Compare Gemini 4 Carbon from Google 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.
Gemini 4 Carbon
Kimi K3Gemini 4 Carbon is Google DeepMind’s multimodal model for complex coding, long-running agent workflows, and cybersecurity defense. Building on Gemini 4 Argon, it improves software engineering, vulnerability detection and remediation, multimodal understanding, and execution across challenging, multi-step tasks.
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.
LMArena ratings by capability (Bradley-Terry, commonly called Elo). Higher is better.
Source: LMArena (arena.ai) leaderboard, imported by AIHubMix. Models without published ratings are omitted per chart.
Estimate your monthly bill for the same workload on each model.
Monthly = daily × 30. Discounted rates applied where a promotion is active.
Gemini 4 Carbon: $10/M output tokens; Kimi K3: $14.2/M. Use the cost calculator above to estimate your own workload.
Gemini 4 Carbon: 1573; Kimi K3: 1540 (LMArena coding leaderboard).
Gemini 4 Carbon accepts text input; 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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