LLM Comparison

Kimi K3.1 Pro vs LongCat 2.0: benchmark scores, pricing & comparison.

Side-by-side Kimi K3.1 Pro vs LongCat 2.0 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 #143) and wins 3 of 3 shared benchmarks; LongCat 2.0 costs less ($0.75/$2.95 vs $2.00/$10.0 per 1M tokens).

Rank #4 vs #143AskClash overall scores 75.5 vs 26.5.
Pricing $2.00/$10.0 vs $0.75/$2.95Input and output token prices per 1M tokens when published.
Open Weight vs Open WeightMoonshot AI vs Meituan.

Kimi K3.1 Pro vs LongCat 2.0 benchmark comparison

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

MetricKimi K3.1 ProLongCat 2.0
Overall Score75.526.5
Leaderboard Rank#4#143
HLE70.033.7
GPQA94.288.9
IFEval—90.0
SWE-Pro—59.5
Terminal-Bench65.7—
DeepSWE75.5—
GDPval-AA1846.0869.8
Finance Agent58.6—
CharXiv89.0—
MMMU-Pro84.1—
Tau2—88.2
Input Price (per 1M tokens)$2.00$0.75
Output Price (per 1M tokens)$10.0$2.95
Context Window1M1M
Benchmarks Published97

Kimi K3.1 Pro vs LongCat 2.0 head-to-head charts

Kimi K3.1 Pro leads 4 and LongCat 2.0 leads 0 of 4 shared benchmarks. LongCat 2.0 is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

Kimi K3.1 ProLongCat 2.0
Overall
75.5Kimi K3.1 Pro
26.5LongCat 2.0
HLE
70.0Kimi K3.1 Pro
33.7LongCat 2.0
GPQA
94.2Kimi K3.1 Pro
88.9LongCat 2.0
GDPval-AA
1846.0Kimi K3.1 Pro
869.8LongCat 2.0
Kimi K3.1 Pro
Input$2.00
Output$10.0
Workload$4.00
Context1M
LongCat 2.0
Input$0.75
Output$2.95
Workload$1.34
Context1M

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

More Kimi K3.1 Pro and LongCat 2.0 comparisons

Explore how Kimi K3.1 Pro and LongCat 2.0 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.