LLM Comparison

Claude Sonnet 5.5 vs GPT-6.1 Terra: benchmark scores, pricing & comparison.

Side-by-side Claude Sonnet 5.5 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: Claude Sonnet 5.5 ranks higher (#4 vs #35) and wins 4 of 4 shared benchmarks; GPT-6.1 Terra costs less ($1.00/$5.00 vs $2.00/$10.0 per 1M tokens).

Rank #4 vs #35AskClash overall scores 76.0 vs 61.7.
Pricing $2.00/$10.0 vs $1.00/$5.00Input and output token prices per 1M tokens when published.
Proprietary vs ProprietaryAnthropic vs OpenAI.

Claude Sonnet 5.5 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.

MetricClaude Sonnet 5.5GPT-6.1 Terra
Overall Score76.061.7
Leaderboard Rank#4#35
ACB76.662.1
HLE64.546.2
SWE-Pro81.3—
Terminal-Bench63.655.6
DeepSWE71.0—
GDPval-AA1844.21467.6
Finance Agent58.1—
MMMU-Pro—84.7
Input Price (per 1M tokens)$2.00$1.00
Output Price (per 1M tokens)$10.0$5.00
Context Window1M1.05M
Benchmarks Published108

Claude Sonnet 5.5 vs GPT-6.1 Terra head-to-head charts

Claude Sonnet 5.5 leads 5 and GPT-6.1 Terra leads 0 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.

Claude Sonnet 5.5GPT-6.1 Terra
Overall
76.0Claude Sonnet 5.5
61.7GPT-6.1 Terra
ACB
76.6Claude Sonnet 5.5
62.1GPT-6.1 Terra
HLE
64.5Claude Sonnet 5.5
46.2GPT-6.1 Terra
Terminal-Bench
63.6Claude Sonnet 5.5
55.6GPT-6.1 Terra
GDPval-AA
1844.2Claude Sonnet 5.5
1467.6GPT-6.1 Terra
Claude Sonnet 5.5
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 Claude Sonnet 5.5 and GPT-6.1 Terra comparisons

Explore how Claude Sonnet 5.5 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.