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Gemini 2.5 Pro vs Gemini 3.7 Flash

Compare Gemini 2.5 Pro and Gemini 3.7 Flash side-by-side. See how these vision models stack up in Open Prompt, Classification, OCR, and Image Captioning.

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GoogleGemini 2.5 Pro
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GoogleGemini 3.7 Flash
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Models in this comparison

Gemini 2.5 Pro vs Gemini 3.7 Flash on Vision Evals

Gemini 3.7 Flash scores higher on 5 of the six Vision Evals tasks.

The widest gap is Reasoning, where Gemini 3.7 Flash leads 82.8% to 42.4%.

Overall, Gemini 2.5 Pro averages 66.0% (#22 of 30) against 84.6% (#2 of 30) for Gemini 3.7 Flash.

Gemini 3.7 Flash is cheaper ($0.0016 vs $0.0050 per sample), while Gemini 2.5 Pro is faster (6.1s vs 10.0s per sample).

Gemini 2.5 ProGemini 3.7 Flash

Gemini 2.5 Pro vs Gemini 3.7 Flash Comparison Table

Evals updated August 14, 2026Pricing updated August 15, 2026

PropertyGemini 2.5 ProGemini 3.7 Flash
OrganizationGoogleGoogle
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateJun 2025Aug 2026
Context Window1.0M1.0M
ParametersUndisclosed
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$1.25$0.375
Output $/1M$10.00$1.88
Vision Tasks
CaptioningDemoDemo
Chart Question Answering
ClassificationDemoDemo
Document Question Answering
Image Tagging
Multi-Label Classification
Object DetectionDemo
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
66.0%
84.6%
Avg cost / sample$0.0050$0.0016
Avg speed / sample6.11s9.97s
By task
Object Detection
33.7%
$0.010
69.4%
$0.0024
Counting
52.7%
$0.0012
77.0%
$0.0013
Identification
93.8%
$0.0012
96.9%
$0.0007
OCR
88.8%
$0.0047
86.9%
$0.0014
Data Extraction
84.5%
$0.0013
94.8%
$0.0007
Reasoning (low)
42.4%
$0.0013
82.8%
$0.0011
Reasoning (high)
62.3%
$0.011
82.1%
$0.0026

Gemini 2.5 Pro vs Gemini 3.7 Flash: Overview

Gemini 2.5 Pro

Gemini 2.5 Pro, released on June 17, 2025, is Google DeepMind’s most capable model in the Gemini 2.5 family, optimized for deep reasoning, coding, and complex multimodal tasks. It accepts text, images, audio, video, and PDFs as input and outputs text. The model supports 1 million input tokens with an output capacity of up to 65K tokens, enabling large-scale comprehension of datasets, codebases, and technical documents. Its training knowledge extends to January 2025.

Pro outperforms earlier Gemini 2.0 models across benchmarks, including agentic coding tasks where it achieved ~63.8% on SWE-Bench Verified. It supports structured outputs, function calling, code execution, search grounding, and URL context, making it well-suited for enterprise, STEM, and developer workflows. However, it does not currently support image or audio generation in its stable release, and its higher computational cost and latency make it less efficient than Flash or Flash-Lite. It is available via the Gemini API, Google AI Studio, and Vertex AI.

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.

Frequently Asked Questions

On Roboflow's Vision Evals, Gemini 3.7 Flash performed better. It scores higher on 5 of the six vision tasks and averages 84.6% (#2 of 30) against 66.0% (#22 of 30) for Gemini 2.5 Pro. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.

No. On the Vision Evals Reasoning benchmark at low effort, Gemini 3.7 Flash leads with 82.8% against 42.4%. 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 $0.0016 per sample against $0.0050. Gemini 2.5 Pro is priced at $1.25 per 1M input tokens and $10.00 per 1M output; Gemini 3.7 Flash is priced at $0.38 per 1M input tokens and $1.88 per 1M output. Actual costs depend on your image sizes, prompts, and output length.

Gemini 2.5 Pro is faster. Across Roboflow's Vision Evals it averaged 6.1s per inference against 10.0s. 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 open prompts and image classification in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.