Roboflow

Gemini 3.7 Flash vs GPT-5.6 Terra

Compare Gemini 3.7 Flash and GPT-5.6 Terra side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, Classification, and OCR.

Compare Gemini 3.7 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.

Compare image classification labels and confidence scores side-by-side.

Open Classification in the full playground
GoogleGemini 3.7 Flash
Run to compare this model.
OpenAIGPT-5.6 Terra
Run to compare this model.

Models in this comparison

Gemini 3.7 Flash vs GPT-5.6 Terra on Vision Evals

Gemini 3.7 Flash scores higher on 5 of the six Vision Evals tasks.

The widest gap is Reasoning, where Gemini 3.7 Flash leads 82.8% to 59.6%.

Overall, Gemini 3.7 Flash averages 84.6% (#2 of 30) against 72.4% (#13 of 30) for GPT-5.6 Terra.

Gemini 3.7 Flash is cheaper ($0.0016 vs $0.0044 per sample), while GPT-5.6 Terra is faster (7.2s vs 10.0s per sample).

Gemini 3.7 FlashGPT-5.6 Terra

Gemini 3.7 Flash vs GPT-5.6 Terra Comparison Table

Evals updated August 14, 2026Pricing updated August 15, 2026

PropertyGemini 3.7 FlashGPT-5.6 Terra
OrganizationGoogleOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateAug 2026Jul 2026
Context Window1.0M1.1M
ParametersUndisclosed
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.375$1.00
Output $/1M$1.88$6.00
Vision Tasks
CaptioningDemoDemo
Chart Question Answering
ClassificationDemoDemo
Document Question Answering
Image Tagging
Multi-Label Classification
Object DetectionDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort
Overall
84.6%
72.4%
Avg cost / sample$0.0016$0.0044
Avg speed / sample9.97s7.15s
By task
Object Detection
69.4%
$0.0024
60.7%
$0.0070
Counting
77.0%
$0.0013
67.6%
$0.0030
Identification
96.9%
$0.0007
78.1%
$0.0020
OCR
86.9%
$0.0014
88.8%
$0.0065
Data Extraction
94.8%
$0.0007
79.4%
$0.0018
Reasoning (low)
82.8%
$0.0011
59.6%
$0.0025
Reasoning (high)
82.1%
$0.0026
64.2%
$0.0033

Gemini 3.7 Flash vs GPT-5.6 Terra: Overview

Gemini 3.7 Flash

Gemini 3.7 Flash is a proprietary multimodal model from Google, positioned in the Flash branch of the Gemini 3 series that trades some of the capacity of the larger Pro models for lower latency and lower cost per token. It accepts interleaved text and image input alongside other modalities handled by the Gemini family and returns text, and it continues the series pattern of exposing a configurable thinking budget so that reasoning effort can be scaled up for harder problems or reduced for high throughput extraction, routing and classification work. The model is announced roughly three weeks after Gemini 3.6 Flash, part of an unusually fast iteration cadence within the Flash line.

Google reports gains concentrated in agentic coding and front end generation, citing a WebDev Arena Elo of 1588 for this release compared with 1538 for the preceding Flash model, and describes it as producing more functional layouts and more feature complete applications in fewer prompts. Weights are not published and the architecture, parameter count and training corpus are undisclosed, consistent with prior Gemini releases. Visual capability follows the Flash lineage, covering image and document understanding, chart and diagram interpretation, text recognition in images, and general visual question answering.

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 3.7 Flash performed better. It scores higher on 5 of the six vision tasks and averages 84.6% (#2 of 30) against 72.4% (#13 of 30) 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 3.7 Flash leads with 82.8% against 59.6%. This is the widest gap between the two models across the benchmark's tasks.

Gemini 3.7 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0016 per sample against $0.0044. Gemini 3.7 Flash is priced at $0.38 per 1M input tokens and $1.88 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.

GPT-5.6 Terra is faster. Across Roboflow's Vision Evals it averaged 7.2s per inference against 10.0s. 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 image captioning and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.