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

> Source: <https://dev.to/kd_jiang_cb6ed42090a6f3f5/openais-gpt-56-sol-sets-new-record-sub-100ms-response-time-changes-everything-ooe>
> Published: 2026-09-20 05:28:09+00:00

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.*