Gemini 2.5 Flash vs Gemma 4 12B
Compare Gemini 2.5 Flash and Gemma 4 12B side-by-side.
Compare Gemini 2.5 Flash vs Gemma 4 12B live
Run the same image across every model that supports a task and compare their outputs side-by-side.
These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.
Models in this comparison
Gemini 2.5 Flash vs Gemma 4 12B: Overview
Gemini 2.5 Flash, released on June 17, 2025, is Google DeepMind’s production-ready, efficiency-focused model in the Gemini 2.5 family. It is multimodal, accepting text, images, video, and audio as inputs, with text as the primary output format. The model supports 1 million input tokens and up to 65K output tokens, enabling it to process very large contexts such as books, long video transcripts, or extensive datasets. Its training knowledge extends to January 2025.
Designed as a price-performance leader, Gemini 2.5 Flash balances speed and reasoning power, making it suitable for everyday enterprise and developer use cases without the higher latency and cost of Pro models. It supports advanced workflows like function calling, code execution, search grounding, URL context ingestion, and structured outputs. While efficient and scalable, output length is still limited compared to its input capacity, and multimodal outputs (e.g. image or audio generation) remain restricted to specialized or preview variants.
Gemma 4 12B is an open-weight multimodal model from Google in the Gemma 4 family. It is intended for text and image understanding tasks such as visual question answering, OCR, captioning, and document understanding, with a smaller parameter footprint than the larger Gemma 4 variants.
This entry is connected to Roboflow Playground vision evals for comparison. No runnable Playground workflow is configured yet, so the model page is used for discovery and benchmark context rather than direct hosted inference.
Gemini 2.5 Flash vs Gemma 4 12B Comparison Table
| Property | Gemini 2.5 Flash | Gemma 4 12B |
|---|---|---|
| Organization | ||
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Jul 2025 | Jun 2026 |
| Context Window | 1.0M | — |
| Parameters | 12B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.300 | |
| Output $/1M | $2.50 | |
| Vision Tasks | ||
| Captioning | Demo | |
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Classification | Demo | |
| Object Detection | Demo | |
| Model Features | ||
| Multimodal Vision | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Overall Score | 55.22% | 62.69% |
| Avg Response Time | 24.91s | 6.88s |
| Median input tokensincl. image tokens | 294 | |
| Median output tokens | 171 | |
| Est. cost / taskon this benchmark | $0.0005 | |
| Defect Detection | 60%(9/15) | 73.3%(11/15) |
| Document Understanding | 88.9%(8/9) | 88.9%(8/9) |
| Object Counting | 0%(0/10) | 10%(1/10) |
| Object Understanding | 71.4%(10/14) | 78.6%(11/14) |
| Spatial Understanding | 52.6%(10/19) | 57.9%(11/19) |
Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology