Compare Agnes 2.5 Pro from Agnes and Kimi K3 from Moonshot AI on key metrics including benchmarks, price, context length, and other model features. Access both models and hundreds of others through the AIHubMix API.
Agnes 2.5 Pro
Kimi K3Agnes 2.5 Pro is Agnes AI’s paid inference model and the commercially stable version of the Agnes 2.5 Pro Alpha ranking model, suitable for advanced coding, scientific reasoning, long-context analysis, agent workflows, and multimodal understanding. The model is accessed via an OpenAI-compatible Chat Completions API.
Kimi K3 is Kimi’s flagship model for long-horizon coding and end-to-end knowledge work, with a 1M-token context window and industry-leading intelligence.
Prices are per million tokens. Time to First Token and throughput are rolling averages measured on AIHubMix.
Promotional prices show the discounted rate; see each model page for promotion windows.
Daily traffic served through AIHubMix — how demand for each model is trending.
Measured on real AIHubMix traffic, hourly buckets. Gaps mean no traffic in that hour.
Estimate your monthly bill for the same workload on each model.
Monthly = daily × 30. Discounted rates applied where a promotion is active.
Agnes 2.5 Pro: $0.9/M output tokens; Kimi K3: $13.5/M. Use the cost calculator above to estimate your own workload.
Agnes 2.5 Pro: 0.8s time to first token measured on AIHubMix; see the live performance charts above for how each model behaves across the day.
Agnes 2.5 Pro accepts 1,000,000 and Kimi K3 accepts 1,048,576 input tokens. Maximum output per request is 65,536 tokens on Agnes 2.5 Pro and 1,048,576 tokens on Kimi K3.
Kimi K3 at 73.1 tok/s and Agnes 2.5 Pro at 20.1 tok/s, measured as output throughput on AIHubMix — a separate metric from time to first token.
Agnes 2.5 Pro accepts text and image input and supports thinking; Kimi K3 accepts text, image and video input and supports thinking, function calling and structured outputs.
Yes. AIHubMix serves every model on this page behind one OpenAI-compatible endpoint, so switching between them is a one-line change to the model field — no second account, key or SDK.
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