Llama 3.3 70B Instruct (free)

Llama 3.3 70B Instruct (free) by Meta excels in the Coding category, where it ranks #166 with a composite score of 46/100. The Meta Llama 3.3 multilingual large language model (LLM) is a pretrained and instruction tuned generative model in 70B (text in/text out). The Llama 3.3 instruction tuned text only model is optimized for multilingual dialogue use cases and outperforms many of the available open source and closed chat models on common industry benchmarks. Supported languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. [Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_3/MODEL_CARD.md) It is particularly strong in areas highlighted by its top benchmark performance and adoption metrics, making it suitable for both individual developers and enterprise teams looking for a reliable coding solution.

Llama 3.3 70B Instruct (free) is priced at $0.00 per million input tokens and $0.00 per million output tokens (USD). Contact the provider for volume discounts and enterprise pricing. Pricing is competitive within the coding category and reflects the model's quality-to-cost ratio.

In the Coding category, Llama 3.3 70B Instruct (free) holds rank #166 out of 6 models tracked. Its quality rank is #166 and adoption rank is #166. You can use our comparison tool at /compare to see detailed side-by-side metrics with specific alternatives. Key differentiators include its composite scoring across benchmarks, community sentiment, and real-world adoption rates.

Llama 3.3 70B Instruct (free) has been evaluated across 6 different signals. Its strongest areas include Capabilities (29/100), Context Window (81/100), Output Capacity (85/100). These scores are derived from industry-standard benchmarks, community ratings, and real-world performance metrics. The composite score of 46/100 reflects a weighted combination of all tracked signals.

Llama 3.3 70B Instruct (free) is available as a free, open-source model. You can run it locally or use it through various hosting providers. Some API providers may charge for hosted inference, so check individual provider pricing.