Claude Haiku 4.5 vs Claude Opus 4.6
Compare Claude Haiku 4.5 and Claude Opus 4.6 side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, OCR, Classification, and Object Detection.
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Claude Haiku 4.5 vs Claude Opus 4.6 : 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.
Claude Opus 4.6 is the flagship large language model from Anthropic, released on 2026-02-05 for advanced reasoning, complex coding, and enterprise agent workflows. It supports text and image inputs via API, offers a 200K-token standard context window with a 1M-token beta option, and enables outputs up to 128K tokens, with adaptive reasoning and context compaction for sustained tasks.
As of 2026-02-17, Anthropic also released Claude Sonnet 4.6, extending the 1M-token context window to a broader tier. Opus remains positioned for maximum depth and benchmark performance, while Sonnet 4.6 brings long-context capability to more cost- and latency-sensitive production use cases.
Claude Haiku 4.5 vs Claude Opus 4.6 Comparison Table
| Property | Claude Haiku 4.5 | Claude Opus 4.6 |
|---|---|---|
| Organization | Anthropic | Anthropic |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Oct 2025 | Feb 2026 |
| Context Window | 200K | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $1.00 | $5.00 |
| Output $/1M | $5.00 | $25.00 |
| 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 | 58.21% | 64.18% |
| Avg Response Time | 3.15s | 23.35s |
| Median input tokensincl. image tokens | 2.2K | 2.2K |
| Median output tokens | 174 | 130 |
| Est. cost / taskon this benchmark | $0.0030 | $0.014 |
| Defect Detection | 80%(12/15) | 73.3%(11/15) |
| Document Understanding | 77.8%(7/9) | 77.8%(7/9) |
| Object Counting | 0%(0/10) | 20%(2/10) |
| Object Understanding | 71.4%(10/14) | 71.4%(10/14) |
| Spatial Understanding | 52.6%(10/19) | 68.4%(13/19) |
| OCR | ||
| Overall Score | 61.57% | 82.53% |
| Avg Response Time | 2.13s | 5.05s |
| Median input tokensincl. image tokens | 735 | 736 |
| Median output tokens | 101 | 99 |
| Est. cost / taskon this benchmark | $0.0012 | $0.0062 |
| Focused Scene OCR | 61.6%(61/99) | 85.9%(85/99) |
| Handwritten Math | 20%(2/10) | 70%(7/10) |
| License Plate Recognition | 66.7%(20/30) | 90%(27/30) |
| Text Recognition | 63.3%(19/30) | 80%(24/30) |
| VQA & Extraction | 65%(39/60) | 76.7%(46/60) |
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