Compare Gemma 4 31B It 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.
Gemma 4 31B It
Kimi K3Gemma 4 31B Instruct is Google DeepMind's 30.7B dense multimodal model supporting text and image input with text output. Features a 256K token context window, configurable thinking/reasoning mode, native function calling, and multilingual support across 140+ languages. Strong on coding, reasoning, and document understanding tasks. Apache 2.0 license.
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.
Gemma 4 31B It: $0.4/M output tokens; Kimi K3: $13.5/M. Use the cost calculator above to estimate your own workload.
Gemma 4 31B It: 0.2s time to first token measured on AIHubMix; see the live performance charts above for how each model behaves across the day.
Gemma 4 31B It accepts 262,100 and Kimi K3 accepts 1,048,576 input tokens. Maximum output per request is 131,100 tokens on Gemma 4 31B It and 1,048,576 tokens on Kimi K3.
Gemma 4 31B It at 209.1 tok/s and Kimi K3 at 73.1 tok/s, measured as output throughput on AIHubMix — a separate metric from time to first token.
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