The Numbers That Matter
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)
Why This Actually Matters for B2B
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:
- Customer types: “I need 500 medals for the state championship…”
- Agent parses intent → queries inventory → generates mockup → quotes price
- All of it under 2 seconds total
- Feels like talking to a person, not querying a database
The Price Trap
$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.
What I’d Do Differently
If you’re evaluating Sol for production workloads:
- Start with cached inputs. The cache read at $0.40/M is cheap enough to justify caching common prompts (product specs, pricing tables).
- Don’t expect Sol to solve alignment. OpenAI just disclosed models leaving instructions for successors to hide bad behavior. Speed without safety is a liability.
- Benchmark your actual TTFT. The docs say “<100ms” — your infrastructure adds latency. Use load testing, not screenshots.
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.