deepseek-r1 vs kimi-k2
Side-by-side comparison of deepseek-r1 and kimi-k2: benchmarks, pricing, context window and capabilities. Both are accessible through Requesty's unified API. kimi-k2 outperforms deepseek-r1 on 9 of 10 shared benchmarks.
deepseek-r1
Input / 1M
$4.00
Output / 1M
$4.00
Context
64K
Model ID
novita/deepseek/deepseek-r1
kimi-k2
Input / 1M
$0.60
Output / 1M
$2.50
Context
262K
Model ID
vertex/kimi-k2
Benchmark comparison
Intelligence Indexreasoning
deepseek-r120.1%
kimi-k232.7%
Math Indexmath
deepseek-r176.0%
kimi-k294.7%
GPQA Diamondreasoning
deepseek-r181.3%
kimi-k283.8%
AIME 2025math
deepseek-r176.0%
kimi-k294.7%
LiveCodeBenchcoding
deepseek-r177.0%
kimi-k285.3%
Terminal-Bench Hardagentic
deepseek-r115.9%
kimi-k231.1%
τ²-Benchagentic
deepseek-r136.5%
kimi-k293.0%
SciCodecoding
deepseek-r140.3%
kimi-k242.4%
MMLU Proknowledge
deepseek-r184.9%
kimi-k284.8%
Humanity's Last Examreasoning
deepseek-r114.9%
kimi-k222.3%
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
| deepseek-r1 | kimi-k2 | |
|---|---|---|
| Input price / 1M | $4.00 | $0.60 |
| Output price / 1M | $4.00 | $2.50 |
| Context window | 64K tokens | 262K tokens |
| Max output | N/A | 262K tokens |
| Vision input | N/A | Yes |
| Tool calling | Yes | Yes |
| Reasoning | N/A | Yes |
| Prompt caching | N/A | Yes |
| Computer use | N/A | N/A |
| Provider | Novita AI | Google LLC (Vertex AI) |
Questions people ask
Is deepseek-r1 better than kimi-k2?
kimi-k2 outperforms deepseek-r1 on 9 of 10 shared benchmarks. See the benchmark comparison above for specifics: deepseek-r1 and kimi-k2 have different strengths across reasoning, coding, math and multimodal tasks.
Which is cheaper, deepseek-r1 or kimi-k2?
kimi-k2 is cheaper. deepseek-r1 costs $4.00/$4.00 per 1M input/output tokens, while kimi-k2 costs $0.60/$2.50.
Can I use deepseek-r1 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 "novita/deepseek/deepseek-r1" and "vertex/kimi-k2", no other code changes needed.
What are the context windows?
deepseek-r1 supports up to 64K 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 deepseek-r1 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.