Aleph Alpha

Aleph Alpha's Kolibri is a hybrid reasoning model whose system prompt carries one sentence selecting no reasoning, low, medium or high effort. Because its supervised fine-tuning traces carried no effort labels, the team annotated each trace post hoc by its reasoning length relative to traces from the same dataset at the same judged difficulty, with a 2B student model reproducing a larger model's difficulty grades. Median reasoning length orders low below medium below high on 23 of 24 benchmarks; low keeps 96% of the high score at 35% of the length, and the released model keeps 98% at 76% of the completion tokens.

Aleph Alpha's Kolibri is a hybrid reasoning model whose system prompt carries one sentence selecting no reasoning, low, medium or high effort. Because its supervised fine-tuning traces carried no effort labels, the team annotated each trace post hoc by its reasoning length relative to traces from the same dataset at the same judged difficulty, with a 2B student model reproducing a larger model's difficulty grades. Median reasoning length orders low below medium below high on 23 of 24 benchmarks; low keeps 96% of the high score at 35% of the length, and the released model keeps 98% at 76% of the completion tokens.

Aleph Alpha distilled its self-checking agentic parsing harness into a single vision-language model, Parsy, which handles each page in one pass. The harness reached 86.0 on ParseBench by running several models and up to three passes per page, too slow and costly for millions of pages a month. Parsy was trained on harness-labeled public documents whose labels were cleaned with an independent layout model, an ink-coverage check and a second OCR model; it scores 87.9 on ParseBench, parses hundreds of thousands of pages per day on one H100, and costs one to two orders of magnitude less per page.

Aleph Alpha distilled its self-checking agentic parsing harness into a single vision-language model, Parsy, which handles each page in one pass. The harness reached 86.0 on ParseBench by running several models and up to three passes per page, too slow and costly for millions of pages a month. Parsy was trained on harness-labeled public documents whose labels were cleaned with an independent layout model, an ink-coverage check and a second OCR model; it scores 87.9 on ParseBench, parses hundreds of thousands of pages per day on one H100, and costs one to two orders of magnitude less per page.

Bits-per-byte (BPB), a common pre-training score, divides a model's surprisal on the gold answer by that answer's UTF-8 byte length, yet part of the cost does not grow with answer length, so short-answer tasks and short items are penalized. Aleph Alpha's team fits each item's bits as a fixed overhead plus a per-byte rate, shows corpus BPB equals that fixed overhead divided by the task's mean gold length plus the per-byte rate, and releases PriorBPB in the open-source eval-framework: it integrates a per-byte cost curve against one length prior shared across tasks. Across five model families and more than 60 checkpoints, PriorBPB is far less correlated with mean gold length and tracks accuracy better than standard BPB.

Bits-per-byte (BPB), a common pre-training score, divides a model's surprisal on the gold answer by that answer's UTF-8 byte length, yet part of the cost does not grow with answer length, so short-answer tasks and short items are penalized. Aleph Alpha's team fits each item's bits as a fixed overhead plus a per-byte rate, shows corpus BPB equals that fixed overhead divided by the task's mean gold length plus the per-byte rate, and releases PriorBPB in the open-source eval-framework: it integrates a per-byte cost curve against one length prior shared across tasks. Across five model families and more than 60 checkpoints, PriorBPB is far less correlated with mean gold length and tracks accuracy better than standard BPB.