gemini-3.8-flash vs kimi-k2
Side-by-side comparison of gemini-3.8-flash and kimi-k2: benchmarks, pricing, context window and capabilities. Both are accessible through Requesty's unified API. gemini-3.8-flash outperforms kimi-k2 on 3 of 3 shared benchmarks.
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
kimi-k2
Input / 1M
$0.60
Output / 1M
$2.50
Context
262K
Model ID
vertex/kimi-k2
Benchmark comparison
Intelligence Indexreasoning
gemini-3.8-flash47.1%
kimi-k225.9%
Coding Indexcoding
gemini-3.8-flash76.3%
kimi-k2N/A
Math Indexmath
gemini-3.8-flashN/A
kimi-k294.7%
GPQA Diamondreasoning
gemini-3.8-flash95.3%
kimi-k283.8%
AIME 2025math
gemini-3.8-flashN/A
kimi-k294.7%
LiveCodeBenchcoding
gemini-3.8-flashN/A
kimi-k285.3%
Terminal-Bench Hardagentic
gemini-3.8-flashN/A
kimi-k231.1%
τ²-Benchagentic
gemini-3.8-flashN/A
kimi-k293.0%
SciCodecoding
gemini-3.8-flash56.6%
kimi-k2N/A
MMLU Proknowledge
gemini-3.8-flashN/A
kimi-k284.8%
Humanity's Last Examreasoning
gemini-3.8-flash47.8%
kimi-k223.8%
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
| gemini-3.8-flash | kimi-k2 | |
|---|---|---|
| Input price / 1M | $1.50$0.75 | $0.60 |
| Output price / 1M | $7.50$3.75 | $2.50 |
| Context window | 1.0M tokens | 262K tokens |
| Max output | 66K tokens | 262K tokens |
| Vision input | Yes | N/A |
| Tool calling | Yes | Yes |
| Reasoning | Yes | Yes |
| Prompt caching | Yes | Yes |
| Computer use | N/A | N/A |
| Provider | Google LLC (Vertex AI) | Google LLC (Vertex AI) |
Questions people ask
Is gemini-3.8-flash better than kimi-k2?
gemini-3.8-flash outperforms kimi-k2 on 3 of 3 shared benchmarks. See the benchmark comparison above for specifics: gemini-3.8-flash and kimi-k2 have different strengths across reasoning, coding, math and multimodal tasks.
Which is cheaper, gemini-3.8-flash or kimi-k2?
kimi-k2 is cheaper. gemini-3.8-flash costs $0.75/$3.75 per 1M input/output tokens, while kimi-k2 costs $0.60/$2.50. 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 gemini-3.8-flash and kimi-k2 through the same API?
Yes. Requesty provides a single OpenAI-compatible API that routes to both. Change just the "model" parameter to switch between "vertex/gemini-3.8-flash" and "vertex/kimi-k2", no other code changes needed.
What are the context windows?
gemini-3.8-flash supports up to 1.0M tokens of context. kimi-k2 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-3.8-flash and kimi-k2 with one line of code
Requesty provides a single OpenAI-compatible API for 600+ models. Change the model parameter, not your code.