Roboflow

Gemini 3.7 Flash vs GPT-5.6 Sol

Compare Gemini 3.7 Flash and GPT-5.6 Sol 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 Sol 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 Sol
Run to compare this model.

Models in this comparison

Gemini 3.7 Flash vs GPT-5.6 Sol 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 65.6%.

Overall, Gemini 3.7 Flash averages 84.6% (#2 of 30) against 76.9% (#10 of 30) for GPT-5.6 Sol.

Gemini 3.7 Flash is both cheaper ($0.0016 vs $0.025 per sample) and faster (10.0s vs 11.7s per sample).

Gemini 3.7 FlashGPT-5.6 Sol

Gemini 3.7 Flash vs GPT-5.6 Sol Comparison Table

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

PropertyGemini 3.7 FlashGPT-5.6 Sol
OrganizationGoogleOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateAug 2026Jul 2026
Context Window1.0M1.5M
ParametersUndisclosed
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.375$5.00
Output $/1M$1.88$30.00
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%
76.9%
Avg cost / sample$0.0016$0.025
Avg speed / sample9.97s11.72s
By task
Object Detection
69.4%
$0.0024
68.2%
$0.045
Counting
77.0%
$0.0013
73.0%
$0.013
Identification
96.9%
$0.0007
81.3%
$0.0070
OCR
86.9%
$0.0014
90.7%
$0.032
Data Extraction
94.8%
$0.0007
82.5%
$0.0085
Reasoning (low)
82.8%
$0.0011
65.6%
$0.011
Reasoning (high)
82.1%
$0.0026
72.2%
$0.016

Gemini 3.7 Flash vs GPT-5.6 Sol: 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 Sol

GPT-5.6 Sol is the flagship model in OpenAI's GPT-5.6 family, which also includes Terra (a balanced everyday-work tier) and Luna (a fast, cost-efficient tier). Sol is designed for demanding reasoning, long-horizon agentic workflows, software engineering, computer use, scientific research, and cybersecurity tasks. It introduces two new capability modes: a "max" reasoning effort setting that allocates additional compute time for difficult problems, and an "ultra" mode that coordinates multiple subagents in parallel to accelerate complex, multi-step work. The model supports native multimodal input, allowing it to process screenshots, diagrams, charts, documents, and photographs alongside text. A reported context window of approximately 1.5 million tokens enables processing of large codebases, lengthy research documents, and extended agentic sessions.

GPT-5.6 Sol was announced on June 26, 2026, initially in a limited preview for trusted partners, and reached general availability on July 9, 2026. On the Agents' Last Exam benchmark, which evaluates long-running professional workflows across 55 fields, Sol scores 53.6. On Terminal-Bench 2.1, which tests command-line agentic coding workflows, Sol Ultra achieves 91.9%. The model also demonstrates gains in life sciences evaluations, including long-horizon genomics and quantitative biology analyses. OpenAI paired the release with its most extensive safety evaluation to date, combining human red teaming with large-scale automated testing, and classified Sol as High capability in both cybersecurity and biological risk under its Preparedness Framework, though it does not cross the Critical threshold in either category.

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 76.9% (#10 of 30) for GPT-5.6 Sol. 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 65.6%. 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 $0.025. Gemini 3.7 Flash is priced at $0.38 per 1M input tokens and $1.88 per 1M output; GPT-5.6 Sol is priced at $5.00 per 1M input tokens and $30.00 per 1M output. Actual costs depend on your image sizes, prompts, and output length.

Gemini 3.7 Flash is faster. Across Roboflow's Vision Evals it averaged 10.0s per inference against 11.7s. 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.