AI model by Meta
Llama 3.3 70B for business
An older, text-only open-weight Llama model with tool use and a 128K token window, still widely hosted and well understood.
- Open weights
- General purpose
- Open weights
- Reviewed on September 24, 2026
- Meta
Capability tiersRelative, not benchmarks
Key facts about Llama 3.3 70B
- Model ID at review
- Llama-3.3-70B-Instruct
- Provider
- Meta
- Main category
- Open weights
- Open weights
- Yes, Llama 3.3 Community License
- Last reviewed
- September 24, 2026
Fit
Where it fits and where it does not
Good at
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Text tasks on your own infrastructure.
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Tool use, as listed in the model card.
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Eight supported languages.
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Stable, well-documented behavior.
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Broad support in hosting tools and frameworks.
Not the right choice for
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Images, since it is text only.
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New projects, where newer open models are worth testing first.
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Inputs longer than 128K tokens.
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The hardest reasoning tasks.
Use cases
Business use cases we would use it for
Existing self-hosted systems
Private classification
Private summaries
Our notes
When we would choose it
Llama 3.3 70B was released in December 2024 as a text-only open-weight model. Its model card lists support for eight languages, tool use and a 128K token context window, under the Llama 3.3 Community License. It is still published and widely hosted.
It remains a reasonable option for self-hosted systems that already run it and meet their goals. Its behavior is well understood, and most hosting tools support it. For new projects, though, we would start with newer open models. Gemma 4, Mistral Small and Llama 4 Scout add image input and larger windows, and often need less hardware for similar quality.
The move from one open model to another is usually straightforward if the integration was built with that in mind. Keep prompts, tests and the model name in configuration, and run your test set whenever a new candidate appears.
A 70B model is not small. Running it well takes substantial GPU memory, and serving many users at once takes more. Before investing in hardware, try the model through a hosting provider on your real workload and measure speed, quality and cost.
If your main reason for self-hosting is data control, check whether a hosted model with strong business terms and a regional endpoint would meet your rules. Sometimes it does, and it is simpler to run. See security and IP ownership and open weights models.
Before you commit
Things to check before you commit
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Newer options
Compare with Llama 4, Gemma 4 and Mistral Small on the same tasks.
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License
Read the Llama 3.3 Community License for your use.
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Hardware
A 70B model needs substantial GPU memory.
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Languages
The model card lists eight languages. Test others carefully.
Alternatives
Models to compare it with
Llama 4 Scout
An open-weight Llama 4 model with image input and a very long context window, for teams that want to host a capable model themselves.
Gemma 4
Google's open-weight model family under Apache 2.0, in sizes from phone-friendly to 31B, with image input and function calling.
Mistral Small 4
Mistral's efficient open-weight model under Apache 2.0, one hybrid model for instructions, reasoning and coding, with tool calling and a 256K window.
Keep exploring
Solutions, services and guides
Related services
View all related services- LLM integration Add a large language model to software you already have, with the guardrails, costs and logging handled.
- AI model evaluation Test candidate AI models on your real data and tasks, then pick the one that balances quality, speed and cost.
- AI Product Development AI agents, knowledge assistants, copilots and document automation built into the way your team already works.
- AI agents AI that takes actions in your systems, such as qualifying leads or processing requests, with people checking the results.
- AI copilots AI helpers built into your product or internal tools that draft, summarize and suggest while people decide.
- AI workflows Step-by-step automations where AI handles the reading, sorting and drafting inside a process you control.
Solutions
View all solutions- Email triage Sort a shared inbox by topic and urgency, pull out the key details and draft replies for a person to send.
- Knowledge assistant (RAG) Ask a question in plain words and get an answer from your own documents, with links to the sources.
- AI-assisted content workflows Briefs, first drafts, edits and repurposing in a workflow where people set the angle and approve every word.
- AI translation workflows Translate websites, products and support content quickly with AI, a shared glossary and native-speaker review where it matters.
- Feedback analysis Read every review, survey and ticket, group them by theme and sentiment, and show what customers keep asking for.
- Screening assistant Summarize each application against your criteria, flag strong matches and missing information, and leave every decision to a person.
Industries
View all industries- Legal Contract review assistants, knowledge search, intake and document automation for law firms and in-house legal teams.
- Education and elearning Course platforms, student portals, content workflows and AI tutoring assistants for schools and training companies.
- Manufacturing Quality inspection, production dashboards, quoting tools, knowledge assistants and legacy system modernization for manufacturers.
Case studies
View all case studiesGuides and articles
View all guides and articles- How to choose an AI model for your business A step-by-step way to pick an AI model by testing candidates on your own tasks, data rules and budget.
- RAG vs fine-tuning Two ways to make AI work with your knowledge, compared by cost, accuracy and upkeep.
- What is RAG and when do you need it? How retrieval-augmented generation lets AI answer from your documents, when it fits and how to build it well.
- How to keep customer data safe when using AI Practical steps to protect customer data when you use AI services, from data terms to access and logging.
- Prompt writing basics for business teams How to write prompts that get consistent, useful results from AI tools, with templates your team can reuse.
MCP servers
View all mcp serversGlossary terms
View all glossary terms- Large language model A large language model, or LLM, is an AI model trained on vast amounts of text that can understand and generate language, and often images and code.
- Machine learning Machine learning is a way of building software that learns patterns from data to make predictions or decisions, instead of following only hand-written rules.
- Fine-tuning Fine-tuning is further training an existing AI model on your own examples so it learns a specific style, format or task.
- Inference Inference is the step where a trained AI model is used to produce an output, such as an answer, a label or a prediction, from new input.
- Open-weights model An open-weights model is an AI model whose trained parameters are published, so anyone can download and run it on their own hardware under its license.
- Token A token is a small piece of text, often part of a word, that AI models read and write, and that providers use to measure limits and pricing.
FAQ
Questions people ask us
Have a question that is not here? Ask us directly.
No. It is a text-only model.
Yes. The weights are still published, and many hosts offer it.
Test newer open models such as Gemma 4, Mistral Small and Llama 4 on your own tasks.