Llama 3.2 3B
Meta's tiny open-weights workhorse — 3B parameters, 128K context, and tool use for edge and budget inference.
Get API key- Provider
- Meta
- Price
- $0.15 in · $0.60 out / 1M
- Context window
- 128K tokens
- Released
- September 25, 2024
- License
- Llama 3.2 Community License
What is Llama 3.2 3B?
Llama 3.2 3B is Meta's lightweight 3-billion-parameter multilingual language model, released in September 2024. Designed for edge devices and low-latency inference, it handles dialogue, summarization, and tool use with a 128K context window, and ships as open weights under the Llama 3.2 Community License.
Use it privately on Venice
On Venice, Llama 3.2 3B runs with zero retention — your prompts are not stored, profiled, or used to train downstream models. It supports tool use and web search, giving you agentic capabilities without the Big-Tech surveillance footprint. Because the weights are open, you get sovereignty over inference with full permissionless access.
What can it do?
- •Open weights under the Llama 3.2 Community License, enabling self-hosting, fine-tuning, and sovereignty.
- •Extremely low inference cost on Venice with native support for tool use and web search.
- •128K context window in a 3B parameter footprint — uncommon for this size class.
- •Multilingual text coverage suitable for global edge and mobile deployments.
- •Optimized for low-resource environments without sacrificing core dialogue and summarization quality.
- •Smaller parameter count limits complex reasoning, coding, and nuanced instruction following versus larger models.
- •Text-only — no vision capabilities, unlike the 11B and 90B Llama 3.2 variants.
- •Requires quantization or optimization for peak performance on consumer edge hardware.
- •Includes Meta safety alignment; it is not uncensored and may refuse certain requests.
Llama 3.2 3B capabilities
- Tool use / function calling
- Vision (image input)
- Reasoning
- Web search
- Code-optimized
- Structured output (JSON schema)
- Audio input
- Video input
- Multiple image inputs
- Log probabilities
How to use it via API
Venice exposes an OpenAI-compatible API. Swap your base URL and call llama-3.2-3b.
curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "llama-3.2-3b",
"messages": [{ "role": "user", "content": "Explain quantum tunneling simply." }]
}'Specifications
Pricing
Billed per token on Venice: $0.15 per 1M input tokens and $0.60 per 1M output tokens.
New Venice accounts include a free daily allowance and 500 welcome credits — no credit card required.
Llama 3.2 3B vs alternatives
| Model | Context window | Open weights | Price (Venice) | Best for |
|---|---|---|---|---|
| Llama 3.2 3B | 128K tokens | Yes | $0.15 / $0.60 | Edge & budget inference |
| DeepSeek V3.2 | 160K tokens | Yes | $0.33 / $0.48 | General reasoning |
| GLM 4.7 Flash Heretic | 200K tokens | Yes | $0.14 / $0.80 | Long-context open tasks |
| Kimi K2.6 | 256K tokens | Yes | $0.85 / $4.66 | Long-context workflows |
Tiny footprint, open weights, and tool use at the lowest per-token price on Venice.
What is it good for?
- •High-volume, low-cost chatbots and customer-assistance agents.
- •On-device summarization and rewriting for privacy-sensitive mobile workflows.
- •Agentic pipelines leveraging function calling and web search.
- •Multilingual edge applications on Arm, Qualcomm, and MediaTek hardware.
- •Rapid prototyping and A/B testing before scaling to larger checkpoints.
Prompting tips
- •Use explicit system prompts and structured instructions to improve reliability at small scale.
- •Leverage the 128K context for long-document retrieval and summarization tasks.
- •Define tool schemas precisely — the model supports function calling when formats are strict.
- •For local deployment, quantize the open weights to INT8 or INT4 to fit mobile memory constraints.
Version history
Smaller text-only sibling for ultra-low-resource devices.
Current — the most capable text-only lightweight variant.
CurrentMultimodal variant with image understanding capabilities.
Frequently asked questions
Llama 3.2 3B is Meta's lightweight 3-billion-parameter multilingual language model, released in September 2024. It is designed for edge and low-latency inference, supporting dialogue, summarization, tool use, and web search with a 128K context window.
Venice bills Llama 3.2 3B at $0.15 per 1M input tokens and $0.60 per 1M output tokens. There is no subscription required; you pay per token in credits.
It is open weights under the Llama 3.2 Community License, meaning you can download, fine-tune, and self-host the model. It is not fully open source in the OSI-approved sense because the license carries some restrictions for very large deployers.
Yes. On Venice, Llama 3.2 3B supports function calling and web search, allowing you to build agentic workflows that invoke external tools or retrieve live information.
Choose Llama 3.2 3B for edge deployment, maximum cost efficiency, and high-volume text tasks. Choose DeepSeek V3.2 for stronger general reasoning, a larger parameter count, and a 160K context window when budget is less constrained.
Summarization, multilingual dialogue, on-device inference, agentic retrieval with tool use, and any workflow where low latency and low cost matter more than frontier reasoning depth.
No. Llama 3.2 3B includes Meta's safety alignment and may refuse certain requests. It is not an uncensored model.
It runs under Venice's private, zero-retention tier. Your prompts are not stored, profiled, or used for training, and no personal conversation history is built.
Related models
Run Llama 3.2 3B privately.
No prompt logging. No data used for training. Free to start — no credit card.
