kimi-k2.7-code vs deepseek-r1
Side-by-side comparison of kimi-k2.7-code and deepseek-r1: benchmarks, pricing, context window and capabilities. Both are accessible through Requesty's unified API.
kimi-k2.7-code
Input / 1M
$0.95
Output / 1M
$4.00
Context
262K
Model ID
moonshot/kimi-k2.7-code
deepseek-r1
Input / 1M
$4.00
Output / 1M
$4.00
Context
64K
Model ID
novita/deepseek/deepseek-r1
Benchmark comparison
Intelligence Indexreasoning
kimi-k2.7-codeN/A
deepseek-r127.1%
Coding Indexcoding
kimi-k2.7-codeN/A
deepseek-r124.0%
Math Indexmath
kimi-k2.7-codeN/A
deepseek-r176.0%
GPQA Diamondreasoning
kimi-k2.7-codeN/A
deepseek-r181.3%
AIME 2025math
kimi-k2.7-codeN/A
deepseek-r176.0%
LiveCodeBenchcoding
kimi-k2.7-codeN/A
deepseek-r177.0%
Terminal-Bench Hardagentic
kimi-k2.7-codeN/A
deepseek-r115.9%
τ²-Benchagentic
kimi-k2.7-codeN/A
deepseek-r136.5%
SciCodecoding
kimi-k2.7-codeN/A
deepseek-r140.3%
MMLU Proknowledge
kimi-k2.7-codeN/A
deepseek-r184.9%
Humanity's Last Examreasoning
kimi-k2.7-codeN/A
deepseek-r114.9%
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-k2.7-code | deepseek-r1 | |
|---|---|---|
| Input price / 1M | $0.95 | $4.00 |
| Output price / 1M | $4.00 | $4.00 |
| Context window | 262K tokens | 64K tokens |
| Max output | 262K tokens | N/A |
| Vision input | Yes | N/A |
| Tool calling | Yes | Yes |
| Reasoning | Yes | N/A |
| Prompt caching | Yes | N/A |
| Computer use | N/A | N/A |
| Provider | Moonshot AI | Novita AI |
Questions people ask
Is kimi-k2.7-code better than deepseek-r1?
Benchmark data is limited for one or both models. Compare pricing and capabilities in the tables above, and test both on your own workload.
Which is cheaper, kimi-k2.7-code or deepseek-r1?
kimi-k2.7-code is cheaper. kimi-k2.7-code costs $0.95/$4.00 per 1M input/output tokens, while deepseek-r1 costs $4.00/$4.00.
Can I use kimi-k2.7-code and 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 between "moonshot/kimi-k2.7-code" and "novita/deepseek/deepseek-r1", no other code changes needed.
What are the context windows?
kimi-k2.7-code supports up to 262K tokens of context. 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-k2.7-code and deepseek-r1 with one line of code
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