GPT-6.1 Sol Ultrafast

• ⚡ Same GPT-6.1 Sol model served on a faster inference tier
• 🔧 Selected as a service-tier setting, not a separate checkpoint
• 📏 1.05M-token context: 922K input, 128K output
• 👁️ Accepts text and image inputs; reasoning effort from low to max
• 🧠 Reasoning, function calling, and web search capabilities
• 🆕 OpenAI advertises up to 8x faster generation than standard serving
• 🏢 Rolling out across the API, Codex, and ChatGPT Work
• 🎯 Aimed at latency-sensitive agents and interactive developer tooling

OpenAI

OpenAI is an American artificial intelligence research organization headquartered in San Francisco, structured as both a for-profit public benefit corporation and a nonprofit foundation. The lab developed the GPT family of large language models, the DALL-E…

GPT-6.1 Sol Ultrafast is not a distinct checkpoint but a faster serving mode applied to GPT-6.1 Sol: developers call the same model identifier and request the ultrafast service tier, and the request is scheduled on faster inference infrastructure. The tier affects generation speed rather than the model's weights, knowledge, or reasoning depth. OpenAI's announcement frames it as up to 8x faster, and describes the rollout as covering the API, Codex, and ChatGPT Work.

Relative to GPT-6.1 Sol, the intelligence profile is identical — the same 1.05M-token window split into 922K input and 128K output tokens, image input alongside text, function calling, web search, and reasoning effort selectable from low through max. The generational step therefore sits upstream in the model line itself: GPT-6.1 Sol succeeded GPT-6 Sol in the same Sol tier, and both sit alongside OpenAI's flagship GPT-6 Astra and the lighter GPT-6 Luna in the GPT-6 generation.

The Ultrafast mode for Sol became available on 8 October 2026. Because the underlying model is unchanged, migrating is a configuration change rather than a prompt-engineering exercise: existing tool definitions, image inputs, and long-context workflows carry over unmodified.

Practically, Ultrafast suits interactive agents, live coding assistants, and other flows where perceived latency matters more than throughput; batch or background jobs that tolerate queueing gain comparatively little from the tier.