gemini-3.6-flash vs kimi-k2.5
Side-by-side comparison of gemini-3.6-flash and kimi-k2.5: benchmarks, pricing, context window and capabilities. Both are accessible through Requesty's unified API. gemini-3.6-flash outperforms kimi-k2.5 on 4 of 4 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-k2.5
Input / 1M
$0.60
Output / 1M
$3.00
Context
262K
Model ID
moonshot/kimi-k2.5
Benchmark comparison
Intelligence Indexreasoning
gemini-3.6-flash40.3%
kimi-k2.527.6%
Coding Indexcoding
gemini-3.6-flash69.2%
kimi-k2.546.8%
GPQA Diamondreasoning
gemini-3.6-flash92.8%
kimi-k2.587.9%
Terminal-Bench Hardagentic
gemini-3.6-flashN/A
kimi-k2.534.8%
τ²-Benchagentic
gemini-3.6-flashN/A
kimi-k2.595.9%
SciCodecoding
gemini-3.6-flash53.4%
kimi-k2.5N/A
Humanity's Last Examreasoning
gemini-3.6-flash40.8%
kimi-k2.530.7%
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-k2.5 | |
|---|---|---|
| Input price / 1M | $1.50 | $0.60 |
| Output price / 1M | $7.00 | $3.00 |
| Context window | 1.0M tokens | 262K tokens |
| Max output | 66K tokens | 262K 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-k2.5?
gemini-3.6-flash outperforms kimi-k2.5 on 4 of 4 shared benchmarks. See the benchmark comparison above for specifics: gemini-3.6-flash and kimi-k2.5 have different strengths across reasoning, coding, math and multimodal tasks.
Which is cheaper, gemini-3.6-flash or kimi-k2.5?
kimi-k2.5 is cheaper. gemini-3.6-flash costs $1.50/$7.00 per 1M input/output tokens, while kimi-k2.5 costs $0.60/$3.00.
Can I use gemini-3.6-flash and kimi-k2.5 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-k2.5", no other code changes needed.
What are the context windows?
gemini-3.6-flash supports up to 1.0M tokens of context. kimi-k2.5 supports up to 262K 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-k2.5 with one line of code
Requesty provides a single OpenAI-compatible API for 600+ models. Change the model parameter, not your code.