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Blended $/1M across tracked versions of this line.
Typical 3:1 output-to-input mix, per 1M tokens
Source: litellm
General-purpose text generation, chat, summarization and content workloads where broad capability and low cost matter most.
Cohere Embed English v3.0 creates English text embeddings for retrieval, clustering, and semantic search.
embed-english-v3.0 is a Text model from Cohere (US). HotON.ai tracks it at $0.10 per 1M input tokens and $0.00 per 1M output tokens, with a 1K-token context window. Its composite efficiency score is 88/100 at an estimated $0.000 per successful task.
embed-english-v3.0 is tracked at $0.10 per 1M input tokens and $0.00 per 1M output tokens. A typical 3:1 output-to-input workload blends to roughly $0.03 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.
embed-english-v3.0 supports up to a 1K-token context window — large enough for long documents and extended conversations in a single request.
Within the HotON.ai tracked set, embed-english-v3.0 is cheaper than 79% of models on input price and ranks #392 of 537 by overall efficiency.
Yes — mistral-nemo is a lower-cost option at $0.04 per 1M output tokens, while still covering similar Text use cases. Compare them side by side on HotON.ai.
Ready to paste into articles, papers or AI prompts — prices and date refresh with the live data.
HotON.ai — embed-english-v3.0 (Cohere): $0.10/1M input, $0.00/1M output. https://hoton.ai/en/models/cohere-embed-english-v3-0Pricing 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.