Roboflow

Gemini 3.5 Flash vs GPT-5.6 Terra

Compare Gemini 3.5 Flash and GPT-5.6 Terra side-by-side. See how these vision models stack up in Open Prompt, Image Captioning, OCR, Classification, and Object Detection.

Compare Gemini 3.5 Flash vs GPT-5.6 Terra live

Run the same image across every model that supports a task and compare their outputs side-by-side.

Detect and compare bounding boxes across models on the same image.

Open Object Detection in the full playground
GoogleGemini 3.5 Flash
Run to compare this model.
OpenAIGPT-5.6 Terra
Run to compare this model.

Models in this comparison

Gemini 3.5 Flash vs GPT-5.6 Terra: Overview

Gemini 3.5 Flash

Gemini 3.5 Flash is a multimodal language model developed by Google DeepMind and released at Google I/O 2026. It is built on the Gemini 3 Flash reasoning foundation and introduces configurable thinking levels (minimal, low, medium, and high) that allow developers to tune the depth of internal reasoning before a response is generated. The model accepts text, image, video, audio, and PDF inputs and produces text output, with a 1 million token context window and up to 65,000 output tokens per request. It is natively multimodal, processing visual inputs alongside text to support tasks such as image captioning, classification, optical character recognition, object detection, and visual grounding, where the model references specific regions within an image or video frame.

Its vision capabilities extend to interpreting UI screenshots, diagrams, charts, and real-world scenes, as well as understanding video and live frame sequences for activity and scene recognition. The model supports combined tool use, including Google Search, URL context, code execution, and custom functions, within a single request, and it uses reasoning context from previous turns when thought signatures are present in the conversation history, enabling persistent multi-turn reasoning chains. Gemini 3.5 Flash carries a knowledge cutoff of January 2026 and is available via the Gemini API, Google AI Studio, Google Antigravity, and the Gemini Enterprise Agent Platform.

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.

Gemini 3.5 Flash vs GPT-5.6 Terra Comparison Table

PropertyGemini 3.5 FlashGPT-5.6 Terra
OrganizationGoogleOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateMay 2026Jul 2026
Context Window1.0M1.1M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$1.50$2.50
Output $/1M$9.00$15.00
Vision Tasks
captioningDemoDemo
ClassificationDemoDemo
Document Question Answering
Object DetectionDemoDemo
OCRDemoDemo
Visual Question AnsweringDemoDemo
Chart Question Answering
Multi-Label Classification
Vision Language
Model Features
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
79.1%
Avg Response Time6.71s
Median input tokensincl. image tokens1.1K
Median output tokens294
Est. cost / taskon this benchmark$0.0043
Defect Detection
80%(12/15)
Document Understanding
77.8%(7/9)
Object Counting
60%(6/10)
Object Understanding
92.9%(13/14)
Spatial Understanding
78.9%(15/19)
OCR
Overall Score
90.39%
Avg Response Time4.86s
Median input tokensincl. image tokens1.1K
Median output tokens196
Est. cost / taskon this benchmark$0.0034
Focused Scene OCR
90.9%(90/99)
Handwritten Math
90%(9/10)
License Plate Recognition
100%(30/30)
Text Recognition
86.7%(26/30)
VQA & Extraction
86.7%(52/60)

Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology