LLM Comparison

Kimi K2.7 vs DeepSeek V4 Pro: benchmark scores, pricing & comparison.

Side-by-side Kimi K2.7 vs DeepSeek V4 Pro comparison across SWE-bench, GPQA, HLE, Terminal-Bench, coding agent scores, token pricing, context window, and AskClash RWT. Green marks the winner on each benchmark.

Rank #19 vs #53AskClash overall scores 56.8 vs 20.0.
Pricing $0.95/$4.00 vs $1.74/$3.48Input and output token prices per 1M tokens when published.
Open Weight vs Open WeightMoonshot AI vs DeepSeek.

Kimi K2.7 vs DeepSeek V4 Pro benchmark comparison

Green cells highlight the winning model for each metric. Scores are cached from the AskClash LLM leaderboard snapshot.

MetricKimi K2.7DeepSeek V4 Pro
Overall Score56.820.0
Leaderboard Rank#19#53
RWT7.57.0
HLE54.07.7
GPQA90.572.9
MATH-50064.5
SWE-bench80.273.6
SWE-Pro58.652.1
Terminal-Bench66.759.1
DeepSWE30.5
OSWorld73.1
MCP Atlas76.069.4
Finance Agent44.944.1
CharXiv80.4
MMMU-Pro79.4
Tau290.1
MRCR44.7
Input Price (per 1M tokens)$0.95$1.74
Output Price (per 1M tokens)$4.00$3.48
Context Window256K1M
Benchmarks Published138

Kimi K2.7 vs DeepSeek V4 Pro head-to-head charts

Kimi K2.7 leads 9 and DeepSeek V4 Pro leads 0 of 9 shared benchmarks. Kimi K2.7 is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

Kimi K2.7DeepSeek V4 Pro
Overall
56.8Kimi K2.7
20.0DeepSeek V4 Pro
RWT
7.5Kimi K2.7
7.0DeepSeek V4 Pro
HLE
54.0Kimi K2.7
7.7DeepSeek V4 Pro
GPQA
90.5Kimi K2.7
72.9DeepSeek V4 Pro
SWE-bench
80.2Kimi K2.7
73.6DeepSeek V4 Pro
SWE-Pro
58.6Kimi K2.7
52.1DeepSeek V4 Pro
Terminal-Bench
66.7Kimi K2.7
59.1DeepSeek V4 Pro
MCP Atlas
76.0Kimi K2.7
69.4DeepSeek V4 Pro
Finance Agent
44.9Kimi K2.7
44.1DeepSeek V4 Pro
Kimi K2.7
Input$0.95
Output$4.00
Workload$1.75
Context256K
DeepSeek V4 Pro
Input$1.74
Output$3.48
Workload$2.44
Context1M

Workload = published cost of 1M input + 200K output tokens. Open the live leaderboard for interactive compare charts.

More Kimi K2.7 and DeepSeek V4 Pro comparisons

Explore how Kimi K2.7 and DeepSeek V4 Pro stack up against other top-ranked LLMs.

How to read this comparison

Benchmark scores

Higher is better for all benchmark scores (SWE-bench, GPQA, HLE, Terminal-Bench, etc.). Green marks the model with the higher score.

Token pricing

Lower is better for input and output prices. Green marks the cheaper model per 1M tokens.

Coverage matters

Models with fewer disclosed benchmark cells may have inflated percentile scores. Check the benchmark cell count for context.

This comparison page is generated from the AskClash LLM leaderboard cache. Open the live leaderboard for real-time scores and interactive filtering.