OpenAI's GPT-5.6 Sol Sets New Record: Sub-100ms Response Time Changes Everything

OpenAI released GPT-5.6 Sol, a model it says achieves sub-100ms time-to-first-token latency for real-time agent applications, according to September 2026 pricing data. The model uses an architecture codenamed FlashDecode that keeps a warm cache of initial layers across requests with a 60-second refresh cycle, priced at $4.00 per million input tokens and $20.00 per million output tokens.

OpenAI just shipped GPT-5.6 Sol with what might be the most practical breakthrough this year: sub-100ms time-to-first-token for real-time agent applications. That's not a benchmark. That's a latency floor so low that conversational AI finally feels natural at the code-execution level. | Metric | GPT-5.6 Sol | Claude 3.7 Sonnet | Gemini 3.7 Flash | |---|---|---|---| | TTFT | <100ms | 210ms | 350ms | | Throughput | 180 tok/s | 90 tok/s | 340 tok/s | | Input price | $4.00/M | $3.00/M | $0.75/M | | Output price | $20.00/M | $15.00/M | $3.75/M | Source: September 2026 pricing data You're not building chatbots anymore. You're building agents that need to think before they speak — and every millisecond of delay compounds across hundreds of API calls. Sol's new architecture codenamed "FlashDecode" keeps a warm cache of the model's initial layers across requests with a 60-second refresh cycle. That's the secret sauce. Your agent doesn't wait for a cold start on every turn. For a B2B product configurator like MedalCraft, this means: $4 input / $20 output looks steep until you compare it to what it replaces. A human sales rep needs 15 minutes to produce a custom quote with mockups. At $0.10/token for Sol, that conversation costs roughly $0.50 in API fees. The math isn't even close. If you're evaluating Sol for production workloads: The race isn't over. It's just moved from "who's most accurate" to "who's most useful." What's your experience with real-time agent latency? I'm curious which use cases are finally viable.