Claude Haiku 4.5 vs Gemini 3 Pro
Compare Claude Haiku 4.5 and Gemini 3 Pro side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, OCR, Classification, and Object Detection.
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Gemini 3 Pro is deprecated and can no longer be run. Details and evals are still available on its model page.
Models in this comparison
Claude Haiku 4.5 vs Gemini 3 Pro: Overview
Claude Haiku 4.5 is Anthropic’s lightweight model in the Claude 4.5 series, released in October 2025 under a proprietary license. Designed for speed and cost efficiency, it delivers near-frontier performance while maintaining Anthropic’s AI Safety Level 2 standard. Haiku 4.5 supports both text and multimodal (text and image) inputs, integrates tool use and extended reasoning, and features a 200,000 token context window, making it adept at handling long or complex workflows. Though the parameter count remains undisclosed, it achieves about 73.3% on SWE-bench Verified, reflecting strong coding and reasoning ability. Haiku 4.5 is ideal for developers and researchers seeking rapid, cost-effective model calls for analysis, coding, or multimodal understanding.
Gemini 3 Pro is Google DeepMind’s flagship multimodal frontier model, built for high-accuracy reasoning and large-scale context understanding across text, images, audio, video, code, and documents. It delivers major gains over Gemini 2.5 Pro, supported by a 1M-token window and strong performance on Google-reported benchmarks such as GPQA Diamond, MMMU-Pro, and Video-MMMU.
The model excels at structured outputs, tool use, and agentic coding, enabling complex multi-step workflows and analysis of entire books, codebases, or long videos in a single prompt. Positioned as Google’s top production model, it balances advanced reasoning with broad multimodal capabilities, making it well suited for research assistants, automation agents, coding systems, and enterprise-scale document and media analysis.
Claude Haiku 4.5 vs Gemini 3 Pro Comparison Table
| Property | Claude Haiku 4.5 | Gemini 3 Pro |
|---|---|---|
| Organization | Anthropic | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Oct 2025 | Nov 2025 |
| Context Window | 200K | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $1.00 | |
| Output $/1M | $5.00 | |
| Vision Tasks | ||
| Captioning | Demo | |
| Classification | Demo | |
| Object Detection | Demo | |
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | 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 | 58.21% | |
| Avg Response Time | 3.15s | |
| Median input tokensincl. image tokens | 2.2K | |
| Median output tokens | 174 | |
| Est. cost / taskon this benchmark | $0.0030 | |
| Defect Detection | 80%(12/15) | |
| Document Understanding | 77.8%(7/9) | |
| Object Counting | 0%(0/10) | |
| Object Understanding | 71.4%(10/14) | |
| Spatial Understanding | 52.6%(10/19) | |
| OCR | ||
| Overall Score | 61.57% | |
| Avg Response Time | 2.13s | |
| Median input tokensincl. image tokens | 735 | |
| Median output tokens | 101 | |
| Est. cost / taskon this benchmark | $0.0012 | |
| Focused Scene OCR | 61.6%(61/99) | |
| Handwritten Math | 20%(2/10) | |
| License Plate Recognition | 66.7%(20/30) | |
| Text Recognition | 63.3%(19/30) | |
| VQA & Extraction | 65%(39/60) | |
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