LLM Comparison

Grok 4.5 vs Kimi K2.7: benchmark scores, pricing & comparison.

Side-by-side Grok 4.5 vs Kimi K2.7 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.

Verdict: Grok 4.5 ranks higher (#32 vs #41) and wins 5 of 5 shared benchmarks; Kimi K2.7 costs less ($0.95/$4.00 vs $2.00/$6.00 per 1M tokens).

Rank #32 vs #41AskClash overall scores 52.2 vs 41.9.
Pricing $2.00/$6.00 vs $0.95/$4.00Input and output token prices per 1M tokens when published.
Proprietary vs Open WeightxAI vs Moonshot AI.

Grok 4.5 vs Kimi K2.7 benchmark comparison

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

MetricGrok 4.5Kimi K2.7
Overall Score52.241.9
Leaderboard Rank#32#41
ACB57.643.0
RWT9.07.5
Coding Agent Index76.4—
HLE40.335.0
GPQA93.189.6
IFEval—63.1
SWE-Pro64.7—
SWE-Atlas83.9—
Terminal-Bench10.6—
DeepSWE—30.5
GDPval-AA1542.81040.1
MCP Atlas—76.0
Finance Agent48.3—
MMMU-Pro80.4—
ARC-AGI 252.6—
Tau2—90.1
Input Price (per 1M tokens)$2.00$0.95
Output Price (per 1M tokens)$6.00$4.00
Context Window500K256K
Benchmarks Published149

Grok 4.5 vs Kimi K2.7 head-to-head charts

Grok 4.5 leads 6 and Kimi K2.7 leads 0 of 6 shared benchmarks. Kimi K2.7 is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

Grok 4.5Kimi K2.7
Overall
52.2Grok 4.5
41.9Kimi K2.7
ACB
57.6Grok 4.5
43.0Kimi K2.7
RWT
9.0Grok 4.5
7.5Kimi K2.7
HLE
40.3Grok 4.5
35.0Kimi K2.7
GPQA
93.1Grok 4.5
89.6Kimi K2.7
GDPval-AA
1542.8Grok 4.5
1040.1Kimi K2.7
Grok 4.5
Input$2.00
Output$6.00
Workload$3.20
Context500K
Kimi K2.7
Input$0.95
Output$4.00
Workload$1.75
Context256K

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

More Grok 4.5 and Kimi K2.7 comparisons

Explore how Grok 4.5 and Kimi K2.7 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.