Gemini 2.5 Pro vs Gemma 4 26B A4B
Compare Gemini 2.5 Pro and Gemma 4 26B A4B side-by-side. See how these vision models stack up in Object Detection, Open Prompt, Classification, OCR, and Image Captioning.
Compare Gemini 2.5 Pro vs Gemma 4 26B A4B live
Run the same image across every model that supports a task and compare their outputs side-by-side.
Detect and compare bounding boxes across models on the same image.
Upload an image
Drag and drop an image here, or click to browse
Models in this comparison
Gemini 2.5 Pro vs Gemma 4 26B A4B on Vision Evals
Gemini 2.5 Pro scores higher on 4 of the six Vision Evals tasks.
The widest gap is Identification, where Gemini 2.5 Pro leads 93.8% to 81.3%.
Overall, Gemini 2.5 Pro averages 66.0% (#34 of 53) against 63.6% (#42 of 53) for Gemma 4 26B A4B.
Gemma 4 26B A4B is cheaper ($0.0019 vs $0.0050 per sample), while Gemini 2.5 Pro is faster (6.1s vs 27.8s per sample).
Gemini 2.5 Pro vs Gemma 4 26B A4B Comparison Table
Evals updated September 5, 2026Pricing updated September 21, 2026
| Property | Gemini 2.5 Pro | Gemma 4 26B A4B |
|---|---|---|
| Organization | ||
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Jun 2025 | Apr 2026 |
| Context Window | 1.0M | 256K |
| Parameters | 25.2B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $1.25 | $0.090 |
| Output $/1M | $10.00 | $0.300 |
| 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% | 63.6% |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0050 | $0.0019 |
| Avg speed / sample | 6.11s | 27.84s |
| By task | ||
| Object Detection | 33.7% | 44.2% ±0.7, Mean of 3 runs, range 43.5 to 44.8 |
| Counting | 52.7% | 43.2% ±2.0, Mean of 3 runs, range 41.9 to 46.0 |
| Identification | 93.8% | 81.3% ±3.1, Mean of 3 runs, range 78.1 to 84.4 |
| OCR | 88.8% | 88.7% ±1.3, Mean of 3 runs, range 87.6 to 90.2 |
| Data Extraction | 84.5% | 76.6% ±0.5, Mean of 3 runs, range 76.3 to 77.3 |
| Reasoning (low) | 42.4% | 47.7% ±2.0, Mean of 3 runs, range 45.0 to 49.0 |
| Reasoning (high) | 62.3% | – |
Gemini 2.5 Pro vs Gemma 4 26B A4B: 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.
Gemma 4 26B A4B is the Mixture-of-Experts variant in Google's Gemma 4 family, with 25.2B total parameters but only 3.8B active per token. Built from the same Gemini 3 research as the 31B dense sibling and released as open weights under the Apache 2.0 license, it supports a 256K token context window with text and image input and configurable thinking mode. The "A4B" in the name refers to its approximately 4B active parameters. The MoE design makes it significantly faster at inference than the dense 31B, running nearly as fast as a 4B-parameter model while delivering roughly 97% of the dense model's quality.
For vision tasks, the 26B A4B shares the same multimodal capabilities as the 31B image understanding with variable aspect ratios and resolutions, and structured bounding box output for UI element detection. The tradeoff versus the 31B dense model is a small quality reduction in exchange for much faster inference and lower hardware requirements, fitting in 18GB of VRAM at 4-bit quantization. It ranked #6 among open models on the Arena AI text leaderboard at launch.
Frequently Asked Questions
On Roboflow's Vision Evals, Gemini 2.5 Pro performed better. It scores higher on 4 of the six vision tasks and averages 66.0% (#34 of 53) against 63.6% (#42 of 53) for Gemma 4 26B A4B. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Identification benchmark at low effort, Gemini 2.5 Pro leads with 93.8% against 81.3%. This is the widest gap between the two models across the benchmark's tasks.
Gemma 4 26B A4B is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0019 per sample against $0.0050. 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 27.8s. 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.