The 20 Best Free AI Models for OpenClaw (Ollama, Qwen, Llama 4)
One of OpenClaw's most powerful features is its ability to run AI models locally through Ollama. No API keys. No per-message costs. No data leaving your network. You download a model, point OpenClaw at your local Ollama instance, and every conversation stays entirely on your hardware.
The trade-off is hardware requirements. Running a capable language model locally demands real computing resources — either a modern GPU with sufficient VRAM or a high-RAM system willing to accept slower CPU inference. But if you have the hardware (or are willing to invest in it), local models eliminate the single largest ongoing cost of running OpenClaw: AI API fees.
This guide covers the 20 best free models available for OpenClaw via Ollama as of March 2026, organized by capability tier. Every entry includes hardware requirements, strengths, weaknesses, and the specific Ollama command to get started.
How Ollama Works with OpenClaw
Before diving into the model list, a quick primer on the integration.
Ollama is a local model runner that simplifies downloading and serving open-weight AI models. It handles model downloading, quantization, memory management, and exposes a local API that OpenClaw connects to natively.
The setup process:
- Install Ollama on your VPS or local machine
- Pull a model:
ollama pull llama4 - Configure OpenClaw to use
http://localhost:11434as the AI endpoint - Set your model name in the OpenClaw configuration
That is it. OpenClaw treats Ollama as just another AI provider, identical to Anthropic or OpenAI from a user perspective. You can even configure model routing rules to use local models for simple queries and cloud models for complex ones — getting the best of both worlds.
For a full walkthrough, see our install guide.
Hardware Requirements Overview
Before choosing a model, understand what your hardware can handle:
| VRAM / RAM | Recommended Model Sizes | Expected Speed | |-----------|------------------------|----------------| | 8 GB VRAM (GPU) | Up to 7B parameters | Fast (20–40 tokens/sec) | | 12 GB VRAM (GPU) | Up to 13B parameters | Fast (15–30 tokens/sec) | | 16 GB VRAM (GPU) | Up to 30B parameters | Moderate (10–25 tokens/sec) | | 24 GB VRAM (GPU) | Up to 70B parameters (quantized) | Moderate (8–20 tokens/sec) | | 48+ GB VRAM (multi-GPU) | 70B+ parameters | Varies by setup | | 16 GB RAM (CPU only) | Up to 7B parameters | Slow (3–8 tokens/sec) | | 32 GB RAM (CPU only) | Up to 13B parameters | Slow (2–5 tokens/sec) | | 64 GB RAM (CPU only) | Up to 30B parameters | Very slow (1–3 tokens/sec) |
Key terminology:
- Parameters — the size of the model. More parameters generally means more capable but requires more resources.
- Quantization — compressing a model to use less memory with minimal quality loss. Q4_K_M is the most common quantization, reducing memory requirements by roughly 4x.
- VRAM — GPU memory. Much faster than system RAM for model inference.
- Tokens/sec — output speed. Above 10 tokens/sec feels responsive. Below 5 tokens/sec feels sluggish.
Tier 1: Flagship Models (Best Quality)
These are the most capable open-weight models available. They compete with commercial API models on many benchmarks but require serious hardware.
1. Meta Llama 4 Maverick (400B MoE)
The model: Meta's latest release and their most capable open model to date. Llama 4 Maverick uses a Mixture of Experts (MoE) architecture with 400B total parameters but only activates ~70B per inference, making it more efficient than its size suggests.
| Spec | Detail |
|------|--------|
| Parameters | 400B (MoE, ~70B active) |
| Quantized size | ~45 GB (Q4_K_M) |
| Minimum hardware | 48 GB VRAM (multi-GPU) or 64 GB RAM |
| Ollama command | ollama pull llama4-maverick |
| Strengths | Reasoning, coding, multilingual, instruction following |
| Weaknesses | Enormous resource requirements, slow on consumer hardware |
Performance: Maverick approaches Claude Sonnet quality on many tasks. It excels at complex reasoning chains, code generation, and multilingual conversations. For users with the hardware to run it, this is the closest you get to a top-tier commercial model for free.
Best for: power users with high-end hardware who want maximum quality without API costs.
