moonshotai/Kimi-K2.5 vs gemini-3.8-flash
Side-by-side comparison of moonshotai/Kimi-K2.5 and gemini-3.8-flash: benchmarks, pricing, context window and capabilities. Both are accessible through Requesty's unified API.
moonshotai/Kimi-K2.5
Input / 1M
$0.45
Output / 1M
$2.25
Context
262K
Model ID
deepinfra/moonshotai/Kimi-K2.5
gemini-3.8-flash
Input / 1M
$1.50$0.75
Output / 1M
$7.50$3.75
Context
1.0M
Model ID
vertex/gemini-3.8-flash
Benchmark comparison
Intelligence Indexreasoning
moonshotai/Kimi-K2.5N/A
gemini-3.8-flash30.8%
Scores sourced from official model cards, Artificial Analysis, and public leaderboards. Benchmarks measure specific skills and don't capture every aspect of model quality.
Pricing & specifications
| moonshotai/Kimi-K2.5 | gemini-3.8-flash | |
|---|---|---|
| Input price / 1M | $0.45 | $1.50$0.75 |
| Output price / 1M | $2.25 | $7.50$3.75 |
| Context window | 262K tokens | 1.0M tokens |
| Max output | 131K tokens | 66K tokens |
| Vision input | Yes | Yes |
| Tool calling | Yes | Yes |
| Reasoning | Yes | Yes |
| Prompt caching | Yes | Yes |
| Computer use | N/A | N/A |
| Provider | DeepInfra | Google Vertex AI |
Questions people ask
Is moonshotai/Kimi-K2.5 better than gemini-3.8-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-3.8-flash?
moonshotai/Kimi-K2.5 is cheaper. moonshotai/Kimi-K2.5 costs $0.45/$2.25 per 1M input/output tokens, while gemini-3.8-flash costs $0.75/$3.75. The prices shown are what you pay: gemini-3.8-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-3.8-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-3.8-flash", no other code changes needed.
What are the context windows?
moonshotai/Kimi-K2.5 supports up to 262K tokens of context. gemini-3.8-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-3.8-flash with one line of code
Requesty provides a single OpenAI-compatible API for 600+ models. Change the model parameter, not your code.