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kimi-k2.6 vs deepseek-r1

Side-by-side comparison of kimi-k2.6 and deepseek-r1: benchmarks, pricing, context window and capabilities. Both are accessible through Requesty's unified API. kimi-k2.6 outperforms deepseek-r1 on 6 of 6 shared benchmarks.

Benchmark comparison

Intelligence Indexreasoning
kimi-k2.644.2%
deepseek-r120.1%
Coding Indexcoding
kimi-k2.661.8%
deepseek-r1N/A
Math Indexmath
kimi-k2.6N/A
deepseek-r176.0%
GPQA Diamondreasoning
kimi-k2.691.1%
deepseek-r181.3%
AIME 2025math
kimi-k2.6N/A
deepseek-r176.0%
LiveCodeBenchcoding
kimi-k2.6N/A
deepseek-r177.0%
Terminal-Bench Hardagentic
kimi-k2.643.9%
deepseek-r115.9%
τ²-Benchagentic
kimi-k2.695.9%
deepseek-r136.5%
SciCodecoding
kimi-k2.653.5%
deepseek-r140.3%
MMLU Proknowledge
kimi-k2.6N/A
deepseek-r184.9%
Humanity's Last Examreasoning
kimi-k2.635.9%
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-k2.6deepseek-r1
Input price / 1M$0.95$4.00
Output price / 1M$4.00$4.00
Context window262K tokens64K tokens
Max output262K tokensN/A
Vision inputYesN/A
Tool callingYesYes
ReasoningYesN/A
Prompt cachingYesN/A
Computer useN/AN/A
ProviderMoonshot AINovita AI

Questions people ask

Is kimi-k2.6 better than deepseek-r1?
kimi-k2.6 outperforms deepseek-r1 on 6 of 6 shared benchmarks. See the benchmark comparison above for specifics: kimi-k2.6 and deepseek-r1 have different strengths across reasoning, coding, math and multimodal tasks.
Which is cheaper, kimi-k2.6 or deepseek-r1?
kimi-k2.6 is cheaper. kimi-k2.6 costs $0.95/$4.00 per 1M input/output tokens, while deepseek-r1 costs $4.00/$4.00.
Can I use kimi-k2.6 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-k2.6" and "novita/deepseek/deepseek-r1", no other code changes needed.
What are the context windows?
kimi-k2.6 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-k2.6 and deepseek-r1 with one line of code

Requesty provides a single OpenAI-compatible API for 400+ models. Change the model parameter, not your code.