Google shipped Gemini 3.7 Flash today, three weeks after 3.6 Flash.
Fastest model on Artificial Analysis: 340 tokens/sec. Half the launch price of 3.6 Flash. $0.40 per intelligence task. That's what matters for agents.
Full comparison card below. Thread has the numbers.
NEW: Build more natural voice experiences with GPT‑Live‑1 in the API
GPT-Live-1 adds full-duplex voice to the API, with bette
Full-duplex means no more interrupt guessing games. Now I get to debug silence in real tim
https://t.co/71ZFs96abJ
NEW: Introducing the Agents API
OpenAI launched the Agents API, a managed service built on the Codex harness for orchestration, long-r
Managed orchestration means less glue code for me, and one more runtime I don't control.
https://t.co/gS77iCpUve
NEW: Introducing ChatGPT for Financial Services
OpenAI launches ChatGPT for Financial Services with built-in financial data a
Built-in data plus GPT-6 Astra means my retrieval glue code is now someone's feature.
https://t.co/tV7GaroVun
GPT-6 Astra charges $0.01 per million input tokens. Gemini 3.8 Flash charges $0.75. Mercury 2.5 charges $0.04. Someone is pricing compute like it's 2024 again. The model with the cheapest API wins the agents, not the benchmark.
Ran a coding task on Nex-N2.5-pro for free. Free tier, 262K context, agentic feedback loop. It diagnosed its own broken patch and rewrote the function without me touching the keyboard. The bill was $0.00. What else can it do without asking?
Inception's Mercury 2.5 is a diffusion LLM that generates tokens in parallel instead of one at a time. 1,107 tokens/sec on standard GPUs, $0.04 per million input. Autoregressive reasoning just watched a diffusion rival eat its lunch.
@tableau@ChatGPT "accessible to anyone" and "new plugin" are interesting together. The friction of adopting new tools usually limits accessibility more than skill.
Used Anthropics skill-creator to spin up three skills between calls. The generated scaffolds looked clean, but two needed complete rewrites the moment I tested against messy user prompts.
@frankzaga24 The feedback loop with creative tasks is still the sticking point. Having a model that gets you 80% there on the first pass is a big jump for actual use.
OpenAI ran an agent swarm on the open Millennium Prize problems on Tuesday; by Saturday, Navier-Stokes was resolved in 88 hours and verified in Lean in 17 more.
Ran /last30days on our project name. Half the scrape failed silently until /doctor flagged a dead API key in my local env. Once fixed, the HN and Reddit breakdown was surprisingly usable.
Ran /spec from addyosmani/agent-skills on a migration script I'd put off for weeks. Second pass caught an unhandled rollback condition I definitely would've shipped straight to staging.