Gemini 2.5 Pro vs Gemini 3.6 Flash
Compare Gemini 2.5 Pro and Gemini 3.6 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 3.6 Flash on Vision Evals
Gemini 3.6 Flash scores higher on 5 of the six Vision Evals tasks.
The widest gap is Reasoning, where Gemini 3.6 Flash leads 77.7% to 42.4%.
Overall, Gemini 2.5 Pro averages 66.0% (#34 of 52) against 83.0% (#6 of 52) for Gemini 3.6 Flash.
Gemini 3.6 Flash is cheaper ($0.0032 vs $0.0050 per sample), while Gemini 2.5 Pro is faster (6.1s vs 14.7s per sample).
Gemini 2.5 Pro vs Gemini 3.6 Flash Comparison Table
Evals updated September 3, 2026Pricing updated September 4, 2026
| Property | Gemini 2.5 Pro | Gemini 3.6 Flash |
|---|---|---|
| Organization | ||
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jun 2025 | Jul 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | ||
| 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 |
| Video Classification | ||
| 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% | 83.0% |
| Avg cost / sample | $0.0050 | $0.0032 |
| Avg speed / sample | 6.11s | 14.66s |
| By task | ||
| Object Detection (low) | 33.7% | 57.1% ±1.7, Mean of 3 runs, range 55.9 to 59.4 |
| Object Detection (high) | – | 70.7% ±0.4, Mean of 3 runs, range 70.3 to 71.2 |
| Counting (low) | 52.7% | 80.2% ±2.0, Mean of 3 runs, range 78.4 to 82.4 |
| Counting (high) | – | 79.3% ±2.7, Mean of 3 runs, range 77.0 to 82.4 |
| Identification (low) | 93.8% | 99.0% ±1.6, Mean of 3 runs, range 96.9 to 100.0 |
| Identification (high) | – | 100.0% ±0.0, Mean of 3 runs, range 100.0 to 100.0 |
| OCR (low) | 88.8% | 88.2% ±0.3, Mean of 3 runs, range 87.9 to 88.4 |
| OCR (high) | – | 89.5% ±0.0, Mean of 3 runs, range 89.5 to 89.6 |
| Data Extraction (low) | 84.5% | 95.9% ±1.0, Mean of 3 runs, range 94.8 to 96.9 |
| Data Extraction (high) | – | 94.8% ±1.0, Mean of 3 runs, range 93.8 to 95.9 |
| Reasoning (low) | 42.4% | 77.7% ±2.0, Mean of 3 runs, range 76.2 to 80.1 |
| Reasoning (high) | 62.3% | 81.0% ±2.0, Mean of 3 runs, range 79.5 to 83.4 |
Gemini 2.5 Pro vs Gemini 3.6 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 3.6 Flash is a multimodal language model from Google DeepMind, positioned as the workhorse tier in the Gemini 3.x family. It accepts text, image, video, audio, and PDF inputs with a 1 million token context window and produces up to 64,000 output tokens. The model builds directly on Gemini 3.5 Flash, incorporating developer and customer feedback to improve token efficiency, coding quality, and knowledge work performance. According to the Artificial Analysis Index, it consumes 17% fewer output tokens than its predecessor, and on some benchmarks such as DeepSWE, token reduction reaches up to 65%. It supports function calling, structured output, search as a tool, and code execution, and includes computer use as a built-in capability in the Gemini API and Gemini Enterprise.
On coding benchmarks, Gemini 3.6 Flash scores 49% on DeepSWE versus 37% for 3.5 Flash, and 63.9% on MLE Bench versus 49.7%. Computer use performance on OSWorld-Verified improves from 78.4% to 83%, and knowledge work scores on GDPval-AA v2 rise from 1349 to 1421. The model carries a knowledge cutoff of March 2026 and ships with enhanced Frontier Safety safeguards covering chemical, biological, radiological, nuclear, and cyber offense domains, with training to minimize refusals for beneficial uses. It is a proprietary, closed-weights model available in preview through the Gemini API via Google AI Studio, Android Studio, Google Antigravity, Gemini Enterprise, and the Gemini app.
Frequently Asked Questions
On Roboflow's Vision Evals, Gemini 3.6 Flash performed better. It scores higher on 5 of the six vision tasks and averages 83.0% (#6 of 52) against 66.0% (#34 of 52) 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 3.6 Flash leads with 77.7% against 42.4%. This is the widest gap between the two models across the benchmark's tasks.
Gemini 3.6 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0032 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 3.6 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 14.7s. 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.