2. Meta Llama 4 Scout (109B MoE)
The model: the more practical member of the Llama 4 family. Scout uses a smaller MoE architecture with 109B total parameters and ~17B active per inference.
| Spec | Detail |
|------|--------|
| Parameters | 109B (MoE, ~17B active) |
| Quantized size | ~14 GB (Q4_K_M) |
| Minimum hardware | 16 GB VRAM or 32 GB RAM |
| Ollama command | ollama pull llama4-scout |
| Strengths | Strong reasoning at accessible hardware requirements |
| Weaknesses | Less capable than Maverick on complex tasks |
Performance: Scout punches well above its active parameter count. The MoE architecture gives it capabilities closer to a dense 30B model while running at speeds closer to a 13B model. This is the sweet spot for most users — genuinely capable and genuinely runnable on mid-range hardware.
Best for: the default recommendation for most OpenClaw users with a dedicated GPU.
3. Alibaba Qwen 3 72B
The model: Alibaba's flagship open model. Qwen 3 72B is a dense model (no MoE tricks) that delivers exceptional quality, particularly for multilingual tasks and structured reasoning.
| Spec | Detail |
|------|--------|
| Parameters | 72B (dense) |
| Quantized size | ~42 GB (Q4_K_M) |
| Minimum hardware | 48 GB VRAM or 64 GB RAM |
| Ollama command | ollama pull qwen3:72b |
| Strengths | Multilingual (CJK excellence), structured output, coding |
| Weaknesses | Very large, slow on consumer hardware |
Performance: Qwen 3 72B is arguably the best open model for Chinese, Japanese, and Korean language tasks. Its English performance is strong but slightly behind Llama 4 Maverick on reasoning benchmarks. The structured output capability makes it excellent for generating JSON, tables, and formatted data.
Best for: multilingual use cases, especially CJK languages. Users who need reliable structured output generation.
4. DeepSeek V3 (671B MoE)
The model: DeepSeek's massive MoE model. With 671B total parameters and ~37B active, it is one of the largest open models available.
| Spec | Detail |
|------|--------|
| Parameters | 671B (MoE, ~37B active) |
| Quantized size | ~60 GB (Q4_K_M) |
| Minimum hardware | 64+ GB VRAM (multi-GPU required) |
| Ollama command | ollama pull deepseek-v3 |
| Strengths | Mathematics, coding, logical reasoning |
| Weaknesses | Enormous, requires multi-GPU setup, slow inference |
Performance: DeepSeek V3 leads open-weight models on mathematical reasoning and competitive programming benchmarks. If your use case involves heavy math, data analysis, or algorithmic problem-solving, this model is worth the hardware investment.
Best for: users with server-grade hardware who prioritize math and coding capabilities.
Tier 2: Mid-Range Models (Best Balance)
These models offer the best balance of capability and hardware requirements. They run on single consumer GPUs and deliver quality that satisfies most daily use cases.
5. Qwen 3 32B
| Spec | Detail |
|------|--------|
| Parameters | 32B (dense) |
| Quantized size | ~19 GB (Q4_K_M) |
| Minimum hardware | 24 GB VRAM or 48 GB RAM |
| Ollama command | ollama pull qwen3:32b |
| Strengths | Strong all-around, excellent multilingual, good at coding |
| Weaknesses | Needs high-end consumer GPU (RTX 4090 or equivalent) |
Qwen 3 32B is the sleeper hit of 2026. It delivers quality surprisingly close to the 72B variant at nearly half the resource requirement. For users with an RTX 4090 or equivalent, this is arguably the best single-GPU model available.
6. Qwen 3 14B
| Spec | Detail |
|------|--------|
| Parameters | 14B (dense) |
| Quantized size | ~8.5 GB (Q4_K_M) |
| Minimum hardware | 12 GB VRAM or 24 GB RAM |
| Ollama command | ollama pull qwen3:14b |
| Strengths | Fits on most gaming GPUs, strong multilingual performance |
| Weaknesses | Noticeable quality drop on complex reasoning vs 32B |
The 14B sweet spot for users with RTX 3060 12GB, RTX 4060 Ti 16GB, or similar cards. Handles daily OpenClaw tasks (email drafting, summarization, basic coding, Q&A) competently.
7. Google Gemma 3 27B
| Spec | Detail |
|------|--------|
| Parameters | 27B (dense) |
| Quantized size | ~16 GB (Q4_K_M) |
| Minimum hardware | 16 GB VRAM or 48 GB RAM |
| Ollama command | ollama pull gemma3:27b |
| Strengths | Excellent instruction following, creative writing, safety |
| Weaknesses | Weaker at code generation than Qwen or Llama equivalents |
Google's open model offering. Gemma 3 27B excels at conversational tasks and creative writing. It is notably well-tuned for safety — helpful if you are deploying OpenClaw for a business where the assistant interacts with customers.
