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Gemini 3 Flash vs GPT-6 Sol

Compare Gemini 3 Flash and GPT-6 Sol side-by-side. See how these vision models stack up in Object Detection, Classification, Open Prompt, OCR, and Image Captioning.

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GoogleGemini 3 Flash
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OpenAIGPT-6 Sol
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Models in this comparison

OpenAI

Gemini 3 Flash vs GPT-6 Sol on Vision Evals

GPT-6 Sol scores higher on 4 of the five Vision Evals tasks.

The widest gap is Object Detection, where GPT-6 Sol leads 74.7% to 38.6%.

Overall, Gemini 3 Flash averages 65.7% (#27 of 63) against 76.9% (#12 of 63) for GPT-6 Sol.

Gemini 3 Flash is both cheaper ($0.0024 vs $0.0074 per sample) and faster (5.8s vs 10.2s per sample).

Gemini 3 FlashGPT-6 Sol

Gemini 3 Flash vs GPT-6 Sol Comparison Table

Evals updated October 13, 2026Pricing updated October 17, 2026

PropertyGemini 3 FlashGPT-6 Sol
OrganizationGoogleOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateDec 2025Sep 2026
Context Window1.0M1.1M
ParametersUnknownundisclosed
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$0.500$2.00
Output $/1M$3.00$10.00
Vision Tasks
CaptioningDemoDemo
Chart Question AnsweringSupportedSupported
ClassificationDemoDemo
Document Question AnsweringSupportedSupported
Image TaggingSupportedSupported
Multi-Label ClassificationSupportedSupported
Object DetectionDemoDemo
OCRDemoDemo
Vision LanguageSupportedSupported
Visual Question AnsweringDemoDemo
Promptable Concept SegmentationNot listedDemo
Model Features
Foundation VisionSupportedSupported
LLMs with Vision CapabilitiesSupportedSupported
Multimodal VisionSupportedSupported
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
65.7%
76.9%
Avg cost / sample$0.0024$0.0074
Avg speed / sample5.83s10.20s
By task
Object Detection (low)
38.6%
$0.0031
74.7%
±0.7, Mean of 3 runs, range 74.1 to 75.5
$0.011
Object Detection (high)–
76.4%
±1.2, Mean of 3 runs, range 75.0 to 77.4
$0.022
Counting (low)
67.6%
$0.0012
76.6%
±2.0, Mean of 3 runs, range 74.3 to 78.4
$0.0039
Counting (high)–
77.5%
±1.3, Mean of 3 runs, range 75.7 to 78.4
$0.0070
Identification (low)
93.8%
$0.0009
93.8%
±0.0, Mean of 3 runs, range 93.8 to 93.8
$0.0027
Identification (high)–
92.7%
±1.6, Mean of 3 runs, range 90.6 to 93.8
$0.0036
OCR (low)
63.9%
$0.0024
66.7%
$0.0077
by category
Single value
63.9%
Transcription
90.5%
Structured JSON
80.2%
Text localization
7.6%
Single value
45.6%
Transcription
88.3%
Structured JSON
83.4%
Text localization
65.7%
OCR (high)
73.1%
$0.013
68.8%
$0.018
by category
Single value
69.1%
Transcription
90.0%
Structured JSON
87.5%
Text localization
36.9%
Single value
47.4%
Transcription
91.0%
Structured JSON
85.3%
Text localization
68.7%
Reasoning (low)
64.9%
$0.0020
72.6%
±3.6, Mean of 3 runs, range 69.5 to 76.8
$0.0037
Reasoning (high)
74.2%
$0.0040
77.7%
±1.0, Mean of 3 runs, range 76.8 to 78.8
$0.0061

Gemini 3 Flash vs GPT-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-6 Sol

GPT-6 Sol is a proprietary multimodal reasoning model from OpenAI, released on September 22, 2026 alongside GPT-6 Luna as an efficiency-oriented tier of the GPT-6 family that began with GPT-6 Astra. OpenAI states that Sol and Luna are trained with methods similar to those used for Astra, carrying the same work on professional tasks, factuality, coding, computer use, and alignment into models that run faster. Sol accepts text and image input and returns text output, and OpenAI documents a context window of roughly one million tokens together with a knowledge cutoff of April 20, 2026.

The model targets complex coding and agentic workflows and exposes a configurable reasoning effort setting with levels of none, low, medium, high, xhigh, and max, which trades latency and token consumption against answer quality. OpenAI reports results including 33.2% on AutomationBench at xhigh effort and 56.4% on Agents' Last Exam at max effort, while its reported DeepSWE and OSWorld 2.0 figures of 68.8% and 64.4% fall below those of the earlier GPT-5.6 Sol. Its vision behavior covers image understanding tasks such as visual question answering, captioning, document and chart interpretation, and text recognition.

Frequently Asked Questions

On Roboflow's Vision Evals, GPT-6 Sol performed better. It scores higher on 4 of the five vision tasks and averages 76.9% (#12 of 63) against 65.7% (#27 of 63) 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 at low effort, GPT-6 Sol leads with 74.7% 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 $0.0024 per sample against $0.0074. Gemini 3 Flash is priced at $0.50 per 1M input tokens and $3.00 per 1M output; GPT-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 5.8s per inference against 10.2s. 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 object detection and image classification in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.