Skip to content
ModelCap

Head-to-head comparison

Gemini 3.8 Flash vs GPT-5.6 Terra

Gemini 3.8 Flash (Google) and GPT-5.6 Terra (OpenAI) compared on the ModelCap Index, API price, context window, provider availability, weight access and every public benchmark board they share. Figures are the same ones shown on the live rankings; nothing here is a hidden score.

Snapshot as of 18 October 2026

As of 18 October 2026, Gemini 3.8 Flash holds the stronger ModelCap Index position (#5 vs #28); and Gemini 3.8 Flash is 2.9× cheaper per output token ($4.13/1M vs $12.00/1M).

Which should you choose?

Choose Gemini 3.8 Flash if…

  • you want the stronger overall ModelCap Index position — #5 against #28 (83.5 vs 75.3 points).
  • API cost matters — $4.13/1M output tokens against $12.00/1M, about 2.9× cheaper.
  • your workload is prompt-heavy — input tokens cost $0.825/1M against $2.00/1M.
  • Arena text is your yardstick — 1496 against 1466.
  • Arena coding is your yardstick — 1528 against 1517.
  • LMArena Agent is your yardstick — 3.7% against 0.4%.
  • ARC-AGI-2 is your yardstick — 89.2% against 83.9%.
  • ARC-AGI-3 is your yardstick — 10.4% against 0.8%.
  • AA Humanity's Last Exam is your yardstick — 47.8% against 42.9%.
  • you want the more recently listed model — Gemini 3.8 Flash was listed 2 September 2026, GPT-5.6 Terra 9 July 2026.

Choose GPT-5.6 Terra if…

  • you want provider choice — 3 listed API providers against 2.
  • AA Intelligence Index is your yardstick — 42.1 against 40.9.
  • AA Terminal-Bench 2.1 is your yardstick — 88.0% against 87.6%.
  • Terminal-Bench 4.0 is your yardstick — 21.5% against 19.1%.

Gemini 3.8 Flash vs GPT-5.6 Terra: specs, pricing and context

Specification comparison of Gemini 3.8 Flash and GPT-5.6 Terra
FieldGemini 3.8 FlashGoogleGPT-5.6 TerraOpenAI
ModelCap Index position#5#28
Index score83.575.3
EvidenceMeasuredMeasured
Input price / 1M tokens$0.825$2.00
Output price / 1M tokens$4.13$12.00
Context window1M tokens1M tokens
Declared route output cap66K128K
API providers listed23
Weight accessNo linked weightsNo linked weights
Input modalitiesText, Image, Video, File, AudioFile, Image, Text
First listed2 September 20269 July 2026
PublisherGoogleOpenAI

Evidence: 5 public benchmark observations across 5 boards · 5 public benchmark observations across 5 boards. Prices are the lowest listed API offer per million tokens observed on the OpenRouter catalogue.

Benchmark scores: Gemini 3.8 Flash vs GPT-5.6 Terra

Public benchmark boards where Gemini 3.8 Flash or GPT-5.6 Terra has a published result
BoardGemini 3.8 FlashGPT-5.6 TerraLeads
Arena textArena (LMArena)14961492–1501High14661462–1471X-HighGemini 3.8 Flash
Arena codingArena (LMArena)15281521–1535High15171510–1523X-HighGemini 3.8 Flash
LMArena AgentLMArena3.7%2.6%–4.8%High0.4%-0.9%–1.7%X-HighGemini 3.8 Flash
ARC-AGI-2ARC Prize Foundation89.2%High83.9%MaxGemini 3.8 Flash
ARC-AGI-3ARC Prize Foundation10.4%High0.8%MaxGemini 3.8 Flash
AA Intelligence IndexArtificial Analysis40.9High42.1MaxGPT-5.6 Terra
AA Terminal-Bench 2.1Artificial Analysis87.6%High88.0%MaxGPT-5.6 Terra
AA τ²-Bench TelecomArtificial Analysis—86.3%MaxOnly one result
AA Humanity's Last ExamArtificial Analysis47.8%High42.9%MaxGemini 3.8 Flash
Terminal-Bench 4.0Terminal-Bench (Laude Institute)19.1%15.7%–22.5%mini-SWE-agent · High21.5%18.3%–24.8%Codex · MaxGPT-5.6 Terra

Scores are the sources' own published figures for each model's best evaluated configuration; ModelCap never re-runs a benchmark.

Want a different pairing? Open the interactive comparison tool to swap either model for any current ranked language model.

Gemini 3.8 Flash vs GPT-5.6 Terra: common questions

Is Gemini 3.8 Flash better than GPT-5.6 Terra?

Gemini 3.8 Flash ranks higher on the ModelCap Index as of 18 October 2026: #5 against #28. That is a capability ranking built from public benchmark evidence with published uncertainty; whether it is "better" for you also depends on price, context and where you can run it. Their published uncertainty intervals overlap, so the rank difference alone does not establish a reliable capability advantage for your workload.

Is Gemini 3.8 Flash cheaper than GPT-5.6 Terra?

Gemini 3.8 Flash is cheaper on output tokens: $4.13/1M against $12.00/1M. Input tokens are $0.825/1M for Gemini 3.8 Flash and $2.00/1M for GPT-5.6 Terra. Prices are the lowest listed API offer ModelCap observed, in USD per million tokens. For 1,000 requests with 2,000 input and 500 output tokens each (2M input + 0.5M output), the listed-rate estimate is $3.71 for Gemini 3.8 Flash versus $10.00 for GPT-5.6 Terra. Gemini 3.8 Flash costs 62.9% less in this scenario. This excludes caching, batch discounts, prompt-length tiers, tool charges and retries; verify the selected endpoint before budgeting.

Which has the bigger context window, Gemini 3.8 Flash or GPT-5.6 Terra?

Both publish a 1M-token context window. OpenRouter catalogue or provider-endpoint declaration. Limits can vary by provider and prompt length; a model-wide maximum has not been independently verified.

Which is better for coding, Gemini 3.8 Flash or GPT-5.6 Terra?

Arena coding: Gemini 3.8 Flash 1528, GPT-5.6 Terra 1517 — Gemini 3.8 Flash leads. AA Terminal-Bench 2.1: Gemini 3.8 Flash 87.6%, GPT-5.6 Terra 88.0% — GPT-5.6 Terra leads. Terminal-Bench 4.0: Gemini 3.8 Flash 19.1%, GPT-5.6 Terra 21.5% — GPT-5.6 Terra leads.

Are Gemini 3.8 Flash and GPT-5.6 Terra open-weight models?

Gemini 3.8 Flash: No linked weights. GPT-5.6 Terra: No linked weights. Open weights mean the checkpoint can be downloaded and self-hosted under its licence. A missing weight link does not identify every API or chat product that offers a model.

Where can I run Gemini 3.8 Flash and GPT-5.6 Terra?

ModelCap currently lists 2 API providers for Gemini 3.8 Flash and 3 for GPT-5.6 Terra, from the OpenRouter catalogue snapshot the site serves; each model page lists the providers and their prices.

How current is this Gemini 3.8 Flash vs GPT-5.6 Terra comparison?

Every figure and ModelCap Index position comes from the same published snapshot dated 18 October 2026. Publication and page updates happen separately; use the snapshot date shown on the page.

Explore further