World Wire

Open speech recognition stopped being a Whisper monoculture some time in the last twelve months. In March 2026 Cohere released Transcribe , a 2B Apache 2.0 model that took the top of the Hugging Face Open ASR Leaderboard at 5.42% average word error rate. Five weeks later IBM shipped Granite Speech 4.1 2B at 5.33%. Since then ARK-ASR-3B and MOSS-Transcribe-preview-2B have posted lower numbers still. The top of that leaderboard is now separated by less than one WER point. That has a specific consequence for anyone choosing a model: rank is no longer the deciding variable. License, language coverage, streaming support, and cost per audio-hour are. This roundup compares the field on all four. First, a problem with the leaderboard number everyone quotes The Open ASR Leaderboard average is not a single fixed quantity, and the models currently listed side by side were not all scored the same way. Cohere’s 5.42% is an average across eight English test sets, ...

Tokenization is the one part of the language modeling stack that almost nobody profiles. Gigatoken , released by Marcel Rød (a PhD student from Stanford)  under an MIT license, argues that this was a mistake. The library encodes text at gigabytes per second on a single machine, against baselines that are already multithreaded Rust. The GPT-2 tokenizer benchmarking yields remarkable results: evaluated on the 11.9 GB owt_train.txt corpus using a 144-core AMD EPYC 9565 dual-socket setup, Gigatoken processes data at a staggering 24.53 GB/s . In comparison, OpenAI’s tiktoken achieves 36.0 MB/s, while HuggingFace tokenizers registers at 24.8 MB/s on the identical hardware configuration. These marks demonstrate performance advantages of 681x and 989x, respectively. On an Apple M4 Max with 16 cores, the same GPT-2 workload runs at 8.79 GB/s, or 1,268x HuggingFace tokenizers and 140x tiktoken. On a consumer AMD Ryzen 7 9800X3D, it runs at 6.27 GB/s, or 106x and 68x. The sp...