kimi-k2 vs gemini-2.5-pro
Side-by-side comparison of kimi-k2 and gemini-2.5-pro — benchmarks, pricing, context window and capabilities. Both are accessible through Requesty's unified API. gemini-2.5-pro outperforms kimi-k2 on 6 of 7 shared benchmarks.
kimi-k2
Input / 1M
$0.60
Output / 1M
$2.50
Context
262K
Model ID
vertex/kimi-k2
gemini-2.5-pro
Input / 1M
$1.25
Output / 1M
$10.00
Context
1.0M
Model ID
google/gemini-2.5-pro
Benchmark comparison
MMLU Proknowledge
kimi-k282.3%
gemini-2.5-pro86.2%
GPQA Diamondreasoning
kimi-k270.0%
gemini-2.5-pro84.0%
HumanEvalcoding
kimi-k289.9%
gemini-2.5-pro93.2%
SWE-Bench Verifiedcoding
kimi-k265.8%
gemini-2.5-pro63.8%
MATHmath
kimi-k289.2%
gemini-2.5-pro91.4%
AIME 2024math
kimi-k280.1%
gemini-2.5-pro88.0%
MMMUmultimodal
kimi-k2—
gemini-2.5-pro81.7%
LiveBenchreasoning
kimi-k268.3%
gemini-2.5-pro73.6%
τ-bench Retailagentic
kimi-k2—
gemini-2.5-pro69.8%
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
| kimi-k2 | gemini-2.5-pro | |
|---|---|---|
| Input price / 1M | $0.60 | $1.25 |
| Output price / 1M | $2.50 | $10.00 |
| 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-pro?
gemini-2.5-pro outperforms kimi-k2 on 6 of 7 shared benchmarks. See the benchmark comparison above for specifics — kimi-k2 and gemini-2.5-pro have different strengths across reasoning, coding, math and multimodal tasks.
Which is cheaper — kimi-k2 or gemini-2.5-pro?
kimi-k2 is cheaper. kimi-k2 costs $0.60/$2.50 per 1M input/output tokens, while gemini-2.5-pro costs $1.25/$10.00.
Can I use kimi-k2 and gemini-2.5-pro 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-pro" — no other code changes needed.
What are the context windows?
kimi-k2 supports up to 262K tokens of context. gemini-2.5-pro 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-pro with one line of code
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