OpenAI's GPT-5.6 Sol launches to rival Claude Mythos under government access rules it calls unsustainable

· Source: The Decoder · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Intermediate, short

Summary

OpenAI has launched its new GPT-5.6 generation, featuring the flagship Sol, alongside cheaper Terra and Luna tiers. GPT-5.6 Sol claims to match or surpass Anthropic's Claude Mythos 5 across various benchmarks, notably leading in agentic coding with an 88.8 percent score on Terminal-Bench 2.1 (Sol Ultra reaching 91.9 percent) compared to Mythos 5's 88 percent. Sol also demonstrates improved token efficiency in cybersecurity on ExploitBench, matching Mythos Preview's performance using roughly a third of output tokens. Despite these advancements, access to GPT-5.6 Sol is currently restricted to select partners by the US government, a policy OpenAI publicly criticizes as detrimental to developers and businesses. The new models introduce a tiered pricing structure, with Sol costing \$5 input and \$30 output per million tokens, and are slated for a July launch on Cerebras, offering up to 750 tokens per second.

Key takeaway

For Directors of AI/ML evaluating next-generation models, OpenAI's GPT-5.6 Sol presents a compelling option, outperforming Claude Mythos 5 in agentic coding and demonstrating superior token efficiency in cybersecurity. While its \$5 input/\$30 output pricing per million tokens might seem high, its efficiency could lower your effective task costs. However, current US government restrictions mean you cannot widely deploy Sol yet. Monitor policy developments closely to capitalize on its capabilities once broader access becomes available.

Key insights

OpenAI's GPT-5.6 Sol rivals Claude Mythos in performance, but government restrictions limit its immediate availability.

Principles

Method

GPT-5.6 offers "max" mode for deeper reasoning and "ultra" mode, which dispatches complex tasks to parallel sub-agents for enhanced performance.

In practice

Topics

Best for: AI Engineer, Machine Learning Engineer, Investor, Tech Journalist, AI Scientist, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by The Decoder.