Opus 4.8: https://platform.claude.com/docs/en/about-claude/models/whats-new-claude-4-8
Verdent: https://www.verdent.ai/?id=700712

In this video, I'll be telling you about Claude Opus 4.8, Anthropic’s latest Opus model update, and why it performed much better than expected in my benchmark, especially for coding, agentic workflows, frontend tasks, 3D tasks, math, and local fine-tuning workflows.

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Key Takeaways:

🚀 Claude Opus 4.8 is a modest update on paper, but it showed a massive jump in practical testing.  
📊 Opus 4.8 scored 61 out of 70 in my benchmark, reaching 87.14 percent overall.  
💻 The model performed extremely well on interactive coding tasks like the elevator simulation and bow-and-arrow game.  
🧠 Opus 4.8 correctly solved the math problem that every other tested model failed.  
🛠️ Anthropic added effort control, making it easier to choose how much reasoning the model should use.  
⚡ Fast mode is now available for Opus 4.8, offering faster responses with improved pricing compared to previous fast modes.  
🤖 The model is especially strong for long-horizon agentic work, big refactors, and complex coding workflows.  
🎨 Opus 4.8 has strong frontend and design abilities, but users may need to specify the visual style clearly.  
✅ Anthropic says Opus 4.8 is more honest about flaws in its own code, which is very useful for coding tasks.  
👍 Overall, Claude Opus 4.8 is one of the strongest coding and agentic models I have tested so far.

AICodeKing

Claude Opus 4.8 is Anthropic's latest incremental update to the Opus model line, priced the same as Opus 4.7 at $5/M input and $25/M output tokens. Key additions include a faster and cheaper fast mode (2.5x speed, 3x cheaper than before), simplified effort control (low/medium/high/x-high/max instead of manual token budgets), and dynamic workflows in Claude Code for parallel sub-agent orchestration on large codebases. Anthropic claims Opus 4.8 is 4x less likely to silently pass flawed code. In a personal 7-task benchmark covering frontend, 3D rendering, SVG generation, game building, math, and local fine-tuning workflows, Opus 4.8 scored 87.14% (61/70) — a massive jump from Opus 4.7's 55.71% (39/70) and well ahead of GPT-5.5, Gemini 3.5 Flash, Deepseek, and Mimo. The standout result was a combinatorics math problem that every other tested model got wrong. The model is recommended for complex coding, agentic tasks, and large refactors, but may be overkill for simple chat or small edits.

Opus 4.8 (Fully Tested): Is IT ACTUALLY GOOD?