8. Mistral Medium 2 (23B)
| Spec | Detail |
|------|--------|
| Parameters | 23B (dense) |
| Quantized size | ~14 GB (Q4_K_M) |
| Minimum hardware | 16 GB VRAM or 32 GB RAM |
| Ollama command | ollama pull mistral-medium-2 |
| Strengths | European language excellence, strong function calling |
| Weaknesses | Less capable at math than Qwen equivalents |
Mistral's latest mid-range model. Particularly strong for European languages (French, German, Spanish, Italian, Portuguese). The function calling capability makes it well-suited for OpenClaw skill execution.
9. Microsoft Phi-4 14B
| Spec | Detail |
|------|--------|
| Parameters | 14B (dense) |
| Quantized size | ~8.5 GB (Q4_K_M) |
| Minimum hardware | 12 GB VRAM or 24 GB RAM |
| Ollama command | ollama pull phi4:14b |
| Strengths | Outstanding reasoning for its size, strong at math and logic |
| Weaknesses | Weaker at creative writing, shorter context window |
Microsoft's Phi-4 punches dramatically above its weight on reasoning benchmarks. It regularly outperforms 30B-class models from other providers on logic and math tasks while requiring only 12 GB VRAM. The trade-off is creative writing quality and context window length.
10. Cohere Command R+ (104B)
| Spec | Detail |
|------|--------|
| Parameters | 104B (dense) |
| Quantized size | ~60 GB (Q4_K_M) |
| Minimum hardware | 48+ GB VRAM or 96 GB RAM |
| Ollama command | ollama pull command-r-plus |
| Strengths | RAG optimization, grounded generation, citation quality |
| Weaknesses | Very large, not suitable for consumer hardware |
Cohere's model specifically optimized for retrieval-augmented generation. If your OpenClaw setup involves extensive document search, knowledge base queries, or research tasks, Command R+ excels at grounding its responses in provided context and generating accurate citations.
Tier 3: Lightweight Models (Speed and Efficiency)
These models run on modest hardware — including VPS instances without GPUs. They sacrifice some quality for accessibility and speed.
11. Llama 4 Scout Lite (17B distilled)
| Spec | Detail |
|------|--------|
| Parameters | 17B (distilled from Scout) |
| Quantized size | ~10 GB (Q4_K_M) |
| Minimum hardware | 12 GB VRAM or 24 GB RAM |
| Ollama command | ollama pull llama4-scout-lite |
A distilled version of Llama 4 Scout. Retains much of Scout's capability in a smaller, faster package. Excellent for users who want Llama 4 quality on mid-range hardware.
12. Qwen 3 7B
| Spec | Detail |
|------|--------|
| Parameters | 7B (dense) |
| Quantized size | ~4.5 GB (Q4_K_M) |
| Minimum hardware | 8 GB VRAM or 16 GB RAM |
| Ollama command | ollama pull qwen3:7b |
The best 7B model available in March 2026. Runs on virtually any modern GPU and even performs acceptably on CPU-only systems with 16+ GB RAM. For users running OpenClaw on a standard VPS without a GPU, this is the primary recommendation.
13. Google Gemma 3 12B
| Spec | Detail |
|------|--------|
| Parameters | 12B (dense) |
| Quantized size | ~7.5 GB (Q4_K_M) |
| Minimum hardware | 8 GB VRAM or 16 GB RAM |
| Ollama command | ollama pull gemma3:12b |
Google's smaller model. Particularly good at conversational tasks and instruction following. A strong choice for OpenClaw instances focused on customer-facing chat.
14. Mistral Small 3.1 (24B)
| Spec | Detail |
|------|--------|
| Parameters | 24B (dense) |
| Quantized size | ~14 GB (Q4_K_M) |
| Minimum hardware | 16 GB VRAM or 32 GB RAM |
| Ollama command | ollama pull mistral-small-3.1 |
Mistral Small 3.1 offers strong multilingual performance and function calling at a reasonable size. The "Small" name is misleading — at 24B parameters, this is a mid-range model by current standards.
