Opus 5 Was Just Released And Its …
Summary
Anthropic has released Claude Opus 5, significantly reducing its pricing to \$5 per million input tokens and \$25 per million output tokens, a cut from Claude Fable 5's \$10/\$50 rates. Artificial Analysis places Opus 5 at 61 on its Intelligence Index, just one point above Fable 5 (60) and two points above GPT-5.6 Sol (59). Despite Opus 5's strong performance, GPT-5.6 Sol offers a much lower cost per Intelligence Index task at \$1.04, compared to Opus 5's \$2.03 and Fable 5's \$2.75. This highlights that model selection should focus on delivering acceptable work for the actual bill, rather than solely on peak performance claims.
Key takeaway
For AI Engineers or small teams evaluating large language models for coding or agent features, you should prioritize cost-per-task efficiency over marginal performance gains. While Claude Opus 5 shows strong performance, your experimentation budget will benefit significantly by routing work to models like GPT-5.6 Sol, which offers comparable intelligence at nearly half the cost per task. Focus on the model, effort setting, and coding surface that delivers acceptable results within your budget.
Key insights
Raw performance scores do not automatically translate to cost-effective AI model solutions.
Principles
- Prioritize cost-effectiveness for acceptable work over peak performance claims.
- Small intelligence index differences can hide large cost-per-task variations.
In practice
- Evaluate models by cost per task for acceptable output.
- Consider routing work to cost-efficient models like GPT-5.6 Sol.
Topics
- Claude Opus 5
- GPT-5.6 Sol
- LLM Cost-Effectiveness
- Model Selection
- Inference Costs
- Artificial Analysis Intelligence Index
Best for: CTO, VP of Engineering/Data, AI Architect, AI Engineer, Machine Learning Engineer, Director of AI/ML
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Editorial summary, takeaway, and curation by AIssential. Original article published by Data Science on Medium.