kimi-k2 vs gemini-2.5-flash
Side-by-side comparison of kimi-k2 and gemini-2.5-flash — benchmarks, pricing, context window and capabilities. Both are accessible through Requesty's unified API. kimi-k2 outperforms gemini-2.5-flash on 7 of 7 shared benchmarks.
kimi-k2
Input / 1M
$0.60
Output / 1M
$2.50
Context
262K
Model ID
vertex/kimi-k2
gemini-2.5-flash
Input / 1M
$0.30
Output / 1M
$2.50
Context
1.0M
Model ID
google/gemini-2.5-flash
Benchmark comparison
MMLU Proknowledge
kimi-k282.3%
gemini-2.5-flash78.4%
GPQA Diamondreasoning
kimi-k270.0%
gemini-2.5-flash68.3%
HumanEvalcoding
kimi-k289.9%
gemini-2.5-flash88.5%
SWE-Bench Verifiedcoding
kimi-k265.8%
gemini-2.5-flash53.2%
MATHmath
kimi-k289.2%
gemini-2.5-flash85.3%
AIME 2024math
kimi-k280.1%
gemini-2.5-flash72.1%
MMMUmultimodal
kimi-k2—
gemini-2.5-flash72.5%
LiveBenchreasoning
kimi-k268.3%
gemini-2.5-flash64.2%
τ-bench Retailagentic
kimi-k2—
gemini-2.5-flash61.5%
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
| kimi-k2 | gemini-2.5-flash | |
|---|---|---|
| Input price / 1M | $0.60 | $0.30 |
| Output price / 1M | $2.50 | $2.50 |
| Context window | 262K tokens | 1.0M tokens |
| Max output | 262K tokens | 66K tokens |
| Vision input | Yes | Yes |
| Tool calling | Yes | Yes |
| Reasoning | Yes | Yes |
| Prompt caching | Yes | Yes |
| Computer use | — | — |
| Provider | Google LLC (Vertex AI) | Google LLC (Gemini API) |
Questions people ask
Is kimi-k2 better than gemini-2.5-flash?
kimi-k2 outperforms gemini-2.5-flash on 7 of 7 shared benchmarks. See the benchmark comparison above for specifics — kimi-k2 and gemini-2.5-flash have different strengths across reasoning, coding, math and multimodal tasks.
Which is cheaper — kimi-k2 or gemini-2.5-flash?
gemini-2.5-flash is cheaper. kimi-k2 costs $0.60/$2.50 per 1M input/output tokens, while gemini-2.5-flash costs $0.30/$2.50.
Can I use kimi-k2 and gemini-2.5-flash through the same API?
Yes. Requesty provides a single OpenAI-compatible API that routes to both. Change just the "model" parameter to switch — "vertex/kimi-k2" or "google/gemini-2.5-flash" — no other code changes needed.
What are the context windows?
kimi-k2 supports up to 262K tokens of context. gemini-2.5-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 kimi-k2 and gemini-2.5-flash with one line of code
Requesty provides a single OpenAI-compatible API for 400+ models. Change the model parameter, not your code.
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