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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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 80.8% to 42.4%.
Overall, Gemini 2.5 Pro averages 66.0% (#40 of 61) against 85.2% (#6 of 61) for Gemini 3.7 Flash.
Gemini 3.7 Flash is cheaper ($0.0031 vs $0.0050 per sample), while Gemini 2.5 Pro is faster (6.1s vs 16.5s per sample).
Gemini 2.5 Pro vs Gemini 3.7 Flash Comparison Table
Evals updated September 29, 2026Pricing updated September 29, 2026
| Property | Gemini 2.5 Pro | Gemini 3.7 Flash |
|---|---|---|
| Organization | ||
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jun 2025 | Aug 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | Undisclosed | |
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $1.25 | $0.750 |
| Output $/1M | $10.00 | $3.75 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | Demo |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| 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% | 85.2% |
| Avg cost / sample | $0.0050 | $0.0031 |
| Avg speed / sample | 6.11s | 16.50s |
| By task | ||
| Object Detection (low) | 33.9% | 71.0% ±0.9, Mean of 3 runs, range 69.8 to 71.6 |
| Object Detection (high) | – | 74.4% ±0.7, Mean of 3 runs, range 73.6 to 75.0 |
| Counting (low) | 52.7% | 78.4% ±1.4, Mean of 3 runs, range 77.0 to 79.7 |
| Counting (high) | – | 79.3% ±2.0, Mean of 3 runs, range 77.0 to 81.1 |
| Identification (low) | 93.8% | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 |
| Identification (high) | – | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 |
| OCR (low) | 88.8% | 88.2% ±1.6, Mean of 3 runs, range 86.9 to 90.0 |
| OCR (high) | – | 89.0% ±0.8, Mean of 3 runs, range 88.3 to 89.9 |
| Data Extraction (low) | 84.5% | 96.2% ±0.5, Mean of 3 runs, range 95.9 to 96.9 |
| Data Extraction (high) | – | 95.9% ±0.0, Mean of 3 runs, range 95.9 to 95.9 |
| Reasoning (low) | 42.4% | 80.8% ±2.0, Mean of 3 runs, range 78.8 to 82.8 |
| Reasoning (high) | 62.3% | 81.9% ±1.3, Mean of 3 runs, range 80.1 to 82.8 |
Gemini 2.5 Pro vs Gemini 3.7 Flash: Overview
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 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 85.2% (#6 of 61) against 66.0% (#40 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 $0.0031 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 $0.75 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 6.1s per inference against 16.5s. 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.