LLM Comparison

Kimi K3.1 Pro vs GPT-6.1 Terra: benchmark scores, pricing & comparison.

Side-by-side Kimi K3.1 Pro vs GPT-6.1 Terra 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 (#1 vs #35) and wins 3 of 4 shared benchmarks; GPT-6.1 Terra costs less ($1.00/$5.00 vs $2.00/$10.0 per 1M tokens).

Rank #1 vs #35AskClash overall scores 80.3 vs 61.7.
Pricing $2.00/$10.0 vs $1.00/$5.00Input and output token prices per 1M tokens when published.
Open Weight vs ProprietaryMoonshot AI vs OpenAI.

Kimi K3.1 Pro vs GPT-6.1 Terra benchmark comparison

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

MetricKimi K3.1 ProGPT-6.1 Terra
Overall Score80.361.7
Leaderboard Rank#1#35
ACB—62.1
HLE70.046.2
GPQA94.2—
Terminal-Bench65.755.6
DeepSWE75.5—
GDPval-AA1846.01467.6
Finance Agent58.6—
CharXiv89.0—
MMMU-Pro84.184.7
Input Price (per 1M tokens)$2.00$1.00
Output Price (per 1M tokens)$10.0$5.00
Context Window1M1.05M
Benchmarks Published118

Kimi K3.1 Pro vs GPT-6.1 Terra head-to-head charts

Kimi K3.1 Pro leads 4 and GPT-6.1 Terra leads 1 of 5 shared benchmarks. GPT-6.1 Terra is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

Kimi K3.1 ProGPT-6.1 Terra
Overall
80.3Kimi K3.1 Pro
61.7GPT-6.1 Terra
HLE
70.0Kimi K3.1 Pro
46.2GPT-6.1 Terra
Terminal-Bench
65.7Kimi K3.1 Pro
55.6GPT-6.1 Terra
GDPval-AA
1846.0Kimi K3.1 Pro
1467.6GPT-6.1 Terra
MMMU-Pro
84.1Kimi K3.1 Pro
84.7GPT-6.1 Terra
Kimi K3.1 Pro
Input$2.00
Output$10.0
Workload$4.00
Context1M
GPT-6.1 Terra
Input$1.00
Output$5.00
Workload$2.00
Context1.05M

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

More Kimi K3.1 Pro and GPT-6.1 Terra comparisons

Explore how Kimi K3.1 Pro and GPT-6.1 Terra 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.