Gemini 3.7 Flash vs GPT-5.6 Terra

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

Key Takeaways

Coding tier: Gemini 3.7 Flash has a higher reference index on the selected shared benchmark configuration than GPT-5.6 Terra (111.28 vs 110.54, a 0.74-point difference). This does not by itself establish general coding superiority. Editorial tiers: Gemini 3.7 Flash = A+; 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: $1.5000 (Gemini 3.7 Flash) vs $4.4000 (GPT-5.6 Terra).
Metric Gemini 3.7 Flash GPT-5.6 Terra
Coding Index
111.28
110.54
Coding tier A+ A+
Input Price $0.75/1M $2/1M
Output Price $3.75/1M $12/1M
Context Window 1.05M 1.05M

Coding Index

Coding Index · Reference baseline: 100

Gemini 3.7 Flash 111.28
GPT-5.6 Terra 110.54

Integer index difference: 0.74

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

Gemini 3.7 Flash Tier A+

Best For
  • •Coding assistance
  • •Multi-step reasoning
Strengths
  • •Multimodal input
  • •Fast agentic workflows

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

Gemini 3.7 Flash

OpenRouter quote 2026-10-11
Benchmark evidence and ranking method · Gemini 3.7 Flash

Cross-checked

Terminal-Bench 4.0 · mini-SWE-agent · high: 11.21% · 95% CI ± 2.45%

Artificial Analysis · Gemini 3.7 Flash (high)

Test configuration: high

Terminal-Bench 2.1: 85.77% · SciCode: 57.18%

LiveBench · 2026-06-25 · gemini-3.7-flash-high

Coding: 78.89% · Agentic Coding: 58.28%

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):

Gemini 3.7 Flash: $1.5000

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: Gemini 3.7 Flash or GPT-5.6 Terra?

Gemini 3.7 Flash has a higher reference index on the selected shared benchmark configuration than GPT-5.6 Terra (111.28 vs 110.54, a 0.74-point difference). This does not by itself establish general coding superiority. Editorial tiers: Gemini 3.7 Flash = A+; GPT-5.6 Terra = A+. Tiers are separate editorial assessments and do not establish a reliable order within a tier.

How do Gemini 3.7 Flash and GPT-5.6 Terra compare in API pricing and costs?

Gemini 3.7 Flash: $0.75/1M input, $3.75/1M output (OpenRouter quote); GPT-5.6 Terra: $2/1M input, $12/1M output (OpenRouter quote). 1M input + 200K output (USD): $1.5000 vs $4.4000.

What are the context window sizes of Gemini 3.7 Flash and GPT-5.6 Terra?

Gemini 3.7 Flash provides a 1,048,576 token context window, while GPT-5.6 Terra provides 1,050,000 tokens.

Compare current models

Gemini 3.7 Flash vs Claude Opus 4.8 → Gemini 3.7 Flash vs DeepSeek V4.1 Flash → GPT-5.6 Terra vs Claude Opus 4.8 → GPT-5.6 Terra vs DeepSeek V4.1 Flash →
Try Gemini 3.7 Flash → Try GPT-5.6 Terra →