LLM Comparison

Kimi K3.1 Pro vs Kimi K2.7: benchmark scores, pricing & comparison.

Side-by-side Kimi K3.1 Pro 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: Kimi K3.1 Pro ranks higher (#4 vs #120) and wins 4 of 4 shared benchmarks; Kimi K2.7 costs less ($0.95/$4.00 vs $2.00/$10.0 per 1M tokens).

Rank #4 vs #120AskClash overall scores 75.5 vs 37.9.
Pricing $2.00/$10.0 vs $0.95/$4.00Input and output token prices per 1M tokens when published.
Open Weight vs Open WeightMoonshot AI vs Moonshot AI.

Kimi K3.1 Pro vs Kimi K2.7 benchmark comparison

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

MetricKimi K3.1 ProKimi K2.7
Overall Score75.537.9
Leaderboard Rank#4#120
ACB—43.0
RWT—7.5
HLE70.035.0
GPQA94.289.6
IFEval—63.1
Terminal-Bench65.7—
DeepSWE75.530.5
GDPval-AA1846.01035.8
MCP Atlas—76.0
Finance Agent58.6—
CharXiv89.0—
MMMU-Pro84.1—
Tau2—90.1
Input Price (per 1M tokens)$2.00$0.95
Output Price (per 1M tokens)$10.0$4.00
Context Window1M256K
Benchmarks Published99

Kimi K3.1 Pro vs Kimi K2.7 head-to-head charts

Kimi K3.1 Pro leads 5 and Kimi K2.7 leads 0 of 5 shared benchmarks. Kimi K2.7 is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

Kimi K3.1 ProKimi K2.7
Overall
75.5Kimi K3.1 Pro
37.9Kimi K2.7
HLE
70.0Kimi K3.1 Pro
35.0Kimi K2.7
GPQA
94.2Kimi K3.1 Pro
89.6Kimi K2.7
DeepSWE
75.5Kimi K3.1 Pro
30.5Kimi K2.7
GDPval-AA
1846.0Kimi K3.1 Pro
1035.8Kimi K2.7
Kimi K3.1 Pro
Input$2.00
Output$10.0
Workload$4.00
Context1M
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 Kimi K3.1 Pro and Kimi K2.7 comparisons

Explore how Kimi K3.1 Pro 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.