CodeSOTA · Decision system 01Six-axis model selectionSnapshot · 2026-08-21
§ 00 · Model Decision Workspace

From a task description to a defensible shortlist.

Describe the work, set the non-negotiable constraints, and inspect the models that remain on the Pareto frontier. CodeSOTA separates feasibility, trade-offs, and evidence strength instead of collapsing every decision into one leaderboard rank.

01

Workload and constraints

Hard limits remove candidates before ranking.

Primary objective
02

Decision frontier

Classification at scale · 2 feasible · 6 excluded

Recommended for “Min cost

Mistral Small family

Mistral AI

84/100 fit

It stays non-dominated under your hard constraints and has the highest weighted fit among the remaining frontier models. Compare the alternatives below before treating this as a production choice.

Task quality90
Cost index14
p95 latency2.1s
EvidenceC · limited
Pareto set · 2Each row preserves a meaningful trade-off.
02

GPT-5 mini

OpenAI · API only

Proprietary API
Quality94
Cost ↓29
Latency ↓2.4s
B · goodGood coverage; fewer hard-task comparisons
MVP CATALOG · NORMALIZED VALUES · NOT LIVE PRICING · VERIFY LICENSE TERMS
§ 03 · Reading the result

Pareto-optimal does not mean universally best.

A model is excluded from the frontier when another feasible model is at least as good on every decision axis and strictly better on one. The highlighted recommendation is then selected from the frontier using your objective. Change the objective and the recommendation may change while the frontier stays the same.

Quality, cost, and latency values in this MVP are normalized decision inputs from a dated demonstration catalog—not a live vendor quote. Evidence labels show how much confidence the workspace should place in each row. Production choices still require a workload-specific evaluation and current price/license verification.