How do Claude Opus 4.8 and GPT-5.6 Terra compare on coding benchmarks?

The comparison table shows SWE-bench, SWE-Pro, SWE-Atlas, Terminal-Bench, DeepSWE, and coding agent index scores for both Claude Opus 4.8 and GPT-5.6 Terra when publicly disclosed.

LLM Comparison

Claude Opus 4.8 vs GPT-5.6 Terra: benchmark scores, pricing & comparison.

Q: Which is better: Claude Opus 4.8 or GPT-5.6 Terra?

AskClash compares Claude Opus 4.8 and GPT-5.6 Terra side by side across SWE-bench, GPQA, HLE, Terminal-Bench, coding agent scores, token pricing, and context window so you can see which model wins on each benchmark.

Side-by-side Claude Opus 4.8 vs GPT-5.6 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.

Open live leaderboard Claude Opus 4.8 model page GPT-5.6 Terra model page

Rank #4 vs #8AskClash overall scores 80.4 vs 76.3.

Pricing $5.00/$25.0 vs $2.50/$15.0Input and output token prices per 1M tokens when published.

Proprietary vs ProprietaryAnthropic vs OpenAI.

Claude Opus 4.8 vs GPT-5.6 Terra benchmark comparison

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

Metric	Claude Opus 4.8	GPT-5.6 Terra
Overall Score	80.4	76.3
Leaderboard Rank	#4	#8
RWT	9.0	8.5
Coding Agent Index	72.5	77.0
HLE	57.9	—
GPQA	93.6	92.9
IFEval	62.2	—
SWE-bench	88.6	—
SWE-Pro	69.2	63.4
SWE-Atlas	82.5	81.0
Terminal-Bench	74.6	87.4
DeepSWE	59.0	69.6
OSWorld	83.4	—
MCP Atlas	82.2	—
Finance Agent	53.9	52.4
CharXiv	89.9	—
MMMU-Pro	—	80.7
ARC-AGI 2	72.1	83.9
Tau2	94.4	86.3
MRCR	—	89.6
Input Price (per 1M tokens)	$5.00	$2.50
Output Price (per 1M tokens)	$25.0	$15.0
Context Window	1M	1M
Benchmarks Published	17	13

Claude Opus 4.8 vs GPT-5.6 Terra head-to-head charts

Claude Opus 4.8 leads 7 and GPT-5.6 Terra leads 4 of 11 shared benchmarks. GPT-5.6 Terra is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

Claude Opus 4.8GPT-5.6 Terra

Overall

80.4Claude Opus 4.8

76.3GPT-5.6 Terra

RWT

9.0Claude Opus 4.8

8.5GPT-5.6 Terra

Coding Agent Index

72.5Claude Opus 4.8

77.0GPT-5.6 Terra

GPQA

93.6Claude Opus 4.8

92.9GPT-5.6 Terra

SWE-Pro

69.2Claude Opus 4.8

63.4GPT-5.6 Terra

SWE-Atlas

82.5Claude Opus 4.8

81.0GPT-5.6 Terra

Terminal-Bench

74.6Claude Opus 4.8

87.4GPT-5.6 Terra

DeepSWE

59.0Claude Opus 4.8

69.6GPT-5.6 Terra

Finance Agent

53.9Claude Opus 4.8

52.4GPT-5.6 Terra

ARC-AGI 2

72.1Claude Opus 4.8

83.9GPT-5.6 Terra

Tau2

94.4Claude Opus 4.8

86.3GPT-5.6 Terra

Claude Opus 4.8

Input$5.00

Output$25.0

Workload$10

Context1M

GPT-5.6 Terra

Input$2.50

Output$15.0

Workload$5.50

Context1M

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

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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.