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Gemini 3.7 Flash vs GPT-5.6 Sol

Gemini 3.7 Flash and GPT-5.6 Sol score 80.1% vs 72.4% averaged across five Vision Evals tasks (#10 and #23 of 69 models), so Gemini 3.7 Flash leads overall.

  • Gemini 3.7 Flash wins all 5 tasks, with its widest lead on reasoning (80.8% vs 66.0%).
  • Gemini 3.7 Flash is about 1/6 of the cost: $0.0033 vs $0.021 overall average cost per sample.
  • Gemini 3.7 Flash is faster: 11.0s vs 12.2s per sample.

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GoogleGemini 3.7 Flash
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Gemini 3.7 Flash vs GPT-5.6 Sol on Vision Evals

Score by taskGemini 3.7 FlashGPT-5.6 Sol
Reasoning
80.8
66.0
OCR
73.9
63.6
Identification
96.9
89.6
Counting
78.4
74.3
Object Detection
70.5
68.4

Roboflow Vision Evals. Full breakdown below.

Gemini 3.7 Flash vs GPT-5.6 Sol Comparison Table

Evals updated November 12, 2026Pricing updated November 13, 2026

PropertyGemini 3.7 FlashGPT-5.6 Sol
OrganizationGoogleOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateAug 2026Jul 2026
Context Window1.0M1.5M
ParametersUndisclosedUnknown
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$0.750$4.40
Output $/1M$3.75$22.00
Vision Tasks
Both support 10 more tasks
Model Features
Both support 3 more features
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
80.1%
72.4%
Avg cost / sample$0.0033$0.021
Avg speed / sample11.04s12.16s
By task
Object Detection (low)
70.5%
±1.1, Mean of 3 runs, range 69.4 to 71.5
$0.0047
68.4%
±0.7, Mean of 3 runs, range 67.9 to 69.3
$0.034
Object Detection (high)
74.3%
±0.8, Mean of 3 runs, range 73.3 to 75.0
$0.0089
68.4%
±0.8, Mean of 3 runs, range 67.7 to 69.3
$0.078
Counting (low)
78.4%
±1.4, Mean of 3 runs, range 77.0 to 79.7
$0.0025
74.3%
±1.4, Mean of 3 runs, range 73.0 to 75.7
$0.011
Counting (high)
79.3%
±2.0, Mean of 3 runs, range 77.0 to 81.1
$0.0056
76.1%
±2.0, Mean of 3 runs, range 74.3 to 78.4
$0.017
Identification (low)
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.0013
89.6%
±4.7, Mean of 3 runs, range 84.4 to 93.8
$0.0061
Identification (high)
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.0021
92.7%
±1.6, Mean of 3 runs, range 90.6 to 93.8
$0.0066
OCR (low)
73.9%
$0.0032
63.6%
$0.021
by category
Single value
74.8%
Transcription
91.1%
Structured JSON
86.7%
Text localization
29.4%
Single value
43.5%
Transcription
91.7%
Structured JSON
82.0%
Text localization
52.2%
OCR (high)
78.8%
$0.010
65.3%
$0.053
by category
Single value
71.7%
Transcription
91.9%
Structured JSON
90.6%
Text localization
59.2%
Single value
46.1%
Transcription
92.3%
Structured JSON
83.9%
Text localization
51.6%
Reasoning (low)
80.8%
±2.0, Mean of 3 runs, range 78.8 to 82.8
$0.0022
66.0%
±2.6, Mean of 3 runs, range 63.6 to 68.9
$0.0095
Reasoning (high)
81.9%
±1.3, Mean of 3 runs, range 80.1 to 82.8
$0.0050
71.7%
±1.3, Mean of 3 runs, range 70.2 to 72.8
$0.013

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. 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 all five vision tasks and averages 80.1% (#10 of 69) against 72.4% (#23 of 69) 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 80.8% against 66.0%. 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 $0.0033 per sample against $0.021. Gemini 3.7 Flash is priced at $0.75 per 1M input tokens and $3.75 per 1M output; GPT-5.6 Sol is priced at $4.40 per 1M input tokens and $22.00 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 11.0s per inference against 12.2s. 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 image captioning and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.