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Gemini 3.8 Flash vs GPT-5.6 Sol

Compare Gemini 3.8 Flash and GPT-5.6 Sol side-by-side. See how these vision models stack up in Object Detection, Image Captioning, OCR, Classification, and Open Prompt.

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GoogleGemini 3.8 Flash
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OpenAIGPT-5.6 Sol
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Models in this comparison

Gemini 3.8 Flash vs GPT-5.6 Sol on Vision Evals

Gemini 3.8 Flash scores higher on 4 of the five Vision Evals tasks.

The widest gap is Reasoning, where Gemini 3.8 Flash leads 81.2% to 66.0%.

Overall, Gemini 3.8 Flash averages 79.8% (#7 of 63) against 72.4% (#18 of 63) for GPT-5.6 Sol.

Gemini 3.8 Flash is cheaper ($0.0045 vs $0.019 per sample), while GPT-5.6 Sol is faster (12.2s vs 12.8s per sample).

Gemini 3.8 FlashGPT-5.6 Sol

Gemini 3.8 Flash vs GPT-5.6 Sol Comparison Table

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

PropertyGemini 3.8 FlashGPT-5.6 Sol
OrganizationGoogleOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateSep 2026Jul 2026
Context Window1.0M1.5M
ParametersUnknownUnknown
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$0.825$4.00
Output $/1M$4.13$20.00
Vision Tasks
CaptioningDemoDemo
Chart Question AnsweringSupportedSupported
ClassificationDemoDemo
Document Question AnsweringSupportedSupported
Image TaggingSupportedSupported
Multi-Label ClassificationSupportedSupported
Object DetectionDemoDemo
OCRDemoDemo
Vision LanguageSupportedSupported
Visual Question AnsweringDemoDemo
Model Features
Foundation VisionSupportedSupported
LLMs with Vision CapabilitiesSupportedSupported
Multimodal VisionSupportedSupported
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
79.8%
72.4%
Avg cost / sample$0.0045$0.019
Avg speed / sample12.76s12.16s
By task
Object Detection (low)
68.1%
±0.8, Mean of 3 runs, range 67.3 to 69.0
$0.0045
68.4%
±0.7, Mean of 3 runs, range 67.9 to 69.3
$0.031
Object Detection (high)
74.8%
±1.1, Mean of 3 runs, range 73.4 to 75.6
$0.023
68.4%
±0.8, Mean of 3 runs, range 67.7 to 69.3
$0.071
Counting (low)
78.8%
±2.7, Mean of 3 runs, range 75.7 to 81.1
$0.0040
74.3%
±1.4, Mean of 3 runs, range 73.0 to 75.7
$0.0098
Counting (high)
79.3%
±1.4, Mean of 3 runs, range 78.4 to 81.1
$0.027
76.1%
±2.0, Mean of 3 runs, range 74.3 to 78.4
$0.016
Identification (low)
97.9%
±1.6, Mean of 3 runs, range 96.9 to 100.0
$0.0016
89.6%
±4.7, Mean of 3 runs, range 84.4 to 93.8
$0.0056
Identification (high)
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.0037
92.7%
±1.6, Mean of 3 runs, range 90.6 to 93.8
$0.0060
OCR (low)
72.8%
$0.0049
63.6%
$0.019
by category
Single value
73.5%
Transcription
91.3%
Structured JSON
86.1%
Text localization
26.4%
Single value
43.5%
Transcription
91.7%
Structured JSON
82.0%
Text localization
52.2%
OCR (high)
78.7%
$0.039
65.3%
$0.048
by category
Single value
70.9%
Transcription
94.1%
Structured JSON
90.9%
Text localization
58.6%
Single value
46.1%
Transcription
92.3%
Structured JSON
83.9%
Text localization
51.6%
Reasoning (low)
81.2%
±0.3, Mean of 3 runs, range 80.8 to 81.5
$0.0038
66.0%
±2.6, Mean of 3 runs, range 63.6 to 68.9
$0.0086
Reasoning (high)
84.5%
±1.0, Mean of 3 runs, range 83.4 to 85.4
$0.023
71.7%
±1.3, Mean of 3 runs, range 70.2 to 72.8
$0.012

Gemini 3.8 Flash vs GPT-5.6 Sol: Overview

Gemini 3.8 Flash

Gemini 3.8 Flash is a natively multimodal reasoning model in Google's Gemini 3 series, positioned as the speed and cost oriented Flash tier while targeting long-horizon software engineering, autonomous agents, and enterprise workflows. It accepts text, images, video, audio, and PDF documents in a single request and returns text, with an input limit of 1,048,576 tokens and an output limit of 65,536 tokens. Thinking is configurable at low, medium, and high levels, and the model supports function calling, code execution, structured outputs, context caching, search and Maps grounding, file search, and computer use in preview. Image generation, audio generation, and the Live API are not supported.

On vision oriented evaluations the model reports 86.2% on CharXiv Reasoning for chart and figure synthesis and 87.8% on LVBench for long video understanding in agentic mode, alongside 90.8% on Terminal-Bench 2.1 and 61.6% on SWE-Bench Pro for coding. Following Gemini API conventions, it can localize objects by emitting bounding boxes as [ymin, xmin, ymax, xmax] integers normalized to a 0 to 1000 range, which supports prompt driven detection and grounding in addition to captioning, document parsing, and visual question answering. The knowledge cutoff is March 2026, though coverage in some domains reflects the January 2025 cutoff shared across the Gemini 3 family.

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 3.8 Flash performed better. It scores higher on 4 of the five vision tasks and averages 79.8% (#7 of 63) against 72.4% (#18 of 63) 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 3.8 Flash leads with 81.2% against 66.0%. This is the widest gap between the two models across the benchmark's tasks.

Gemini 3.8 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0045 per sample against $0.019. Gemini 3.8 Flash is priced at $0.83 per 1M input tokens and $4.13 per 1M output; GPT-5.6 Sol is priced at $4.00 per 1M input tokens and $20.00 per 1M output. Actual costs depend on your image sizes, prompts, and output length.

GPT-5.6 Sol is faster. Across Roboflow's Vision Evals it averaged 12.2s 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 captioning in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.