Codex Becomes a Super-Individual, EvoX Builds a Swarm — Two Roads for Multi-Agent
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发布于 2026-07-28
A Chinese team called EvoMap matched OpenAI Codex on six multi-agent benchmarks while running 3x cheaper — by throwing out the planner agent entirely. Codex bets on one super-individual; EvoX bets on a deterministic swarm. The bottleneck was never the model.
Codex Becomes a Super-Individual, EvoX Builds a Swarm — Two Roads for Multi-Agent
Codex is everywhere. In April, OpenAI shipped computer use, in-app browsing, image generation, memory, and plugins into the macOS and Windows Codex app (OpenAI Blog, 2026-04-16). In July, the $150 Codex Micro keyboard sold out in 14 hours. The same week, a four-month Pro user described running thirty image-model comparison cases by handing the whole job to Codex, going to sleep, and waking up to a finished spreadsheet (袋鼠帝, 2026-07-27; ifanr, 2026-07-24).
One agent. Every workflow. One human at the joystick.
That is Codex's road.
A small Chinese team called EvoMap just published a counter-road. On the same model (Opus 4.8), across six benchmarks and 424 tasks, EvoX matched Codex at 338 solved tasks, behind Claude Code's 358 by less than five points. Per-task cost under cache-aware pricing: $1.95 — visibly the lowest of the three (AI科技评论, 2026-07-26).
The numbers should not be possible. The bottleneck of multi-agent has been "the relay" — and the relay is exactly what EvoX throws away.
Two roads, same destination, opposite bets on who is in charge.
