Claude Sonnet 4.5 vs Gemini 2.5 Pro
Compare Claude Sonnet 4.5 and Gemini 2.5 Pro side-by-side. See how these vision models stack up in Object Detection, Classification, Image Captioning, OCR, and Open Prompt.
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Claude Sonnet 4.5 vs Gemini 2.5 Pro: Overview
Claude Sonnet 4.5, released by Anthropic in September 2025, is the company’s most advanced Sonnet-series model, built for high-performance reasoning, coding, and long-horizon agentic workflows. It is a multimodal system that accepts both text and images, with a 200,000-token context window designed for handling large documents and extended interactions. Anthropic highlights its improvements in reliability, reduced sycophancy, and alignment, making it suitable for sustained enterprise use.
The model delivers strong results in coding and autonomous workflows, achieving 61.4% on the OSWorld benchmark and leading performance on SWE-bench Verified. It introduces infrastructure features such as a memory tool (beta), checkpointing for Claude Code, parallel tool use, and tighter integration with VS Code. Compared to Opus, which targets broader reasoning, Sonnet 4.5 is optimized for structured, long-duration tasks. Positioned against leading offerings from OpenAI and Google, it is aimed at enterprise automation, software engineering, and research-intensive applications.
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.
Claude Sonnet 4.5 vs Gemini 2.5 Pro Comparison Table
| Property | Claude Sonnet 4.5 | Gemini 2.5 Pro |
|---|---|---|
| Organization | Anthropic | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Sep 2025 | Jun 2025 |
| Context Window | 200K | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $3.00 | $1.25 |
| Output $/1M | $15.00 | $10.00 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Demo | Demo |
| 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 Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Visual Understanding | ||
| Overall Score | 59.7% | 70.15% |
| Avg Response Time | 5.67s | 11.87s |
| Median input tokensincl. image tokens | 2.2K | 294 |
| Median output tokens | 182 | 565 |
| Est. cost / taskon this benchmark | $0.0092 | $0.0060 |
| Defect Detection | 73.3%(11/15) | 73.3%(11/15) |
| Document Understanding | 77.8%(7/9) | 88.9%(8/9) |
| Object Counting | 10%(1/10) | 20%(2/10) |
| Object Understanding | 64.3%(9/14) | 78.6%(11/14) |
| Spatial Understanding | 63.2%(12/19) | 78.9%(15/19) |
| OCR | ||
| Overall Score | 67.25% | 78.6% |
| Avg Response Time | 3.93s | 4.91s |
| Median input tokensincl. image tokens | 735 | 290 |
| Median output tokens | 115 | 323 |
| Est. cost / taskon this benchmark | $0.0039 | $0.0036 |
| Focused Scene OCR | 71.7%(71/99) | 78.8%(78/99) |
| Handwritten Math | 20%(2/10) | 80%(8/10) |
| License Plate Recognition | 53.3%(16/30) | 90%(27/30) |
| Text Recognition | 66.7%(20/30) | 73.3%(22/30) |
| VQA & Extraction | 75%(45/60) | 75%(45/60) |
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