Gemini 2.5 Pro vs Gemma 4 31B
Compare Gemini 2.5 Pro and Gemma 4 31B side-by-side. See how these vision models stack up in Object Detection, Open Prompt, Classification, OCR, and Image Captioning.
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Gemini 2.5 Pro vs Gemma 4 31B: 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 31B is the largest dense model in Google's Gemma 4 family, built from the same research as Gemini 3 and released as open weights under the Apache 2.0 license. It supports a 256K token context window with text and image input, configurable thinking mode for step-by-step reasoning, and multilingual support across 140+ languages. The unquantized model fits on a single 80GB GPU.
For vision tasks, Gemma 4 31B supports image understanding with variable aspect ratios and resolutions, and can output structured bounding boxes for UI element detection, making it useful for document parsing and UI understanding. Compared to Gemma 3, it delivers stronger reasoning and multimodal performance. It is part of a four-size family alongside the 26B A4B MoE variant and two on-device models (E2B, E4B), with the 31B dense variant optimized for output quality and fine-tuning over inference speed.
Gemini 2.5 Pro vs Gemma 4 31B Comparison Table
| Property | Gemini 2.5 Pro | Gemma 4 31B |
|---|---|---|
| Organization | ||
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Jun 2025 | Apr 2026 |
| Context Window | 1.0M | 256K |
| Parameters | 31B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $1.25 | $0.120 |
| Output $/1M | $10.00 | $0.370 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Demo | Demo |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Multimodal Vision | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Visual Understanding | ||
| Overall Score | 70.15% | 67.16% |
| Avg Response Time | 11.87s | 34.59s |
| Median input tokensincl. image tokens | 294 | 294 |
| Median output tokens | 565 | 169 |
| Est. cost / taskon this benchmark | $0.0060 | $0.0001 |
| Defect Detection | 73.3%(11/15) | 80%(12/15) |
| Document Understanding | 88.9%(8/9) | 88.9%(8/9) |
| Object Counting | 20%(2/10) | 10%(1/10) |
| Object Understanding | 78.6%(11/14) | 71.4%(10/14) |
| Spatial Understanding | 78.9%(15/19) | 73.7%(14/19) |
| OCR | ||
| Overall Score | 78.6% | 84.72% |
| Avg Response Time | 4.91s | 11.82s |
| Median input tokensincl. image tokens | 290 | 290 |
| Median output tokens | 323 | 131 |
| Est. cost / taskon this benchmark | $0.0036 | $0.0001 |
| Focused Scene OCR | 78.8%(78/99) | 86.9%(86/99) |
| Handwritten Math | 80%(8/10) | 50%(5/10) |
| License Plate Recognition | 90%(27/30) | 93.3%(28/30) |
| Text Recognition | 73.3%(22/30) | 80%(24/30) |
| VQA & Extraction | 75%(45/60) | 85%(51/60) |
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