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Gemini 3 Flash vs GPT-6.1 Sol

Compare Gemini 3 Flash and GPT-6.1 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-6.1 Sol
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Models in this comparison

Gemini 3 Flash vs GPT-6.1 Sol on Vision Evals

GPT-6.1 Sol scores higher on 4 of the five Vision Evals tasks.

The widest gap is Object Detection, where GPT-6.1 Sol leads 80.8% to 38.6%.

Overall, Gemini 3 Flash averages 65.7% (#27 of 63) against 81.7% (#5 of 63) for GPT-6.1 Sol.

Gemini 3 Flash is both cheaper ($0.0024 vs $0.0068 per sample) and faster (5.8s vs 12.8s per sample).

Gemini 3 FlashGPT-6.1 Sol

Gemini 3 Flash vs GPT-6.1 Sol Comparison Table

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

PropertyGemini 3 FlashGPT-6.1 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%
81.7%
Avg cost / sample$0.0024$0.0068
Avg speed / sample5.83s12.82s
By task
Object Detection (low)
38.6%
$0.0031
80.8%
±0.1, Mean of 3 runs, range 80.7 to 80.9
$0.010
Object Detection (high)–
81.6%
±0.4, Mean of 3 runs, range 81.1 to 82.0
$0.022
Counting (low)
67.6%
$0.0012
78.8%
±3.4, Mean of 3 runs, range 75.7 to 82.4
$0.0037
Counting (high)–
80.2%
±3.4, Mean of 3 runs, range 77.0 to 83.8
$0.0057
Identification (low)
93.8%
$0.0009
89.6%
±1.6, Mean of 3 runs, range 87.5 to 90.6
$0.0025
Identification (high)–
88.5%
±1.6, Mean of 3 runs, range 87.5 to 90.6
$0.0030
OCR (low)
63.9%
$0.0024
75.8%
$0.0069
by category
Single value
63.9%
Transcription
90.5%
Structured JSON
80.2%
Text localization
7.6%
Single value
58.3%
Transcription
95.7%
Structured JSON
89.5%
Text localization
74.4%
OCR (high)
73.1%
$0.013
75.8%
$0.016
by category
Single value
69.1%
Transcription
90.0%
Structured JSON
87.5%
Text localization
36.9%
Single value
56.5%
Transcription
96.0%
Structured JSON
90.4%
Text localization
76.7%
Reasoning (low)
64.9%
$0.0020
83.7%
±1.3, Mean of 3 runs, range 82.1 to 84.8
$0.0033
Reasoning (high)
74.2%
$0.0040
88.7%
±2.0, Mean of 3 runs, range 87.4 to 91.4
$0.0042

Gemini 3 Flash vs GPT-6.1 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-6.1 Sol

GPT-6.1 Sol is a reasoning model in OpenAI's GPT-6 series that accepts text and image input and returns text. It is an upgrade to GPT-6 Sol positioned to approach the intelligence of the larger GPT-6 Astra model on agentic coding, computer use, and professional knowledge work. The model exposes an adjustable reasoning effort control, ranging from low settings for simple turns to maximum settings for harder tasks, and can be driven with tool use enabled or disabled. It operates over a context window of roughly one million tokens and emits up to 128,000 output tokens in a single response, which supports long-running agent loops over large codebases and multi-document collections. Audio and video inputs are not supported.

On the visual side, the model is evaluated on GDP.pdf, a benchmark that asks professional questions about complex PDF documents containing tables, charts, diagrams, and fine-print details, and on OSWorld 2.0, which measures agents operating graphical computer applications. OpenAI reports that GPT-6.1 Sol performs on par with or better than GPT-6 Sol across its image input safety evaluations, and that the share of responses containing a factual error at low reasoning effort falls from 11.4 percent to 7.7 percent.

Frequently Asked Questions

On Roboflow's Vision Evals, GPT-6.1 Sol performed better. It scores higher on 4 of the five vision tasks and averages 81.7% (#5 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-6.1 Sol leads with 80.8% 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 $0.0068. Gemini 3 Flash is priced at $0.50 per 1M input tokens and $3.00 per 1M output; GPT-6.1 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 12.8s. 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.