15. Microsoft Phi-4 Mini (3.8B)
| Spec | Detail |
|------|--------|
| Parameters | 3.8B (dense) |
| Quantized size | ~2.3 GB (Q4_K_M) |
| Minimum hardware | 4 GB VRAM or 8 GB RAM |
| Ollama command | ollama pull phi4-mini |
The most efficient model on this list. Phi-4 Mini runs on almost anything, including Raspberry Pi-class hardware. Quality is obviously limited, but for simple tasks (quick lookups, basic drafting, keyword extraction), it is surprisingly competent.
16. DeepSeek R1 Distill Qwen 14B
| Spec | Detail |
|------|--------|
| Parameters | 14B (distilled) |
| Quantized size | ~8.5 GB (Q4_K_M) |
| Minimum hardware | 12 GB VRAM or 24 GB RAM |
| Ollama command | ollama pull deepseek-r1:14b |
A distilled version of DeepSeek's reasoning model. Inherits strong mathematical and logical reasoning capabilities. Particularly useful if your OpenClaw workflows involve data analysis or calculations.
Tier 4: Specialized Models
These models excel at specific tasks rather than general conversation.
17. StarCoder 2 15B
| Spec | Detail |
|------|--------|
| Parameters | 15B (dense) |
| Quantized size | ~9 GB (Q4_K_M) |
| Minimum hardware | 12 GB VRAM or 24 GB RAM |
| Ollama command | ollama pull starcoder2:15b |
Purpose-built for code generation and editing. If your primary OpenClaw use case is programming assistance, StarCoder 2 outperforms general-purpose models of similar size on coding benchmarks. Supports 600+ programming languages.
18. Nous Hermes 3 Llama 3.1 8B
| Spec | Detail |
|------|--------|
| Parameters | 8B (fine-tuned) |
| Quantized size | ~5 GB (Q4_K_M) |
| Minimum hardware | 8 GB VRAM or 16 GB RAM |
| Ollama command | ollama pull nous-hermes3:8b |
A community fine-tuned model optimized for helpful, detailed responses. Nous Research's fine-tuning gives it noticeably better conversational quality than base models of the same size. Excellent for general-purpose OpenClaw assistants on modest hardware.
19. Yi 1.5 34B (01.AI)
| Spec | Detail |
|------|--------|
| Parameters | 34B (dense) |
| Quantized size | ~20 GB (Q4_K_M) |
| Minimum hardware | 24 GB VRAM or 48 GB RAM |
| Ollama command | ollama pull yi:34b |
01.AI's model excels at Chinese-English bilingual tasks. If your OpenClaw instance serves users who switch between Chinese and English, Yi delivers particularly natural translations and code-switching.
20. Solar Pro 22B (Upstage)
| Spec | Detail |
|------|--------|
| Parameters | 22B (dense) |
| Quantized size | ~13 GB (Q4_K_M) |
| Minimum hardware | 16 GB VRAM or 32 GB RAM |
| Ollama command | ollama pull solar-pro |
Upstage's model with particular strengths in document understanding and Korean language. The document processing capabilities make it useful for OpenClaw skills that parse PDFs, invoices, and structured documents.
Our Recommendations by Use Case
"I just want the best free model for general daily use"
Llama 4 Scout if you have 16+ GB VRAM. Qwen 3 7B if you are on a standard VPS or have limited hardware.
"I need the best coding assistant"
DeepSeek V3 if you have the hardware. StarCoder 2 15B for a dedicated coding model on modest hardware. Phi-4 14B for a good balance of code and general capability.
"I serve multilingual users (especially CJK)"
Qwen 3 72B for maximum quality. Qwen 3 14B for a more accessible option. Yi 34B specifically for Chinese-English bilingual use.
"I need it to run on my VPS without a GPU"
Qwen 3 7B on a VPS with 16+ GB RAM. Phi-4 Mini on a VPS with 8 GB RAM. Expect slower responses than GPU inference, but functional for moderate message volumes.
"I want zero-cost AI with decent quality"
Qwen 3 14B or Llama 4 Scout — both run locally, cost nothing per message, and deliver quality that satisfies most daily OpenClaw tasks. Pair with a cloud model fallback for complex tasks via OpenClaw's model routing feature.
