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Gemini 3.6 Flash vs GPT-5.6 Terra

Compare Gemini 3.6 Flash and GPT-5.6 Terra side-by-side. See how these vision models stack up in Open Prompt, Classification, Image Captioning, OCR, and Object Detection.

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GoogleGemini 3.6 Flash
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OpenAIGPT-5.6 Terra
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Models in this comparison

Gemini 3.6 Flash vs GPT-5.6 Terra on Vision Evals

Gemini 3.6 Flash scores higher on 4 of the six Vision Evals tasks.

The widest gap is Reasoning, where Gemini 3.6 Flash leads 80.1% to 59.6%.

Overall, Gemini 3.6 Flash averages 83.1% (#4 of 25) against 72.4% (#12 of 25) for GPT-5.6 Terra.

GPT-5.6 Terra is cheaper ($0.0044 vs $0.0063 per sample), while Gemini 3.6 Flash is faster (4.7s vs 7.2s per sample).

Gemini 3.6 FlashGPT-5.6 Terra

Gemini 3.6 Flash vs GPT-5.6 Terra Comparison Table

Evals updated August 6, 2026Pricing updated August 11, 2026

PropertyGemini 3.6 FlashGPT-5.6 Terra
OrganizationGoogleOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateJul 2026Jul 2026
Context Window1.0M1.1M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$1.50$1.00
Output $/1M$7.50$6.00
Vision Tasks
CaptioningDemoDemo
Chart Question Answering
ClassificationDemoDemo
Document Question Answering
Image Tagging
Multi-Label Classification
Object DetectionDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Video Classification
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort
Overall
83.1%
72.4%
Avg cost / sample$0.0063$0.0044
Avg speed / sample4.73s7.15s
By task
Object Detection
56.0%
$0.0083
60.7%
$0.0070
Counting
82.4%
$0.0065
67.6%
$0.0030
Identification
96.9%
$0.0030
78.1%
$0.0020
OCR
88.4%
$0.0050
88.8%
$0.0065
Data Extraction
94.8%
$0.0030
79.4%
$0.0018
Reasoning (low)
80.1%
$0.0062
59.6%
$0.0025
Reasoning (high)
80.1%
$0.017
64.2%
$0.0033

Gemini 3.6 Flash vs GPT-5.6 Terra: Overview

Gemini 3.6 Flash

Gemini 3.6 Flash is a multimodal language model from Google DeepMind, positioned as the workhorse tier in the Gemini 3.x family. It accepts text, image, video, audio, and PDF inputs with a 1 million token context window and produces up to 64,000 output tokens. The model builds directly on Gemini 3.5 Flash, incorporating developer and customer feedback to improve token efficiency, coding quality, and knowledge work performance. According to the Artificial Analysis Index, it consumes 17% fewer output tokens than its predecessor, and on some benchmarks such as DeepSWE, token reduction reaches up to 65%. It supports function calling, structured output, search as a tool, and code execution, and includes computer use as a built-in capability in the Gemini API and Gemini Enterprise.

On coding benchmarks, Gemini 3.6 Flash scores 49% on DeepSWE versus 37% for 3.5 Flash, and 63.9% on MLE Bench versus 49.7%. Computer use performance on OSWorld-Verified improves from 78.4% to 83%, and knowledge work scores on GDPval-AA v2 rise from 1349 to 1421. The model carries a knowledge cutoff of March 2026 and ships with enhanced Frontier Safety safeguards covering chemical, biological, radiological, nuclear, and cyber offense domains, with training to minimize refusals for beneficial uses. It is a proprietary, closed-weights model available in preview through the Gemini API via Google AI Studio, Android Studio, Google Antigravity, Gemini Enterprise, and the Gemini app.

GPT-5.6 Terra

GPT-5.6 Terra is the mid-tier reasoning model in OpenAI's GPT-5.6 family, which also includes the flagship Sol and the lightweight Luna. Introduced in a limited preview on June 26, 2026, and made broadly available on July 9, 2026, Terra accepts text and image input and produces text output, supporting vision, function calling, tool use, and agentic workflows. It is designed as a balanced option for everyday professional and production workloads — including coding assistance, document analysis, customer support, and multi-step agent tasks — where both output quality and cost efficiency matter. OpenAI positions Terra as delivering performance competitive with GPT-5.5 at approximately half the price, with a context window of around 1,050,000 tokens. On Terminal-Bench 2.1, Terra scores 84.3%, matching Claude Fable 5 on that benchmark. Under OpenAI's Preparedness Framework, Terra is rated High for cybersecurity and biological capabilities, meaning it demonstrates meaningful capability in those domains without reaching the Critical threshold.

GPT-5.6 introduces a new naming convention in which the generation number (5.6) is paired with a durable capability tier name (Sol, Terra, or Luna), allowing each tier to advance on its own schedule. Terra carries the API identifier gpt-5.6-terra and supports the same reasoning effort controls available across the family, including adjustable reasoning depth. The model includes prompt caching with explicit cache breakpoints and a 30-minute minimum cache life, with cache writes billed at 1.25x the uncached input rate and cache reads receiving a 90% discount. GPT-5.6 Terra is a proprietary, closed-weights model served through the OpenAI API, Codex, and ChatGPT.

Frequently Asked Questions

On Roboflow's Vision Evals, Gemini 3.6 Flash performed better. It scores higher on 4 of the six vision tasks and averages 83.1% (#4 of 25) against 72.4% (#12 of 25) for GPT-5.6 Terra. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.

Yes. On the Vision Evals Reasoning benchmark at low effort, Gemini 3.6 Flash leads with 80.1% against 59.6%. This is the widest gap between the two models across the benchmark's tasks.

GPT-5.6 Terra is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0044 per sample against $0.0063. Gemini 3.6 Flash is priced at $1.50 per 1M input tokens and $7.50 per 1M output; GPT-5.6 Terra is priced at $1.00 per 1M input tokens and $6.00 per 1M output. Actual costs depend on your image sizes, prompts, and output length.

Gemini 3.6 Flash is faster. Across Roboflow's Vision Evals it averaged 4.7s per inference against 7.2s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.

Yes. The comparison demo on this page runs both models on the same image side by side for open prompts and image classification in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.