Claude Opus 4.8 vs Claude Sonnet 5
Compare Claude Opus 4.8 and Claude Sonnet 5 side-by-side. See how these vision models stack up in Image Captioning, Classification, OCR, Object Detection, and Open Prompt.
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Claude Opus 4.8 vs Claude Sonnet 5: Overview
Claude Opus 4.8 is Anthropic's most capable generally available large language model, released on May 28, 2026 as an incremental upgrade to Claude Opus 4.7. The model accepts text and image inputs and produces text outputs, with a 1 million token context window on the Claude API, Amazon Bedrock, and Google Cloud Vertex AI (200k tokens on Microsoft Foundry) and up to 128k max output tokens. It uses adaptive thinking and supports adjustable effort tiers — high by default, with extra and max tiers available for more demanding tasks. A fast mode operates at approximately 2.5x standard speed. The model is described by Anthropic as a hybrid reasoning model designed for advanced coding, agentic workflows, long-context reasoning, and professional knowledge work.
Key behavioral improvements over Opus 4.7 include substantially reduced rates of unreported code flaws, improved honesty in self-assessment, and better tool-calling reliability. On Anthropic's Super-Agent benchmark, Opus 4.8 completes every case end-to-end, and it scores 84% on Online-Mind2Web for computer-use and browser-agent tasks. It achieves 88.6% on SWE-bench Verified and 69.2% on SWE-bench Pro. Alongside the model, Anthropic launched Dynamic Workflows in Claude Code (research preview), which enables Claude to orchestrate hundreds of parallel subagents for codebase-scale tasks such as large migrations. The Messages API was also updated to accept mid-task system messages without breaking prompt caching, improving support for long-running agentic pipelines.
Claude Sonnet 5 is a mid-tier large language model from Anthropic, released on June 30, 2026, as the latest model in the Sonnet series and a direct successor to Claude Sonnet 4.6. It is a hybrid reasoning model designed primarily for agentic workflows, software coding, and professional tasks. The model features a 1 million token context window, a 128k maximum output token limit, and runs adaptive thinking by default, giving API users fine-grained control over reasoning effort across five levels (low, medium, high, max, and extra-high). It uses an updated tokenizer shared with Opus 4.7 and later models, which produces approximately 30% more tokens for equivalent text compared to earlier Claude models. On benchmarks, Sonnet 5 scores 63.2% on agentic coding and 81.2% on OSWorld, narrowing the gap with Opus 4.8 while remaining at Sonnet-tier pricing.
The model supports text and image input with text output, and accepts tools including browsers and terminals for autonomous multi-step task execution. Anthropic's safety evaluations report that Sonnet 5 shows a lower rate of undesirable behaviors than Sonnet 4.6 and is generally safer in agentic contexts, with improved resistance to prompt injection and reduced sycophancy. Cybersecurity safeguards equivalent to those on Opus 4.7 and 4.8 are active, though Anthropic notes the model was not deliberately trained on cybersecurity tasks. The model is proprietary and API-only, with no open weights.
Claude Opus 4.8 vs Claude Sonnet 5 Comparison Table
| Property | Claude Opus 4.8 | Claude Sonnet 5 |
|---|---|---|
| Organization | Anthropic | Anthropic |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | May 2026 | Jun 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | $2.00 |
| Output $/1M | $25.00 | $10.00 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Demo | Demo |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Document Question Answering | ||
| Multi-Label Classification | ||
| Model Features | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
| Foundation Vision | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Visual Understanding | ||
| Overall Score | 67.16% | 70.15% |
| Avg Response Time | 4.36s | 3.90s |
| Median input tokensincl. image tokens | 2.0K | 2.1K |
| Median output tokens | 92 | 61 |
| Est. cost / taskon this benchmark | $0.012 | $0.0048 |
| Defect Detection | 66.7%(10/15) | 73.3%(11/15) |
| Document Understanding | 77.8%(7/9) | 66.7%(6/9) |
| Object Counting | 30%(3/10) | 20%(2/10) |
| Object Understanding | 85.7%(12/14) | 92.9%(13/14) |
| Spatial Understanding | 68.4%(13/19) | 78.9%(15/19) |
| OCR | ||
| Overall Score | 87.34% | 83.84% |
| Avg Response Time | 3.99s | 2.77s |
| Median input tokensincl. image tokens | 578 | 642 |
| Median output tokens | 81 | 64 |
| Est. cost / taskon this benchmark | $0.0049 | $0.0019 |
| Focused Scene OCR | 91.9%(91/99) | 88.9%(88/99) |
| Handwritten Math | 60%(6/10) | 50%(5/10) |
| License Plate Recognition | 90%(27/30) | 90%(27/30) |
| Text Recognition | 86.7%(26/30) | 80%(24/30) |
| VQA & Extraction | 83.3%(50/60) | 80%(48/60) |
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