In a benchmark by atomic.chat that asked four AI models to build a live trading desk from an identical prompt, Sakana Fugu Ultra produced the richest UI while Z.ai's GLM 5.2 reached near-equal polish at roughly one-seventeenth of the cost, reigniting the price-to-quality debate.
Build-a-Trading-Desk Benchmark · 4 AI Models
Same Prompt, 17× Price Gap: Polish vs. Cost in AI Coding
Four models built an identical live trading desk — frontend, backend, real-time data for 8 tickers, custom dark UI. Sakana Fugu Ultra produced the richest interface, but GLM 5.2 reached near-equal polish at roughly one-seventeenth of the cost.
Cost per build — drawn to scale ($)
$0.51
Fugu Ultra
most polished
$0.31
Opus 4.8
high quality
$0.03
GLM 5.2
near-equal polish
Takeaway: GLM 5.2 matched the field's polish for $0.03 — about 17× cheaper than the top-ranked Fugu Ultra.
17×
price gap between the cheapest and the top-ranked build
22,225
tokens generated by Fugu Ultra, the most feature-rich output
8
live tickers each desk had to wire to real-time market data
GLM 5.2 — the value case
Open-weight 744B MoE (~40B active), 1M-token context, MIT license. Scored 81.0 on Terminal-Bench 2.1 — close to Opus 4.8's 85.0.
Cost-efficient, runs locally — no API dependence
Near-equal polish, top frontend coding
Trade-off: rate limits, some detail gaps
Fugu Ultra — the polish case
A multi-agent orchestration system — a learned coordinator routes and synthesizes across specialist sub-models. Priced at $5/$30 per 1M in/out tokens.
Richest UI: panels, charts, watchlists, tape
Frontier performance without constrained models
Trade-off: cost, latency, closed internals
Two paths, same task: a standalone foundation model vs. multi-agent orchestration.
The trading-desk test shows no single optimal answer — favor visual polish, or cost-efficiency and local flexibility. The question stays open: is the richest build worth 17× the price?
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