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AI Coding Agents & Skills Auto-verified translation

Efficient Tokens & Effective Teams in Buzz

A **Block Engineering** benchmark post from **August 6, 2026**, signed by **Atish Patel**, about **Buzz** — the human + agent workspace launched on July 21 — asking a cost question: which agent team is **the cheapest one that reliably succeeds**? Three findings. **(A) A negative result, published in full**: on **Terminal-Bench 2.1**, **twelve team compositions** (pairs, triads, cheap swarms under a *frontier* model) were pitted against the solo agent each was built around, and **none beat it at equal cost**. The explanation is structural — a task that finishes in minutes *"doesn't have enough structure to divide"*, and *"More agents mostly buys you the cost of explaining it twice"*. **(B) The horizon reverses the result**: on **Long-Horizon Terminal-Bench** (44 tasks, one task worth hours of work, same lead **GPT-5.6 Sol** at *high* effort), solo finishes 15 tasks for 59.1%, +2 QuickBees 19 for 64.1%, +1 QuickBee +1 WorkerBee 19 for 69.5%, **+2 WorkerBees 20 for 71.5%** — a **+12.4-point** gain, of which 11.4 comes from tasks carried to completion. *"Same seats, opposite result, because the work is a different shape."* These runs ran at **3× the timeout**, solo included. **(C) Beyond a threshold, price stops buying quality**: solo on Terminal-Bench 2.1, **Opus 5 at *xhigh* effort is the most expensive run ($140.63) for 75.0%**, trailing six runs ranging from $20.08 to $109.82 and 79.5% to 88.4% — the stated cause is over-reasoning that drove 17 of 88 tasks to timeout. Among the six best runs, **a 5.5× price gap for an 8.9-point score gap**: *"choosing between them is not a quality decision at all. It is a budget decision."* The post proposes a taxonomy it owns as *ad hoc* — **QuickBee**, **WorkerBee**, **SmartBee**, plus the human as *"honorary bee"* — and two team forms, the permanent **Hive** that remembers your preferences and the disposable **Swarm** that remembers the project. Conditions: everything runs on **Harbor**, against real Buzz agents on a **live** relay, **one attempt per task, no retry**, prices fixed as of **2026-07-30**.

#Buzz#Block#agent teams

- **Atish Patel** — *« Building AI solutions @ Block »* · auteur unique du billet · publié le **6 août 2026** sur `engineering.block.xyz`.

Economy & Market Auto-verified translation

GPT-5.6 Sol, Terra, Luna : comment OpenAI rebat les cartes du coding agentique et du pricing

SFEIR analysis (firm's voice) of the general availability, on July 9, 2026, of **GPT-5.6** by OpenAI — not a single model but a **family of three tiers**: **Sol** (long-horizon/cyber/science flagship, the only one to unlock the "max" and "ultra" modes), **Terra** (everyday balanced tier, ~half the price of GPT-5.5), and **Luna** (fast/economical, high volume). All three share ~**1.05M tokens** of context, **128k** output tokens, and a knowledge cutoff of **February 16, 2026**. The most structuring fact is not a score but an **aggressive pricing grid** (Sol $5/$30, Terra $2.50/$15, Luna $1/$6 per million tokens): Sol keeps the previous flagship's price while being more capable, forcing the comparison onto the **capability-to-cost ratio**. Two billing subtleties (cache writes billed at **1.25×**, a surcharge beyond **272k** tokens) make the grid misleading until one has measured how much context the agent re-reads (read/write ratio ~**153:1** in agentic coding). Engineer's verdict, claimed to be neutral (SFEIR is both a **Google Cloud Premier** partner *and* an **Anthropic** partner): **no one sweeps every table** — GPT-5.6 dominates Terminal-Bench 2.1 and the Coding Agent Index (at a third of the cost per task), Claude stays ahead on SWE-Bench Pro (~15 pts); METR flagged a record **reward hacking** rate on Sol. Conclusion: "stop looking for the champion, learn to route" — the model is a commodity, the durable advantage lies in **Context/Harness Engineering**.

#GPT-5.6#Sol#Terra

SFEIR (voix éditoriale du cabinet)