gemini-3.6-flash vs kimi-k3
Side-by-side comparison of gemini-3.6-flash and kimi-k3: benchmarks, pricing, context window and capabilities. Both are accessible through Requesty's unified API. kimi-k3 outperforms gemini-3.6-flash on 5 of 5 shared benchmarks.
gemini-3.6-flash
Input / 1M
$1.50
Output / 1M
$7.00
Context
1.0M
Model ID
google/gemini-3.6-flash
kimi-k3
Input / 1M
$3.00
Output / 1M
$15.00
Context
1.0M
Model ID
moonshot/kimi-k3
Benchmark comparison
Intelligence Indexreasoning
gemini-3.6-flash40.3%
kimi-k350.2%
Coding Indexcoding
gemini-3.6-flash69.2%
kimi-k376.2%
GPQA Diamondreasoning
gemini-3.6-flash92.8%
kimi-k393.5%
SciCodecoding
gemini-3.6-flash53.4%
kimi-k359.5%
Humanity's Last Examreasoning
gemini-3.6-flash40.8%
kimi-k346.9%
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
| gemini-3.6-flash | kimi-k3 | |
|---|---|---|
| Input price / 1M | $1.50 | $3.00 |
| Output price / 1M | $7.00 | $15.00 |
| Context window | 1.0M tokens | 1.0M tokens |
| Max output | 66K tokens | 1.0M tokens |
| Vision input | Yes | Yes |
| Tool calling | Yes | Yes |
| Reasoning | Yes | Yes |
| Prompt caching | Yes | Yes |
| Computer use | N/A | N/A |
| Provider | Google LLC (Gemini API) | Moonshot AI |
Questions people ask
Is gemini-3.6-flash better than kimi-k3?
kimi-k3 outperforms gemini-3.6-flash on 5 of 5 shared benchmarks. See the benchmark comparison above for specifics: gemini-3.6-flash and kimi-k3 have different strengths across reasoning, coding, math and multimodal tasks.
Which is cheaper, gemini-3.6-flash or kimi-k3?
gemini-3.6-flash is cheaper. gemini-3.6-flash costs $1.50/$7.00 per 1M input/output tokens, while kimi-k3 costs $3.00/$15.00.
Can I use gemini-3.6-flash and kimi-k3 through the same API?
Yes. Requesty provides a single OpenAI-compatible API that routes to both. Change just the "model" parameter to switch between "google/gemini-3.6-flash" and "moonshot/kimi-k3", no other code changes needed.
What are the context windows?
gemini-3.6-flash supports up to 1.0M tokens of context. kimi-k3 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 gemini-3.6-flash and kimi-k3 with one line of code
Requesty provides a single OpenAI-compatible API for 600+ models. Change the model parameter, not your code.