LLM Comparison

Kimi K3 vs Gemini 3.1 Pro: benchmark scores, pricing & comparison.

Side-by-side Kimi K3 vs Gemini 3.1 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 #3 vs #24AskClash overall scores 82.3 vs 51.7.
Pricing $3.00/$15.0 vs $2.00/$12.0Input and output token prices per 1M tokens when published.
Open Weight vs ProprietaryMoonshot AI vs Google.

Kimi K3 vs Gemini 3.1 Pro benchmark comparison

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

MetricKimi K3Gemini 3.1 Pro
Overall Score82.351.7
Leaderboard Rank#3#24
Coding Agent Index42.7
HLE43.551.4
GPQA93.594.3
SWE-bench80.6
SWE-Pro54.2
Terminal-Bench88.368.5
DeepSWE67.511.8
MCP Atlas84.269.2
Finance Agent43.0
CharXiv84.880.2
MMMU-Pro81.683.9
ARC-AGI 277.1
Tau299.3
MRCR84.9
Input Price (per 1M tokens)$3.00$2.00
Output Price (per 1M tokens)$15.0$12.0
Context Window1M1M
Benchmarks Published916

Kimi K3 vs Gemini 3.1 Pro head-to-head charts

Kimi K3 leads 5 and Gemini 3.1 Pro leads 3 of 8 shared benchmarks. Gemini 3.1 Pro is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

Kimi K3Gemini 3.1 Pro
Overall
82.3Kimi K3
51.7Gemini 3.1 Pro
HLE
43.5Kimi K3
51.4Gemini 3.1 Pro
GPQA
93.5Kimi K3
94.3Gemini 3.1 Pro
Terminal-Bench
88.3Kimi K3
68.5Gemini 3.1 Pro
DeepSWE
67.5Kimi K3
11.8Gemini 3.1 Pro
MCP Atlas
84.2Kimi K3
69.2Gemini 3.1 Pro
CharXiv
84.8Kimi K3
80.2Gemini 3.1 Pro
MMMU-Pro
81.6Kimi K3
83.9Gemini 3.1 Pro
Kimi K3
Input$3.00
Output$15.0
Workload$6.00
Context1M
Gemini 3.1 Pro
Input$2.00
Output$12.0
Workload$4.40
Context1M

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

More Kimi K3 and Gemini 3.1 Pro comparisons

Explore how Kimi K3 and Gemini 3.1 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.