gemini-3.5-flash vs kimi-k2.6
Side-by-side comparison of gemini-3.5-flash and kimi-k2.6: benchmarks, pricing, context window and capabilities. Both are accessible through Requesty's unified API. gemini-3.5-flash outperforms kimi-k2.6 on 4 of 7 shared benchmarks.
gemini-3.5-flash
Input / 1M
$1.50
Output / 1M
$9.00
Context
1.0M
Model ID
google/gemini-3.5-flash
kimi-k2.6
Input / 1M
$0.95
Output / 1M
$4.00
Context
262K
Model ID
moonshot/kimi-k2.6
Benchmark comparison
Intelligence Indexreasoning
gemini-3.5-flash52.0%
kimi-k2.645.1%
Coding Indexcoding
gemini-3.5-flash70.1%
kimi-k2.661.8%
GPQA Diamondreasoning
gemini-3.5-flash92.2%
kimi-k2.691.1%
Terminal-Bench Hardagentic
gemini-3.5-flash40.9%
kimi-k2.643.9%
τ²-Benchagentic
gemini-3.5-flash95.3%
kimi-k2.695.9%
SciCodecoding
gemini-3.5-flash53.1%
kimi-k2.653.5%
Humanity's Last Examreasoning
gemini-3.5-flash42.7%
kimi-k2.637.5%
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-3.5-flash | kimi-k2.6 | |
|---|---|---|
| Input price / 1M | $1.50 | $0.95 |
| Output price / 1M | $9.00 | $4.00 |
| 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-3.5-flash better than kimi-k2.6?
gemini-3.5-flash outperforms kimi-k2.6 on 4 of 7 shared benchmarks. See the benchmark comparison above for specifics: gemini-3.5-flash and kimi-k2.6 have different strengths across reasoning, coding, math and multimodal tasks.
Which is cheaper, gemini-3.5-flash or kimi-k2.6?
kimi-k2.6 is cheaper. gemini-3.5-flash costs $1.50/$9.00 per 1M input/output tokens, while kimi-k2.6 costs $0.95/$4.00.
Can I use gemini-3.5-flash and kimi-k2.6 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-3.5-flash" and "moonshot/kimi-k2.6", no other code changes needed.
What are the context windows?
gemini-3.5-flash supports up to 1.0M tokens of context. kimi-k2.6 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-3.5-flash and kimi-k2.6 with one line of code
Requesty provides a single OpenAI-compatible API for 600+ models. Change the model parameter, not your code.