LLM Comparison

GPT-5.6 Terra vs Claude Sonnet 5: benchmark scores, pricing & comparison.

Side-by-side GPT-5.6 Terra vs Claude Sonnet 5 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 #8 vs #9AskClash overall scores 76.3 vs 76.2.
Pricing $2.50/$15.0 vs $3.00/$15.0Input and output token prices per 1M tokens when published.
Proprietary vs ProprietaryOpenAI vs Anthropic.

GPT-5.6 Terra vs Claude Sonnet 5 benchmark comparison

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

MetricGPT-5.6 TerraClaude Sonnet 5
Overall Score76.376.2
Leaderboard Rank#8#9
RWT8.58.5
Coding Agent Index77.0
HLE57.4
GPQA92.991.1
SWE-bench85.2
SWE-Pro63.463.2
SWE-Atlas81.0
Terminal-Bench87.480.4
DeepSWE69.653.8
OSWorld81.2
Finance Agent52.4
CharXiv88.3
MMMU-Pro80.777.3
ARC-AGI 283.9
Tau286.3
MRCR89.6
Input Price (per 1M tokens)$2.50$3.00
Output Price (per 1M tokens)$15.0$15.0
Context Window1M1M
Benchmarks Published1311

GPT-5.6 Terra vs Claude Sonnet 5 head-to-head charts

GPT-5.6 Terra leads 6 and Claude Sonnet 5 leads 0 of 7 shared benchmarks. GPT-5.6 Terra is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

GPT-5.6 TerraClaude Sonnet 5
Overall
76.3GPT-5.6 Terra
76.2Claude Sonnet 5
RWT
8.5GPT-5.6 Terra
8.5Claude Sonnet 5
GPQA
92.9GPT-5.6 Terra
91.1Claude Sonnet 5
SWE-Pro
63.4GPT-5.6 Terra
63.2Claude Sonnet 5
Terminal-Bench
87.4GPT-5.6 Terra
80.4Claude Sonnet 5
DeepSWE
69.6GPT-5.6 Terra
53.8Claude Sonnet 5
MMMU-Pro
80.7GPT-5.6 Terra
77.3Claude Sonnet 5
GPT-5.6 Terra
Input$2.50
Output$15.0
Workload$5.50
Context1M
Claude Sonnet 5
Input$3.00
Output$15.0
Workload$6.00
Context1M

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

More GPT-5.6 Terra and Claude Sonnet 5 comparisons

Explore how GPT-5.6 Terra and Claude Sonnet 5 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.