GPT-5.6 Sol vs GPT-5.6 Terra

Select two models to compare their coding performance, pricing, and features.

Key Takeaways

Coding tier: GPT-5.6 Sol has a higher reference index on the selected shared benchmark configuration than GPT-5.6 Terra (112.62 vs 110.54, a 2.08-point difference). This does not by itself establish general coding superiority. Editorial tiers: GPT-5.6 Sol = S; GPT-5.6 Terra = A+. Tiers are separate editorial assessments and do not establish a reliable order within a tier.
API cost estimate: Estimated 1M in / 200K out tokens: $4.0000 (GPT-5.6 Sol) vs $4.4000 (GPT-5.6 Terra).
Metric GPT-5.6 Sol GPT-5.6 Terra
Coding Index
112.62
110.54
Coding tier S A+
Input Price $2/1M $2/1M
Output Price $10/1M $12/1M
Context Window 1.05M 1.05M

Coding Index

Coding Index · Reference baseline: 100

GPT-5.6 Sol 112.62
GPT-5.6 Terra 110.54

Integer index difference: 2.08

DeepSeek V4 Flash 0731 = 100. The index reflects two shared tests, not capability multiples or statistically established gaps.

Verdict

The models have different editorial tier assessments. The index is supplementary and cannot predict performance on every development task.

Capabilities & Use Cases Comparison

GPT-5.6 Sol Tier S

Best For
  • •GitHub Copilot
  • •Algorithm design
  • •Full-stack development
  • •Codex CLI
Strengths
  • •Top-tier code generation across all languages
  • •Excellent algorithm design and implementation
  • •Outstanding at explaining code concepts
  • •Great ecosystem integration (Copilot, Codex)

GPT-5.6 Terra Tier A+

Best For
  • •Autonomous agentic workflows
  • •Large-scale refactoring
  • •System architecture design
  • •Codex CLI complex tasks
Strengths
  • •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

GPT-5.6 Sol

OpenRouter quote 2026-10-11
Benchmark evidence and ranking method · GPT-5.6 Sol

Cross-checked

Terminal-Bench 4.0 · Codex · max: 37.27% · 95% CI ± 3.78%

Artificial Analysis · GPT-5.6 Sol (max)

Test configuration: max

Terminal-Bench 2.1: 88.01% · SciCode: 57.06%

LiveBench · 2026-06-25 · gpt-5.6-sol-max

Coding: 83.94% · Agentic Coding: 56.21%

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

OpenRouter quote 2026-10-11
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):

GPT-5.6 Sol: $4.0000

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: GPT-5.6 Sol or GPT-5.6 Terra?

GPT-5.6 Sol has a higher reference index on the selected shared benchmark configuration than GPT-5.6 Terra (112.62 vs 110.54, a 2.08-point difference). This does not by itself establish general coding superiority. Editorial tiers: GPT-5.6 Sol = S; GPT-5.6 Terra = A+. Tiers are separate editorial assessments and do not establish a reliable order within a tier.

How do GPT-5.6 Sol and GPT-5.6 Terra compare in API pricing and costs?

GPT-5.6 Sol: $2/1M input, $10/1M output (OpenRouter quote); GPT-5.6 Terra: $2/1M input, $12/1M output (OpenRouter quote). 1M input + 200K output (USD): $4.0000 vs $4.4000.

What are the context window sizes of GPT-5.6 Sol and GPT-5.6 Terra?

GPT-5.6 Sol provides a 1,050,000 token context window, while GPT-5.6 Terra provides 1,050,000 tokens.

Compare current models

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Try GPT-5.6 Sol → Try GPT-5.6 Terra →