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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 Object Detection, 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 all five Vision Evals tasks.

The widest gap is Reasoning, where Gemini 3.7 Flash leads 80.8% to 42.4%.

Overall, Gemini 2.5 Pro averages 57.6% (#38 of 61) against 80.1% (#5 of 61) for Gemini 3.7 Flash.

Gemini 3.7 Flash is cheaper ($0.0033 vs $0.0051 per sample), while Gemini 2.5 Pro is faster (6.8s vs 11.0s per sample).

Gemini 2.5 ProGemini 3.7 Flash

Gemini 2.5 Pro vs Gemini 3.7 Flash Comparison Table

Evals updated October 8, 2026Pricing updated October 10, 2026

PropertyGemini 2.5 ProGemini 3.7 Flash
OrganizationGoogleGoogle
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateJun 2025Aug 2026
Context Window1.0M1.0M
ParametersUnknownUndisclosed
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$1.25$0.750
Output $/1M$10.00$3.75
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
57.6%
80.1%
Avg cost / sample$0.0051$0.0033
Avg speed / sample6.83s11.04s
By task
Object Detection (low)
33.7%
$0.010
70.5%
±1.1, Mean of 3 runs, range 69.4 to 71.5
$0.0047
Object Detection (high)–
74.3%
±0.8, Mean of 3 runs, range 73.3 to 75.0
$0.0089
Counting (low)
52.7%
$0.0012
78.4%
±1.4, Mean of 3 runs, range 77.0 to 79.7
$0.0025
Counting (high)–
79.3%
±2.0, Mean of 3 runs, range 77.0 to 81.1
$0.0056
Identification (low)
93.8%
$0.0012
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.0013
Identification (high)–
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.0021
OCR (low)
65.2%
$0.0046
73.9%
$0.0032
by category
Single value
60.9%
Transcription
86.8%
Structured JSON
76.3%
Text localization
35.9%
Single value
74.8%
Transcription
91.1%
Structured JSON
86.7%
Text localization
29.4%
OCR (high)
64.6%
$0.025
78.8%
$0.010
by category
Single value
63.0%
Transcription
87.3%
Structured JSON
78.3%
Text localization
20.6%
Single value
71.7%
Transcription
91.9%
Structured JSON
90.6%
Text localization
59.2%
Reasoning (low)
42.4%
$0.0013
80.8%
±2.0, Mean of 3 runs, range 78.8 to 82.8
$0.0022
Reasoning (high)
62.3%
$0.011
81.9%
±1.3, Mean of 3 runs, range 80.1 to 82.8
$0.0050

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 all five vision tasks and averages 80.1% (#5 of 61) against 57.6% (#38 of 61) 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 80.8% 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 $0.0033 per sample against $0.0051. 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 $0.75 per 1M input tokens and $3.75 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 6.8s per inference against 11.0s. 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 object detection and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.