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Llama 3.3 70B Instruct

Llama 3.3 70B Instruct is Meta's multilingual 128K-context instruction model, released December 6, 2024 under the Llama 3.3 Community License.

General Purpose Language ModelText Free
In plain English

What is this model and why does it matter?

Llama 3.3 70B Instruct is Meta's multilingual 70B instruction model with 128K context and a December 2023 knowledge cutoff.

Large self-hosted assistantsMultilingual chatCodingRAGSynthetic data
Model overview

Llama 3.3 70B Instruct: features, use cases and important details

Llama 3.3 70B Instruct is Meta’s instruction-tuned 70B text model released on December 6, 2024.

Verified model facts

Meta documents a 128K context window, December 2023 pretraining cutoff and official support for eight languages. The weights are governed by the Llama 3.3 Community License.

Best fit

It is best suited to large self-hosted assistants, multilingual chat, coding, RAG and tool-using workflows.

Limitations

It requires substantial GPU infrastructure, has static knowledge and can still hallucinate or make incorrect tool decisions.

Get started

How to use this model

  1. Accept the Llama 3.3 Community License.
  2. Download the official checkpoint.
  3. Provision suitable multi-GPU infrastructure.
  4. Use the official chat format and supported tool-use pattern.
  5. Benchmark accuracy, latency and safety before production.
Copy and try

Example prompts

  • Analyze this long report and identify conflicting claims.
  • Write and explain a robust API integration.
  • Answer this multilingual customer query using retrieved evidence.
Capabilities

What it can do

  • 128K context
  • Multilingual dialogue
  • Reasoning
  • Coding
  • Tool use
Best for

Practical use cases

  • Enterprise self-hosting
  • RAG
  • Coding assistants
  • Multilingual agents
  • Research
Pricing

What does it cost?

Open-weight Meta checkpoint under the Llama 3.3 Community License; hosting cost depends on infrastructure.

Simple summaryNo Meta per-token price applies to the downloadable checkpoint; 70B serving generally requires substantial GPU resources.

What stands out

  • Strong 70B capability
  • 128K context
  • Official Meta weights
  • Tool-use support

Things to consider

  • High hardware cost
  • Custom license
  • December 2023 static knowledge
Limitations

Important restrictions and trade-offs

  • Can hallucinate
  • Requires significant infrastructure
  • No live knowledge without retrieval
SimplifyAITools verdict

Our editorial take

A capable open-weight 70B model for teams that want control over deployment and can support the infrastructure required.

References

Primary sources

  1. Open source 1 ↗
  2. Open source 2 ↗
  3. Open source 3 ↗
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