# Llama 3.3 70B Instruct

Meta's refined 70B text model targets 405B-class results at 70B serving cost, with gains in instruction following and multilingual quality.

- **Model ID:** `meta/llama-3.3-70b`
- **Type:** chat
- **Providers:** bedrock
- **Context window:** 128,000
- **Maximum output tokens:** 8,192
- **Pricing:** $0.72/1M input tokens, $0.72/1M output tokens
- **Canonical page:** https://vercel.com/ai-gateway/models/llama-3.3-70b

## Supported parameters

Detailed capability metadata has not been reported for this model.

## Example

```ts
import { streamText } from 'ai'

const result = streamText({
  model: 'meta/llama-3.3-70b',
  prompt: 'Why is the sky blue?'
})
```

## About

Meta released Llama 3.3 70B Instruct on December 6, 2024 as the final model in its 2024 Llama release cadence. The 3.3 70B is text-only, but it represents a targeted refinement of the 70B tier. Llama 3.3 70B Instruct delivers similar performance to the 3.1 405B at a fraction of the serving cost.

The core improvements center on instruction following and multilingual capability. Instruction following (the model's ability to accurately execute detailed or constrained directions) is one of the most important capabilities in production deployments where system prompts encode complex behavioral rules. The multilingual improvements matter for enterprise applications serving global audiences: better handling of non-English instructions reduces the engineering overhead of maintaining separate language-specific prompts.

Llama Stack, which Meta standardized throughout 2024 as a set of interfaces for RAG and agentic applications, is fully compatible with the 3.3 70B. Teams already using Llama Stack distributions for toolchain orchestration can upgrade to the 3.3 generation without rearchitecting their integration layer.

## What to consider

If you're migrating from Llama 3.1 70B, test your existing prompts against 3.3 70B before you switch. Improved instruction following can change output style enough that you'll adjust prompts. Compare $0.72 and $0.72.

## When to use

### Best For

- Applications that previously required the 405B for output quality but where serving a 400B+ parameter model is economically prohibitive
- Workloads with complex, detailed system prompts that depend on precise instruction following, customer support bots, structured data extraction, multi-step reasoning chains
- Multilingual production deployments where improved non-English instruction handling reduces prompt engineering overhead
- Teams upgrading from Llama 3.1 70B who want clear quality gains without moving to a larger, more expensive model

### Consider Alternatives When

- Vision or image understanding is part of the task: 3.3 70B is text-only, and Llama 3.2 90B handles multimodal inputs
- The use case requires the absolute maximum reasoning depth and cost is not the constraint: Llama 3.1 405B remains the largest open model
- Native multimodal architecture (rather than adapter-based vision) is a technical requirement: consider Llama 4 Maverick or Scout

## Best for

- **405B quality at 70B cost:** Applications that previously required the 405B for output quality but where serving a 400B+ parameter model is economically prohibitive
- **Complex system prompts:** Workloads depending on precise instruction following for customer support bots, structured data extraction, and multi-step reasoning chains
- **Multilingual production deployments:** Improved non-English instruction handling reduces prompt engineering overhead
- **Upgrading from 3.1 70B:** Teams that want clear quality gains without moving to a larger, more expensive model

## Consider alternatives

- **Vision required:** Image understanding is part of the task and 3.3 70B is text-only, so Llama 3.2 90B handles multimodal inputs
- **Maximum reasoning depth:** Cost is not the constraint and Llama 3.1 405B remains the largest open model
- **Native multimodal architecture:** Rather than adapter-based vision, consider Llama 4 Maverick or Scout

## Frequently asked questions

### What specifically improved in Llama 3.3 70B Instruct over Llama 3.1 70B?

Instruction following quality and multilingual capabilities. Llama 3.3 70B Instruct delivers performance comparable to the much larger 3.1 405B, with refinements in how the model handles detailed and constrained instructions.

### Is Llama 3.3 70B Instruct a drop-in upgrade from 3.1 70B?

Architecturally, yes. But improved instruction following means outputs may differ in style or format compared to 3.1 70B for the same prompts. Run regression tests against existing prompts before switching production workloads.

### Does Llama 3.3 70B Instruct support vision inputs?

No. It is a text-only model. For multimodal workflows at the 70B scale, Llama 3.2 90B (adapter-based vision) or Llama 4 Maverick (natively multimodal) are the appropriate alternatives.

### How does Llama 3.3 70B Instruct relate to the broader Llama ecosystem tooling?

Fully compatible with Llama Stack distributions, which provide standardized interfaces for RAG and agentic application development.

## Links

- [Model page](https://vercel.com/ai-gateway/models/llama-3.3-70b)
- [AI Gateway documentation](https://vercel.com/docs/ai-gateway)
- [Provider model documentation](https://aws.amazon.com/bedrock/meta/)
- [Provider pricing](https://aws.amazon.com/bedrock/pricing/)


If you encounter incorrect information, can’t find what you need, need a capability Vercel doesn’t currently offer, or have any other feedback to report, please do it by following the instructions at https://vercel.com/feedback.md.