Multi-model check when it really counts.
How confident is the answer?
AI sometimes sounds most confident exactly when it's completely making things up.
When it really counts, you can switch on the AI turbo — and the best models step into the ring for you. Several independent models check each other. “discode can disagree — with itself.”
Ask a question that really matters.
Judge → A wins · 3 Modelle, blind bewertet
Der Vertrag ist kündbar: §8 erlaubt die ordentliche Kündigung mit drei Monaten Frist zum Quartalsende. Die Schriftform ist zwingend …
Eine Kündigung ist möglich. Beachte die Frist in §8 und die Formvorschrift. Eine außerordentliche Kündigung käme nur bei wichtigem Grund …
Ja, du kannst kündigen. Schau in den Abschnitt zu Laufzeit und Fristen; sende die Kündigung am besten per Einschreiben …
Trio & Judge
When being wrong has consequences — contracts, law, fact-checks, medicine — you have several independent models compete and a fourth judge them. That lifts factual precision from ~73 % to ~96 % and pushes hallucinations from ~25 % down below 2 %. Slower and pricier than Solo — but dependable.
1. Battle
Your question goes in parallel to three models from three provider families — genuinely different perspectives, not the same training bias three times over.
2. Judge
A separate model judges all answers blind and in random order (to counter position bias), finds the consensus and picks the strongest elements.
3. Synthesis
A final answer from the best of the three. Discrepancies aren't hidden but flagged — you see where the uncertainty sits.
How Trio works
Three models, an independent referee, one synthesised answer — automatically, without you setting anything up.
Challenger
The first answer counts as a draft — because that's what it is. A model from another provider reads it and looks specifically for what's going wrong: logical gaps, missing context, unsupported claims.
1. Critic
A model from another provider checks every statement and flags critical problems, logical gaps and missing info. If all findings are minor, the process ends here.
2. Improver
A different model family processes the critique and writes an improved version that addresses the gaps head-on.
3. Refiner
If problems remain after that, a final round tightens everything up and fills in what's still missing.
The three Challenger rounds
Each round guarantees a different provider family — so the same blind spot doesn't check twice. Early exit as soon as only minor issues remain; the model sequence is optimised per domain (maths, code, law, medicine).