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deepseek-v4-flash-0731 vs kimi-k2

Side-by-side comparison of deepseek-v4-flash-0731 and kimi-k2: benchmarks, pricing, context window and capabilities. Both are accessible through Requesty's unified API. deepseek-v4-flash-0731 outperforms kimi-k2 on 3 of 3 shared benchmarks.

Benchmark comparison

Intelligence Indexreasoning
deepseek-v4-flash-073140.8%
kimi-k225.9%
Coding Indexcoding
deepseek-v4-flash-073169.1%
kimi-k2N/A
Math Indexmath
deepseek-v4-flash-0731N/A
kimi-k294.7%
GPQA Diamondreasoning
deepseek-v4-flash-073190.8%
kimi-k283.8%
AIME 2025math
deepseek-v4-flash-0731N/A
kimi-k294.7%
LiveCodeBenchcoding
deepseek-v4-flash-0731N/A
kimi-k285.3%
Terminal-Bench Hardagentic
deepseek-v4-flash-0731N/A
kimi-k231.1%
τ²-Benchagentic
deepseek-v4-flash-0731N/A
kimi-k293.0%
SciCodecoding
deepseek-v4-flash-073150.3%
kimi-k2N/A
MMLU Proknowledge
deepseek-v4-flash-0731N/A
kimi-k284.8%
Humanity's Last Examreasoning
deepseek-v4-flash-073138.6%
kimi-k223.8%

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-v4-flash-0731kimi-k2
Input price / 1M$0.44$0.60
Output price / 1M$1.32$2.50
Context window1M tokens262K tokens
Max output384K tokens262K tokens
Vision inputN/AN/A
Tool callingYesYes
ReasoningN/AYes
Prompt cachingYesYes
Computer useN/AN/A
ProviderDeepSeekGoogle LLC (Vertex AI)

Questions people ask

Is deepseek-v4-flash-0731 better than kimi-k2?
deepseek-v4-flash-0731 outperforms kimi-k2 on 3 of 3 shared benchmarks. See the benchmark comparison above for specifics: deepseek-v4-flash-0731 and kimi-k2 have different strengths across reasoning, coding, math and multimodal tasks.
Which is cheaper, deepseek-v4-flash-0731 or kimi-k2?
deepseek-v4-flash-0731 is cheaper. deepseek-v4-flash-0731 costs $0.44/$1.32 per 1M input/output tokens, while kimi-k2 costs $0.60/$2.50.
Can I use deepseek-v4-flash-0731 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 "deepseek/deepseek-v4-flash-0731" and "vertex/kimi-k2", no other code changes needed.
What are the context windows?
deepseek-v4-flash-0731 supports up to 1M 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-v4-flash-0731 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.