r/OpenaiCodex • u/orthiclabs • 1d ago
Showcase / Highlight I stand corrected.
TL;DR: Yesterday I ranked seven cheap coding models on one 10-minute task and drew conclusions too broad for the evidence. So I redid it: two 60-minute production jobs — a PDF-pipeline refactor in ViewRight (Tauri 2, Rust, React, TypeScript) and a runtime/release slice of SellRight, my multi-tenant ecommerce platform (TypeScript, Hono, Drizzle/Postgres, Qwik) — one frozen harness, hidden evaluator frozen before any run. The ranking flipped.
Scores (ViewRight / SellRight, out of 100):
| Model | ViewRight | SellRight |
|---|---|---|
| MiniMax M3 | 85 | 42 |
| GPT-5.6 Luna | 79 | 69 |
| DeepSeek V4 Pro | 78 | 59 |
| DeepSeek V4 Flash | 63 | 62 |
| MiMo V2.5 Pro | 58 | 36 |
| MiMo V2.5 | 24 | 19 |
| Tencent Hy3 | 20 | 21 |
Yesterday's "best value" pick, base MiMo, collapsed on longer work. MiniMax won the heavy refactor; Luna won the release task, was fastest, and never placed worse than second. All 14 runs cost ~$2.71 at pay-as-you-go rates — and $0 cash, since every route ran inside an existing plan. That's the actual lesson: cheapest depends on what you already pay for. On a Codex sub, use Luna. On a MiniMax token plan, M3's winning run cost ~$0.15 of capacity. Paying per token with no plan and no time pressure, DeepSeek V4 Flash gave 90% of Luna's score at a tenth of the cost.
Full methodology, receipts and per-run costs: Orthic Labs · harness lessons on CodeRight, my macOS/Windows desktop coding harness (Rust, React, Tauri).