Long chains without drift.
Llana sustains multi-step arguments over thousands of tokens, re-reading its own premises when it needs to.
Llana är Kapllans flaggskepp inom resonemang — byggd för långsiktiga problem där formen på ett bra svar inte är uppenbar. Den läser noga, visar sina steg och föredrar att ha rätt framför att vara snabb.
Llana sustains multi-step arguments over thousands of tokens, re-reading its own premises when it needs to.
128K context with structural awareness — call graphs, test intent, the difference between a bug and a choice.
Calibrated uncertainty — Llana will decline, hedge, or ask a clarifying question before it invents an answer.
A tool-use interface that treats every action as revocable — Llana narrates its intent before it takes one.
Charts, diagrams, scanned pages, handwritten notes — Llana reads images with the same care it brings to text.
Every refusal comes with a justification you can argue with — not a flat wall. Transparency is a design goal, not a patch.
| Benchmark | What it measures | Llana 3.2 | Prior SOTA |
|---|---|---|---|
| ▸MMLU-Pro | Multi-discipline reasoning | 84.1 | 81.3 |
| ▸GPQA-Diamond | Graduate science Q&A | 71.8 | 68.0 |
| ▸SWE-bench Verified | Real-world coding tasks | 62.4 | 58.9 |
| HumanEval | Code synthesis | 94.7 | 94.2 |
| ▸MATH-500 | Competition mathematics | 88.5 | 85.1 |
| AIME 2025 | Olympiad-level problems | 54.2 | 52.0 |
"Vi vill inte ha en modell som talar tvärsäkert om allt. Vi vill ha en som känner formen på sin egen okunskap."— Ur Llana 3:s tekniska rapport
We release models when their behavior is understood, not when a demo looks clean. We would rather publish a late, calibrated model than an early, charismatic one.
Every capability claim is tied to a public evaluation, a dataset, or a paper. If we cannot describe how we measured it, we do not ship it.
Research that doesn't reduce to a screenshot is still research. A good question is a legitimate deliverable. We pay for depth.
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