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moonshotai/Kimi-K2.5 vs gemini-3.7-flash

Side-by-side comparison of moonshotai/Kimi-K2.5 and gemini-3.7-flash: benchmarks, pricing, context window and capabilities. Both are accessible through Requesty's unified API.

Pricing & specifications

moonshotai/Kimi-K2.5gemini-3.7-flash
Input price / 1M$0.45$1.50$0.75
Output price / 1M$2.25$7.50$3.75
Context window262K tokens1.0M tokens
Max output131K tokens66K tokens
Vision inputYesYes
Tool callingYesYes
ReasoningYesYes
Prompt cachingYesYes
Computer useN/AN/A
ProviderDeepInfraGoogle Vertex AI

Questions people ask

Is moonshotai/Kimi-K2.5 better than gemini-3.7-flash?
Benchmark data is limited for one or both models. Compare pricing and capabilities in the tables above, and test both on your own workload.
Which is cheaper, moonshotai/Kimi-K2.5 or gemini-3.7-flash?
moonshotai/Kimi-K2.5 is cheaper. moonshotai/Kimi-K2.5 costs $0.45/$2.25 per 1M input/output tokens, while gemini-3.7-flash costs $0.75/$3.75. The prices shown are what you pay: gemini-3.7-flash runs at 50% off its list rate of $1.50 per 1M input and $7.50 per 1M output.
Can I use moonshotai/Kimi-K2.5 and gemini-3.7-flash through the same API?
Yes. Requesty provides a single OpenAI-compatible API that routes to both. Change just the "model" parameter to switch between "deepinfra/moonshotai/Kimi-K2.5" and "vertex/gemini-3.7-flash", no other code changes needed.
What are the context windows?
moonshotai/Kimi-K2.5 supports up to 262K tokens of context. gemini-3.7-flash supports up to 1.0M tokens. Longer context means you can feed larger documents or codebases in a single prompt, though quality often degrades past 128K for most models.

Switch between moonshotai/Kimi-K2.5 and gemini-3.7-flash with one line of code

Requesty provides a single OpenAI-compatible API for 600+ models. Change the model parameter, not your code.