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Daily blended price ($/1M) — recorded each day, builds into a trend over time.
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.
Llama 3.2 1B is a 1-billion-parameter language model focused on efficiently performing natural language tasks, such as summarization, dialogue, and multilingual text analysis. Its smaller size allows it to operate...
llama-3.2-1b-instruct is a Text model from Meta (US). HotON.ai tracks it at $0.03 per 1M input tokens and $0.20 per 1M output tokens, with a 60K-token context window. Its composite efficiency score is 89/100 at an estimated $0.000 per successful task.
llama-3.2-1b-instruct is tracked at $0.03 per 1M input tokens and $0.20 per 1M output tokens. A typical 3:1 output-to-input workload blends to roughly $0.16 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-3.2-1b-instruct supports up to a 60K-token context window — large enough for long documents and extended conversations in a single request.
Within the HotON.ai tracked set, llama-3.2-1b-instruct is cheaper than 95% of models on input price and ranks #250 of 537 by overall efficiency.
Yes — qwen3-235b-a22b-2507 is a lower-cost option at $0.10 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-3.2-1b-instruct (Meta): $0.03/1M input, $0.20/1M output, as of 2026-05-11. https://hoton.ai/en/models/meta-llama-llama-3-2-1b-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.