development assistance, not general conversation.
• gpt-oss-20b: Rüb Studio's pick for agents and tool calling. If you're hooking your local model up to tools (code execution, search, files), this is the most reliable starting point.

To dig deeper into this topic, check out our dedicated comparison of the best LLMs for coding, which also covers cloud models — often still superior for complex code.

And what about AI agents?

If your goal is to orchestrate autonomous agents locally, gpt-oss-20b remains the safe bet for tool calling. For agent-oriented models in general, our guide to the best LLMs for AI agents details the specific criteria: function calling reliability, robustness over long action chains, and error handling.

Ollama, LM Studio or vLLM: which one should you choose?

Ollama to try things out, LM Studio to explore comfortably, vLLM/SGLang to serve multiple users. This trio covers 95% of needs, and it's exactly the starter kit recommended by Rüb Studio.

• Ollama: one command, and the model is running. Ideal for checking that a model runs on your machine. Our selections of the best Ollama models are updated regularly.
• LM Studio: clean graphical interface, built-in catalog, visible quantization settings. Perfect if the command line puts you off — see our best LM Studio models.
• vLLM or SGLang: as soon as multiple people access the same model (a team, a family, a department), dedicated serving becomes essential. Ollama is not designed for concurrent load.

A note from experience: many people install vLLM "for performance" when they're the only ones using their machine. That's over-engineering. Ollama is more than enough for personal use, and the throughput difference is marginal on a single stream.

Local or cloud: is it still worth hosting everything at home?

No — and that's the big takeaway from November 2026. The best closed model (Gemini 4 Carbon, 60 on the index) retains a clear lead over the best open weights model you can run at home. BenchLeader confirms it: even the best open weights models consumed via API (Kimi K3 max at 62.1) are counted separately.

My firm position: the local/cloud choice is not a question of performance, but of constraints.

• Choose local if: you need strict confidentiality (medical data, legal data, proprietary code), offline use, API costs are prohibitive at high volume, or simply on principle of sovereignty.
• Stay in the cloud if: you want the cutting edge of quality, you have limited RAM, or your usage is sporadic (in that case, see our best free LLMs — ChatGPT free, Gemini, Groq cover a lot of needs at €0).

The interesting compromise of 2026: hosting your open weights models on a GPU VPS rather than at home. You keep control of the weights and predictable billing, without investing €3,000 in a machine. This is exactly the use case for Hostinger for this type of workload — and it lets you access "your" models from anywhere, which a tower under your desk cannot do.

For the complete overview of cloud models, our monthly comparison of the best LLMs is the go-to reference.

Quantizations: what you really need to understand

Quantization reduces a model's weight by slightly degrading its precision — but not all formats are created equal. As of November 2026, three families dominate:

• GGUF (Q4_K_M and variants): the standard for llama.cpp and Ollama. A Gemma 4 31B drops from ~62 GB in BF16 to 17-20 GB in 4-bit. The quality loss is real but tolerable for most uses.
• MXFP4: the native format for large MoEs like gpt-oss-120b (64.6 GB) and Kimi K3.1 Pro. Better quality preservation than classic 4-bit, but more demanding hardware.
• NVFP4: NVIDIA's format optimized for DGX Spark. Nemotron 3.5 Super VL fits in 75 GB on a single 128 GB machine.

The classic pitfall: downloading an overly aggressive quantization (Q2, IQ1) to "squeeze" a large model onto small hardware. The result: a model that hallucinates more than it answers. A Q4 of a smaller model almost always beats a Q1 of a bigger one. DeepSeek-V4.1-Flash-1017 in UD-IQ1_S (233 GB) is a borderline case — even there, we're talking serious hardware.

❌ Common Mistakes

Mistake 1: Buying hardware for the trendy model

Kimi K3.1 Pro requires 1,561 GB natively. No one should buy a multi-GPU server "for a single model." The solution: first identify your actual hardware, then choose the best model that fits in it (see the table above).

Mistake 2: Confusing API scores with local execution

Rankings like BenchLeader mix open-weights models consumed via API with models actually run on your own machine. A Kimi K3 scoring 62.1 as "open weights" won't do you any good if you have 16 GB of RAM. Always check the model's weight before getting excited about a score.

Mistake 3: Neglecting the license

Not all models in this guide are legally equal. Gemma 4, Qwen3.8, Nanbeige4.3, Arete-27B, gpt-oss and DeepSeek V4.1 Flash are under Apache 2.0 or permissive licenses — perfect for commercial use. Always check the license before integrating a model into a product.

Mistake 4: Judging a model on a single benchmark

OpenWeights Bench tests 270 questions across 13 categories; Modelgrep and BenchLeader use their own indexes. The numbers can vary by a factor of two depending on the methodology. Always test your own use cases before making a final decision — it's 30 minutes that will save you weeks of frustration.

❓ Frequently Asked Questions

Which local LLM for 16 GB of RAM?

Gemma 4 12B IT, without hesitation: ~8.9 GB quantized, a score of 98.6 on the OpenWeights Bench, the best in its class. It runs comfortably via Ollama (gemma4:12b) or LM Studio on a standard laptop, while still leaving room for the system and your browser.

Can you really run Kimi K3.1 Pro at home?

Technically yes, practically no for an individual: 1,561 GB native in MXFP4 calls for professional multi-GPU servers. The reasonable alternative is its API at $2/M input tokens (November 2026), or MiMo-V2.6-Flash (167.4 GB across 2 DGX Spark) if you want top-tier performance self-hosted.

What is the best free local model?

All the open weights models mentioned here are free to download and run: the cost comes down to your hardware and electricity. Ollama with Qwen3.8 or Gemma 4 is the recommended free combo, with the usual limitations: a beefy machine is required and quality falls short of the best cloud models.

Which local model for tool calling and agents?

gpt-oss-20b is the reference choice according to Rüb Studio: lightweight MoE, excellent at tool calling, Apache 2.0 license. For more ambitious agents with large contexts, gpt-oss-120b (64.6 GB, score 98.6) is the next step up, if your hardware can handle it.

Are local LLMs catching up with GPT and Gemini?

They're closing in, without quite catching up: Gemini 4 Carbon (60) leads Kimi K3.1 Pro (46.5) by 13 points. But for everyday use — writing, summarization, routine coding, RAG — a Gemma 4 31B or a Qwen3.8-27B is more than enough, and the gap narrows every quarter.

✅ Conclusion

November 2026 marks a turning point: Kimi K3.1 Pro proves that open weights can come close to closed models, while Gemma 4 31B and 12B make excellent local AI accessible to almost every hardware budget. My advice: start with the Gemma 4 that fits your RAM via Ollama, and save the monsters like Kimi K3.1 Pro for the API — unless you enjoy servers that run hot. Come back next month: at this pace of releases (21 models in 10 weeks), the December ranking will probably look different.