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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 2026-04-28 · Source: legacy_model_catalog
General-purpose text generation, chat, summarization and content workloads where broad capability and low cost matter most.
Qwen2.5 7B is the latest series of Qwen large language models. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and...
qwen-2.5-7b-instruct is a Text model from Alibaba Cloud · Qwen (CN). HotON.ai tracks it at $0.04 per 1M input tokens and $0.10 per 1M output tokens, with a 33K-token context window. Its composite efficiency score is 89/100 at an estimated $0.000 per successful task.
qwen-2.5-7b-instruct is tracked at $0.04 per 1M input tokens and $0.10 per 1M output tokens. A typical 3:1 output-to-input workload blends to roughly $0.09 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.
qwen-2.5-7b-instruct supports up to a 33K-token context window — large enough for long documents and extended conversations in a single request.
Within the HotON.ai tracked set, qwen-2.5-7b-instruct is cheaper than 94% of models on input price and ranks #162 of 535 by overall efficiency.
Yes — morph-rerank-v3 is a lower-cost option at $0.00 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 — qwen-2.5-7b-instruct (Alibaba Cloud · Qwen): $0.04/1M input, $0.10/1M output, as of 2026-04-28. https://hoton.ai/en/models/qwen-qwen-2-5-7b-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.