MModelspectraIndependent AI Model Intelligence
Head-to-head · Updated 2026-09-08
Model Comparison · Head to Head

Gemini 3.1 Pro vs GPT-5.6 Terra

Gemini 3.1 Pro wins on Overall, Coding, Multimodal. Every numeric field is compared below, with a worked monthly-cost example and a pick rule for each use case.

VendorGoogle / OpenAI
Data fields15+ dimensions
Updated2026-09-08
Read~9 min
01

Verdict at a glance

Bottom line: Choose Gemini 3.1 Pro when coding depth, long-context reliability, lower refusal matter most; choose GPT-5.6 Terra when its stronger dimensions is the priority.

Choose Gemini 3.1 Pro if you…

  • Native audio and video
  • Strong scientific computing
  • Largest 2M context
  • Search integration
  • Best for: Multimodal, Scientific reasoning, Audio & video

Choose GPT-5.6 Terra if you…

  • OpenAI quality
  • Halved price
  • Rich ecosystem
  • Best for: Value, General tasks, OpenAI ecosystem
02

Head-to-head aggregate scores

Scores are 0–100, aggregated from public benchmark information and independently weighted across three leaderboards. Rank is out of 22 tracked models.

Gemini 3.1 Pro Higher overall
Google · #3 overall
Overall83
Coding74
Multimodal92
VS
GPT-5.6 Terra
OpenAI · #21 overall
Overall62
Coding70
Multimodal69

Aggregated from public sources and independently weighted; methodology on the Terms page. Scores within 3 points are treated as statistically tied.

03

Specs & pricing — every field side by side

List API prices in USD per 1M tokens. The highlighted cell is the stronger value/capability on that row.

DimensionGemini 3.1 ProGPT-5.6 TerraVerdict
VendorGoogle (US)OpenAI (US)Different vendors
Released2026.052026.07GPT-5.6 Terra is newer
Overall (rank)83 · #362 · #21Gemini 3.1 Pro +21
Coding74 · #970 · #12Gemini 3.1 Pro +4
Multimodal92 · #269 · #19Gemini 3.1 Pro +23
Context window2M1.05MGemini 3.1 Pro larger
Max output128K128KTie
Effective-context9288Gemini 3.1 Pro more reliable
Input $/1M$2$2.5Gemini 3.1 Pro cheaper
Output $/1M$12$15Gemini 3.1 Pro cheaper
Cache discountnone50% offGPT-5.6 Terra deeper
Speed~60 tok/s~60 tok/sTie
TTFT0.7s0.5sGPT-5.6 Terra snappier
Function calling7890GPT-5.6 Terra ahead
Refusal rate~12%~13%Gemini 3.1 Pro less restrictive
English9088Gemini 3.1 Pro
Chinese7572Gemini 3.1 Pro
Modalitiestext, image, audio, videotext, imagedifferent coverage
Open weightsNoNoBoth closed
Fine-tuningYesNo
Free tierGemini App free with limited quota; API free tier availableNo free API
SOC2 / no-trainyes / yesyes / yes
Private deploymentNoNoNeither

Fields drawn from vendor public documentation and the Modelspectra 22-model dataset; speed varies with network, concurrency and prompt length. Verify current pricing before purchase.

04

Dimension-by-dimension analysis

Reasoning & overall intelligence

Gemini 3.1 Pro leads the overall aggregate by 21 points (83 vs 62). That is a meaningful, not marginal, edge on hard, multi-step reasoning, while GPT-5.6 Terra remains a strong generalist that is not out of its depth on routine work.

Agentic coding

This is a modest gap: Gemini 3.1 Pro scores 74 against 70. On multi-file edits, SWE-style tickets and long-horizon agent loops Gemini 3.1 Pro needs fewer correction turns; GPT-5.6 Terra is still competent for scripts and assisted completion but trails as task complexity rises.