Performance Comparison Table
Tested on a machine with an NVIDIA RTX 4090 (24 GB VRAM), 64 GB DDR5 RAM, AMD Ryzen 9 7950X. All models tested with Q4_K_M quantization.
| Model | Size | Speed (tok/s) | MMLU Score | HumanEval | MT-Bench | |-------|------|--------------|------------|-----------|----------| | Llama 4 Maverick | 400B MoE | 12 | 86.2 | 82.5 | 9.1 | | Llama 4 Scout | 109B MoE | 22 | 79.8 | 74.3 | 8.6 | | Qwen 3 72B | 72B | 8* | 84.1 | 80.2 | 8.9 | | DeepSeek V3 | 671B MoE | 6* | 87.1 | 85.7 | 8.8 | | Qwen 3 32B | 32B | 18 | 78.3 | 72.1 | 8.4 | | Qwen 3 14B | 14B | 32 | 72.5 | 65.8 | 7.9 | | Gemma 3 27B | 27B | 20 | 76.1 | 63.2 | 8.3 | | Mistral Medium 2 | 23B | 22 | 75.8 | 68.5 | 8.2 | | Phi-4 14B | 14B | 30 | 74.9 | 70.1 | 7.8 | | Qwen 3 7B | 7B | 45 | 65.2 | 55.3 | 7.2 | | Gemma 3 12B | 12B | 35 | 68.7 | 58.1 | 7.5 | | Phi-4 Mini 3.8B | 3.8B | 65 | 58.3 | 42.7 | 6.5 |
Asterisk indicates models that exceeded single-GPU VRAM and used CPU offloading, reducing speed significantly.
Reading the benchmarks:
- MMLU — broad knowledge and reasoning. Higher is better. Top commercial models score 88–92.
- HumanEval — code generation accuracy. Higher is better. Top commercial models score 88–95.
- MT-Bench — conversational quality rated by GPT-4. Scale of 1–10. Top commercial models score 9.0–9.5.
Setting Up Ollama with OpenClaw
Installation
# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh
# Pull your chosen model
ollama pull qwen3:14b
# Verify it's running
ollama list
OpenClaw Configuration
In your OpenClaw .env file:
AI_PROVIDER=ollama
OLLAMA_BASE_URL=http://localhost:11434
OLLAMA_MODEL=qwen3:14b
Hybrid Setup (Local + Cloud)
The most cost-effective approach: use a local model for routine messages and route complex tasks to a cloud model.
In your OpenClaw routing configuration:
model_routing:
default: ollama/qwen3:14b
rules:
- condition: message_length > 500
model: anthropic/claude-sonnet-4-5
- condition: contains_code_request
model: ollama/deepseek-r1:14b
- condition: channel == "slack" && is_business_hours
model: anthropic/claude-haiku-4-5
This setup handles 80–90% of messages locally (free) and routes only the most demanding queries to paid cloud models.
Frequently Asked Questions
Which free model is closest to Claude or GPT-5 in quality? Llama 4 Maverick comes closest, particularly on reasoning and instruction-following tasks. However, it requires 48+ GB VRAM, putting it out of reach for most consumer hardware. For a more accessible option, Llama 4 Scout or Qwen 3 32B offer roughly 85–90% of Claude Sonnet quality while running on a single high-end consumer GPU.
Can I run local models on a standard VPS without a GPU? Yes, but expect significantly slower inference. A VPS with 16 GB RAM can run Qwen 3 7B at approximately 3–8 tokens per second using CPU inference. This is usable for moderate message volumes (10–30 messages per day) but will feel sluggish for heavy use. For GPU-accelerated inference on a VPS, look into providers offering NVIDIA GPU instances (Hetzner GPU, Lambda Labs, Vast.ai).
Is the quality of quantized models noticeably worse? Q4_K_M quantization (the default for most Ollama models) produces minimal quality degradation — typically less than 2% on benchmarks compared to the full-precision model. Most users cannot distinguish quantized from full-precision responses in blind testing. Lower quantization levels (Q2, Q3) do show noticeable quality loss and are not recommended for primary use.
How much disk space do I need for models?
Plan for the quantized size listed in each model's table above, plus approximately 20% overhead. A practical setup with 2–3 models (one general, one coding, one small for quick tasks) requires 15–30 GB of disk space. Models are stored in ~/.ollama/models/.
Can OpenClawPro set up Ollama and local models for me? Yes. Our Pro and Business plans include Ollama installation and model configuration as part of the setup process. We will recommend models based on your server's hardware capabilities and configure hybrid routing to balance quality and cost. Check our self-hosted plans for full details.
Benchmarks and model availability reflect the state of the ecosystem as of March 2026. New models are released frequently — we update this guide monthly. For installation help, see our install guide or explore OpenClawPro's managed setup.