OpenAI's GPT-5.6 Sol (max) Scores 59 on Artificial Analysis Intelligence Index
Key Takeaways
- ▸GPT-5.6 Sol (max) scores 59/100 on Artificial Analysis Intelligence Index—nearly double the 30-model average—demonstrating elite-tier reasoning and knowledge capabilities
- ▸The model supports reasoning, multimodal input (text + images), and excels at agentic tasks including real-world work, tool use, coding, legal analysis, and SaaS workflows
- ▸Premium pricing of $5/1M input and $30/1M output tokens positions it as an expensive option compared to peer models (averaging $1.71 input, $8.70 output)
Summary
OpenAI's GPT-5.6 Sol (max) has achieved a score of 59 on the Artificial Analysis Intelligence Index, placing it among the leading proprietary models and well above the average of 30. The reasoning model supports multimodal input (text and image), outputs text, and features an expansive 1M token context window—equivalent to roughly 1500 pages of standard text. The model demonstrates particular strength in agentic tasks, including real-world work (GDPval-AA v2), tool use, coding and terminal operations, legal workflows, and SaaS automation.
Pricing for GPT-5.6 Sol (max) reflects its advanced capabilities: $5.00 per 1M input tokens and $30.00 per 1M output tokens, placing it in the expensive category relative to comparable models. The evaluation of the model's intelligence capabilities generated 70M output tokens and cost $2,824.18. While the model's intelligence performance ranks among the highest tested, the pricing positions it as a premium offering, requiring careful cost-benefit analysis for production deployments.
- 1M token context window provides significant capacity for complex, long-form reasoning tasks and multi-document analysis
Editorial Opinion
GPT-5.6 Sol (max) represents OpenAI's commitment to pushing the frontiers of model intelligence, particularly for complex agentic and reasoning-heavy workloads. The benchmark results confirm exceptional capability across reasoning, scientific analysis, and knowledge-intensive tasks—making it an attractive choice for enterprises that can absorb premium pricing. However, the significant cost premium relative to alternatives raises important questions about ROI for typical use cases; organizations will need to carefully weigh the intelligence gains against budget constraints. The model's strong performance on agentic benchmarks suggests it's specifically optimized for autonomous reasoning workflows rather than general-purpose chat.
