Roboflow

Gemini 3 Flash vs GPT-5.6 Sol

Compare Gemini 3 Flash and GPT-5.6 Sol side-by-side. See how these vision models stack up in Object Detection, Classification, Open Prompt, OCR, and Image Captioning.

Compare Gemini 3 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.

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

Open Object Detection in the full playground
GoogleGemini 3 Flash
Run to compare this model.
OpenAIGPT-5.6 Sol
Run to compare this model.

Models in this comparison

Gemini 3 Flash vs GPT-5.6 Sol on Vision Evals

GPT-5.6 Sol scores higher on 4 of the six Vision Evals tasks.

The widest gap is Object Detection, where GPT-5.6 Sol leads 68.2% to 38.6%.

Overall, Gemini 3 Flash averages 74.9% (#11 of 31) against 76.9% (#10 of 31) for GPT-5.6 Sol.

Gemini 3 Flash is both cheaper ($0.0021 vs $0.0089 per sample) and faster (4.1s vs 11.7s per sample).

Gemini 3 FlashGPT-5.6 Sol

Gemini 3 Flash vs GPT-5.6 Sol Comparison Table

Evals updated August 20, 2026Pricing updated August 24, 2026

PropertyGemini 3 FlashGPT-5.6 Sol
OrganizationGoogleOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateDec 2025Jul 2026
Context Window1.0M1.5M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.500$2.00
Output $/1M$3.00$10.00
Vision Tasks
CaptioningDemoDemo
Chart Question Answering
ClassificationDemoDemo
Document Question Answering
Image Tagging
Multi-Label Classification
Object DetectionDemoDemo
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
74.9%
76.9%
Avg cost / sample$0.0021$0.0089
Avg speed / sample4.10s11.72s
By task
Object Detection
38.6%
$0.0031
68.2%
$0.016
Counting
67.6%
$0.0012
73.0%
$0.0048
Identification
93.8%
$0.0009
81.3%
$0.0027
OCR
87.6%
$0.0024
90.7%
$0.011
Data Extraction
96.9%
$0.0008
82.5%
$0.0033
Reasoning (low)
64.9%
$0.0020
65.6%
$0.0042
Reasoning (high)
74.2%
$0.0040
72.2%
$0.0057

Gemini 3 Flash vs GPT-5.6 Sol: Overview

Gemini 3 Flash

Gemini 3 Flash is a proprietary multimodal large language model developed by Google through Google DeepMind, designed to deliver fast, cost-efficient reasoning across real-time products and developer workflows. Released in December 2025, it is the Flash-tier variant of the Gemini 3 family, balancing low latency with reasoning quality approaching Pro models.

The model supports text, images, audio, and video, with an exceptionally large context window of roughly one million input tokens and outputs up to ~65k tokens. It emphasizes rapid responses for coding, summarization, analysis, and agentic tasks, and exposes configurable “thinking levels” via API to trade speed for deeper reasoning. Today, Gemini 3 Flash positions itself as a high-throughput, production-ready model, serving as the default in the Gemini app and Google Search’s AI Mode, optimized for scalable, interactive AI applications.

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, GPT-5.6 Sol performed better. It scores higher on 4 of the six vision tasks and averages 76.9% (#10 of 31) against 74.9% (#11 of 31) for Gemini 3 Flash. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.

No. On the Vision Evals Object Detection benchmark, GPT-5.6 Sol leads with 68.2% against 38.6%. This is the widest gap between the two models across the benchmark's tasks.

Gemini 3 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0021 per sample against $0.0089. Gemini 3 Flash is priced at $0.50 per 1M input tokens and $3.00 per 1M output; GPT-5.6 Sol is priced at $2.00 per 1M input tokens and $10.00 per 1M output. Actual costs depend on your image sizes, prompts, and output length.

Gemini 3 Flash is faster. Across Roboflow's Vision Evals it averaged 4.1s 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 object detection and image classification in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.