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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 2026-05-10 · Source: ai21_official_pricing
General-purpose text generation, chat, summarization and content workloads where broad capability and low cost matter most.
Jamba Large 1.7 is the latest model in the Jamba open family, offering improvements in grounding, instruction-following, and overall efficiency. Built on a hybrid SSM-Transformer architecture with a 256K context...
jamba-large-1.7 is a Text model from AI21 (US). HotON.ai tracks it at $2.00 per 1M input tokens and $8.00 per 1M output tokens, with a 256K-token context window. Its composite efficiency score is 87/100 at an estimated $0.008 per successful task.
jamba-large-1.7 is tracked at $2.00 per 1M input tokens and $8.00 per 1M output tokens. A typical 3:1 output-to-input workload blends to roughly $6.50 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.
jamba-large-1.7 supports up to a 256K-token context window — large enough for long documents and extended conversations in a single request.
Within the HotON.ai tracked set, jamba-large-1.7 is cheaper than 15% of models on input price and ranks #438 of 537 by overall efficiency.
Yes — minimax-m2 is a lower-cost option at $1.00 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 — jamba-large-1.7 (AI21): $2.00/1M input, $8.00/1M output, as of 2026-05-10. https://hoton.ai/en/models/ai21-jamba-large-1-7Pricing 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.