Claude Opus 4.8 vs GPT-5.6 Terra
Select two models to compare their coding performance, pricing, and features.
Key Takeaways
| Metric | Claude Opus 4.8 | GPT-5.6 Terra |
|---|---|---|
| Coding Index | 107.83 | 110.54 |
| Coding tier | A+ | A+ |
| Input Price | $5/1M | $2/1M |
| Output Price | $25/1M | $12/1M |
| Context Window | 1M | 1.05M |
Coding Index
Coding Index · Reference baseline: 100
Integer index difference: 2.71
DeepSeek V4 Flash 0731 = 100. The index reflects two shared tests, not capability multiples or statistically established gaps.
Verdict
Both models share an editorial tier. The evidence does not establish a reliable order within it; compare them on your own projects.
Capabilities & Use Cases Comparison
Claude Opus 4.8 Tier A+
- •Complex architecture
- •Multi-file refactoring
- •Code review
- •Claude Code
- •Best-in-class code reasoning and architecture design
- •Excels at complex multi-file refactoring
- •Superior at understanding large codebases
- •Strong at following nuanced instructions
GPT-5.6 Terra Tier A+
- •Autonomous agentic workflows
- •Large-scale refactoring
- •System architecture design
- •Codex CLI complex tasks
- •Best-in-class deep reasoning for multi-file refactors
- •Excels at long-horizon agentic coding tasks
- •Superior architectural design and system planning
- •Strong tool-use and autonomous task completion
Claude Opus 4.8
Benchmark evidence and ranking method · Claude Opus 4.8
Cross-checked
Terminal-Bench 4.0 · Claude Code · max: 23.64% · 95% CI ± 3.56%
Artificial Analysis · Claude Opus 4.8 (Adaptive Reasoning, Max Effort)
Test configuration: max
Terminal-Bench 2.1: 84.64% · SciCode: 54.40%
LiveBench · 2026-06-25 · claude-opus-4-8-max-effort
Coding: 81.83% · Agentic Coding: 50.50%
ModelRank assigns editorial tiers using public coding evidence, not automatic index thresholds or statistically proven classes. Price, speed, context size and sponsorship do not affect tiers.
DeepSeek V4 Flash 0731 = 100. The index reflects two shared tests, not capability multiples or statistically established gaps.
Results reflect particular model configurations. Agent harnesses, reasoning effort, tools, budgets and tasks may differ. Close indices do not establish superiority. B describes a relative position in this selected group, not an inability to code.
Data checked: 2026-09-10. Snapshot date is the retrieval date, not necessarily the evaluation date.
GPT-5.6 Terra
Benchmark evidence and ranking method · GPT-5.6 Terra
Cross-checked
Terminal-Bench 4.0 · Codex · max: 21.52% · 95% CI ± 3.25%
Artificial Analysis · GPT-5.6 Terra (max)
Test configuration: max
Terminal-Bench 2.1: 88.01% · SciCode: 54.98%
LiveBench · 2026-06-25 · gpt-5.6-terra-max
Coding: 78.25% · Agentic Coding: 54.95%
ModelRank assigns editorial tiers using public coding evidence, not automatic index thresholds or statistically proven classes. Price, speed, context size and sponsorship do not affect tiers.
DeepSeek V4 Flash 0731 = 100. The index reflects two shared tests, not capability multiples or statistically established gaps.
Results reflect particular model configurations. Agent harnesses, reasoning effort, tools, budgets and tasks may differ. Close indices do not establish superiority. B describes a relative position in this selected group, not an inability to code.
Data checked: 2026-09-10. Snapshot date is the retrieval date, not necessarily the evaluation date.
Results reflect particular model configurations. Agent harnesses, reasoning effort, tools, budgets and tasks may differ. Close indices do not establish superiority. B describes a relative position in this selected group, not an inability to code.
API cost estimate
1M input + 200K output (USD):
Claude Opus 4.8: $10.0000
GPT-5.6 Terra: $4.4000
Estimates use the displayed channel and time band; DeepSeek uses peak rates. Cache discounts, tool fees, long-context price tiers and uncounted additional tokens are excluded.
Frequently Asked Questions
Which model is better for coding: Claude Opus 4.8 or GPT-5.6 Terra?
GPT-5.6 Terra has a higher reference index on the selected shared benchmark configuration than Claude Opus 4.8 (110.54 vs 107.83, a 2.71-point difference). This does not by itself establish general coding superiority. Editorial tiers: Claude Opus 4.8 = A+; GPT-5.6 Terra = A+. Tiers are separate editorial assessments and do not establish a reliable order within a tier.
How do Claude Opus 4.8 and GPT-5.6 Terra compare in API pricing and costs?
Claude Opus 4.8: $5/1M input, $25/1M output (OpenRouter quote); GPT-5.6 Terra: $2/1M input, $12/1M output (OpenRouter quote). 1M input + 200K output (USD): $10.0000 vs $4.4000.
What are the context window sizes of Claude Opus 4.8 and GPT-5.6 Terra?
Claude Opus 4.8 provides a 1,000,000 token context window, while GPT-5.6 Terra provides 1,050,000 tokens.