llama-3.3-70b-instruct

Llama 3.3 70B Instruct is Meta's refined 70B open-weights model, delivering strong performance through improved training data and fine-tuning relative to its parameter count. It supports a 128K-token context window, tool use, and streaming. As one of the most widely deployed open-weights models, it benefits from extensive community support, broad framework compatibility (vLLM, Ollama, llama.cpp, TGI), and availability across virtually every major GPU cloud provider.

Llama 3.3 70B is available at approximately $0.20–$0.80 per million input tokens and $0.20–$0.80 per million output tokens from third-party providers, with significant variation by provider. At 1,000 input + 500 output tokens per call, it costs approximately $0.0003–$0.0006 per 1,000 API calls — among the lowest prices for a capable general-purpose model. Self-hosted deployments on a single H100 80GB GPU achieve approximately 60–100 tokens per second throughput, making it one of the most cost-effective models to self-host at the 70B scale. The break-even point versus API pricing for self-hosting is typically reached within days of sustained high-volume usage.

Llama 3.3 70B's 128K-token context window handles most production use cases without chunking. Self-hosted latency on H100 80GB is approximately 100–200ms TTFT — fast enough for interactive applications. Throughput is approximately 60–100 tokens per second on H100 80GB, or 20–40 tokens per second on A100 80GB. Maximum output is 8,192 tokens. The model fits comfortably in a single H100 80GB GPU in FP16 precision, or in a single A100 80GB with quantization — making it accessible for teams with modest GPU infrastructure. For teams without GPU infrastructure, third-party API providers offer it at very competitive rates.

Llama 3.3 70B is best suited to general-purpose language tasks where cost efficiency and open-weights flexibility are important: chat assistants, document summarization, code generation for common patterns, entity extraction, classification, and RAG pipelines. Its tool use capability enables agentic workflows at very low cost. The open-weights nature makes it suitable for fine-tuning on domain-specific data — a significant advantage for specialized applications where a fine-tuned 70B model can match or exceed a larger general-purpose model. It is not the right choice for tasks requiring frontier reasoning capability or multimodal inputs (it is text-only).

Llama 3.3 70B sits in the advanced tier of Meta's open-weights lineup, above Llama 3.2 and below Llama 4 Scout and Maverick. Its main competitors are proprietary models at similar capability levels, and other open-weights models of similar size. For teams seeking a capable, cost-efficient open-weights model with broad ecosystem support, Llama 3.3 70B remains one of the strongest choices despite being superseded by Llama 4 models. Its intelligence score of approximately 78/100 reflects strong advanced-tier performance for its parameter count.