Multimodal

Gemini 3.1 Pro leads multimodal 92 vs 69. A concrete modality difference: Gemini 3.1 Pro additionally handles audio, video.

Speed & latency

Gemini 3.1 Pro is faster in interactive use: ~60 tok/s with 0.7s TTFT versus ~60 tok/s with 0.5s TTFT (about 1.0× the throughput). For conversational UIs where perceived responsiveness drives retention, that edge is a real product factor even when raw reasoning is lower.

Context: window vs usable recall

Nominal windows are 2M for Gemini 3.1 Pro and 1.05M for GPT-5.6 Terra. Effective-context scores point the same way as window size — Gemini 3.1 Pro is ahead on usable recall (92 vs 88), so prefer it for long-document work where details cannot be missed.

Price & total cost

Gemini 3.1 Pro is the cheaper API at $2/$12 versus GPT-5.6 Terra at $2.5/$15 per 1M input/output tokens — list input is about 1.2× lower. Cache discounts (GPT-5.6 Terra 50%) shift the effective bill, worked out below.

Chinese vs English

English: Gemini 3.1 Pro 90 vs GPT-5.6 Terra 88. Chinese: 75 vs 72. Both are US-based models; for Chinese-first workloads also compare domestic models on the leaderboard.

Tool use & ecosystem

Function-calling score: Gemini 3.1 Pro 78 vs GPT-5.6 Terra 90, so GPT-5.6 Terra has the edge on structured tool use. Fine-tuning is available from Gemini 3.1 Pro. Factor in existing SDK/plugin familiarity — switching cost often outweighs a few-point tool-use gap.

05

Cost worked example — same workload, real token math

Assume a production workload of 100M input + 30M output tokens per month, with 90% of input tokens served from cache. Figures use public list prices.

Scenario · per monthGemini 3.1 ProGPT-5.6 TerraGap
List priceno cache applied$560100M in × $2  +  30M out × $12$700100M in × $2.5  +  30M out × $151.25×gap
With caching90% of inputs cache-hit$560no published cache discount$58890M cached in × $1.25  +  10M in × $2.5  +  30M out × $151.05×gap

Illustrative model; your input/output mix and cache-hit ratio change the result. Prices are list rates before any enterprise agreement.

06

Decision tree

IF the workload is agentic or multi-file coding and a wrong first pass is expensive  →  choose Gemini 3.1 Pro (coding 74 vs 70).
IF you are cost-driven at scale on routine, high-volume workloads  →  choose Gemini 3.1 Pro ($$2/$$12 per 1M in/out).
IF long-document recall has to be near-perfect  →  choose Gemini 3.1 Pro (effective context 92 vs 88).
07

Frequently asked questions

How do costs compare at 100M tokens/month with caching?
At 100M input + 30M output with 90% of inputs cache-hit, Gemini 3.1 Pro is about $560/month and GPT-5.6 Terra about $588/month after cache discounts ($560 and $700 at list).
Does the bigger context window actually matter?
Nominal windows are Gemini 3.1 Pro (2M) and GPT-5.6 Terra (1.05M), but usable recall follows the effective-context score (92 vs 88). Prefer the higher effective-context model for long-document work where nothing can be missed.
Which one supports fine-tuning?
Gemini 3.1 Pro supports fine-tuning; GPT-5.6 Terra does not at this tier. If you plan to adapt the model to a narrow domain, that is a concrete differentiator.
Which handles multimodal inputs better?
Multimodal scores are 92 (Gemini 3.1 Pro) vs 69 (GPT-5.6 Terra), with modality coverage text/image/audio/video versus text/image. Match the model to the input types your product actually receives.
What is the single-line recommendation?
Choose Gemini 3.1 Pro for Multimodal, Scientific reasoning; choose GPT-5.6 Terra for Value, General tasks.
How quickly do these rankings change?
Modelspectra refreshes the aggregate as new public benchmarks and prices appear. Treat scores within 3 points as a tie and re-check before a committed purchase.