kimi-k2.7-code vs deepseek-v4-flash

Side-by-side comparison of kimi-k2.7-code and deepseek-v4-flash: benchmarks, pricing, context window and capabilities. Both are accessible through Requesty's unified API. deepseek-v4-flash outperforms kimi-k2.7-code on 5 of 5 shared benchmarks.

Benchmark comparison

Intelligence Indexreasoning
kimi-k2.7-code43.0%
deepseek-v4-flash51.8%
Coding Indexcoding
kimi-k2.7-code60.8%
deepseek-v4-flash69.1%
GPQA Diamondreasoning
kimi-k2.7-code89.6%
deepseek-v4-flash90.8%
Terminal-Bench Hardagentic
kimi-k2.7-code44.7%
deepseek-v4-flashN/A
τ²-Benchagentic
kimi-k2.7-code90.1%
deepseek-v4-flashN/A
SciCodecoding
kimi-k2.7-code47.5%
deepseek-v4-flash49.9%
Humanity's Last Examreasoning
kimi-k2.7-code35.0%
deepseek-v4-flash38.6%

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-codedeepseek-v4-flash
Input price / 1M$0.95$0.14
Output price / 1M$4.00$0.28
Context window262K tokens1.0M tokens
Max output262K tokensN/A
Vision inputYesN/A
Tool callingYesYes
ReasoningYesYes
Prompt cachingYesN/A
Computer useN/AN/A
ProviderMoonshot AINovita AI

Questions people ask

Is kimi-k2.7-code better than deepseek-v4-flash?
deepseek-v4-flash outperforms kimi-k2.7-code on 5 of 5 shared benchmarks. See the benchmark comparison above for specifics: kimi-k2.7-code and deepseek-v4-flash have different strengths across reasoning, coding, math and multimodal tasks.
Which is cheaper, kimi-k2.7-code or deepseek-v4-flash?
deepseek-v4-flash is cheaper. kimi-k2.7-code costs $0.95/$4.00 per 1M input/output tokens, while deepseek-v4-flash costs $0.14/$0.28.
Can I use kimi-k2.7-code and deepseek-v4-flash 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-v4-flash", no other code changes needed.
What are the context windows?
kimi-k2.7-code supports up to 262K tokens of context. deepseek-v4-flash 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-k2.7-code and deepseek-v4-flash with one line of code

Requesty provides a single OpenAI-compatible API for 600+ models. Change the model parameter, not your code.