kimi-k2 vs deepseek-ai/DeepSeek-R1
Side-by-side comparison of kimi-k2 and deepseek-ai/DeepSeek-R1 — benchmarks, pricing, context window and capabilities. Both are accessible through Requesty's unified API. deepseek-ai/DeepSeek-R1 outperforms kimi-k2 on 5 of 7 shared benchmarks.
kimi-k2
Input / 1M
$0.60
Output / 1M
$2.50
Context
262K
Model ID
vertex/kimi-k2
deepseek-ai/DeepSeek-R1
Input / 1M
$3.00
Output / 1M
$7.00
Context
64K
Model ID
together/deepseek-ai/DeepSeek-R1
Benchmark comparison
MMLU Proknowledge
kimi-k282.3%
deepseek-ai/DeepSeek-R185.0%
GPQA Diamondreasoning
kimi-k270.0%
deepseek-ai/DeepSeek-R171.5%
HumanEvalcoding
kimi-k289.9%
deepseek-ai/DeepSeek-R192.0%
SWE-Bench Verifiedcoding
kimi-k265.8%
deepseek-ai/DeepSeek-R149.2%
MATHmath
kimi-k289.2%
deepseek-ai/DeepSeek-R192.2%
AIME 2024math
kimi-k280.1%
deepseek-ai/DeepSeek-R179.8%
MMMUmultimodal
kimi-k2—
deepseek-ai/DeepSeek-R174.8%
LiveBenchreasoning
kimi-k268.3%
deepseek-ai/DeepSeek-R171.5%
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 | deepseek-ai/DeepSeek-R1 | |
|---|---|---|
| Input price / 1M | $0.60 | $3.00 |
| Output price / 1M | $2.50 | $7.00 |
| Context window | 262K tokens | 64K tokens |
| Max output | 262K tokens | 8K tokens |
| Vision input | Yes | — |
| Tool calling | Yes | — |
| Reasoning | Yes | — |
| Prompt caching | Yes | — |
| Computer use | — | — |
| Provider | Google LLC (Vertex AI) | Together AI Inc. |
Questions people ask
Is kimi-k2 better than deepseek-ai/DeepSeek-R1?
deepseek-ai/DeepSeek-R1 outperforms kimi-k2 on 5 of 7 shared benchmarks. See the benchmark comparison above for specifics — kimi-k2 and deepseek-ai/DeepSeek-R1 have different strengths across reasoning, coding, math and multimodal tasks.
Which is cheaper — kimi-k2 or deepseek-ai/DeepSeek-R1?
kimi-k2 is cheaper. kimi-k2 costs $0.60/$2.50 per 1M input/output tokens, while deepseek-ai/DeepSeek-R1 costs $3.00/$7.00.
Can I use kimi-k2 and deepseek-ai/DeepSeek-R1 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 "together/deepseek-ai/DeepSeek-R1" — no other code changes needed.
What are the context windows?
kimi-k2 supports up to 262K tokens of context. deepseek-ai/DeepSeek-R1 supports up to 64K 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 deepseek-ai/DeepSeek-R1 with one line of code
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