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Blended $/1M across tracked versions of this line.
Typical 3:1 output-to-input mix, per 1M tokens
Price as of 2026-05-11 · Source: meta_reference_catalog
General-purpose text generation, chat, summarization and content workloads where broad capability and low cost matter most.
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...
llama-3.3-70b-instruct is a Text model from Meta (US). HotON.ai tracks it at $0.10 per 1M input tokens and $0.32 per 1M output tokens, with a 131K-token context window. Its composite efficiency score is 89/100 at an estimated $0.000 per successful task.
llama-3.3-70b-instruct is tracked at $0.10 per 1M input tokens and $0.32 per 1M output tokens. A typical 3:1 output-to-input workload blends to roughly $0.27 per 1M tokens. Figures are illustrative demo data.
General-purpose text generation, chat, summarization and content workloads where broad capability and low cost matter most.
llama-3.3-70b-instruct supports up to a 131K-token context window — large enough for long documents and extended conversations in a single request.
Within the HotON.ai tracked set, llama-3.3-70b-instruct is cheaper than 79% of models on input price and ranks #125 of 535 by overall efficiency.
Yes — ling-2.6-flash is a lower-cost option at $0.24 per 1M output tokens, while still covering similar Text use cases. Compare them side by side on HotON.ai.
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HotON.ai — llama-3.3-70b-instruct (Meta): $0.10/1M input, $0.32/1M output, as of 2026-05-11. https://hoton.ai/en/models/meta-llama-llama-3-3-70b-instructPricing is real (via the TestKey catalog, updated daily). Quality (Arena Elo) is real where the model is ranked on LMArena. Efficiency is a modeled composite of real price and context.