GLM-ASR is the speech member of the GLM model family: a dedicated automatic speech recognition model that turns audio into text in real time, with a published character error rate of 0.0717. It is not a language model with an audio front-end bolted on — it is a purpose-built recognition system, and it is evaluated on the metric that actually matters for transcription rather than on reasoning benchmarks.
A character error rate of 0.0717 means roughly seven characters in a hundred are wrong, counting insertions, deletions and substitutions. In practice that is comfortably readable output for meeting notes, captions or voice commands, with proper nouns and domain jargon remaining the usual sources of the residual error. Like the rest of the family, GLM-ASR is MIT licensed with weights published openly, so the audio never has to leave your own infrastructure.
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Character error rate is the edit distance between the transcript and the reference, divided by the length of the reference — so 0.0717 means about seven errors per hundred characters. It is a stricter and more language-agnostic measure than word error rate, which is why it is the standard metric for systems that have to handle languages without clear word boundaries. What the number does not tell you is where the errors fall. Recognition systems are consistently weakest on names, product terms, acronyms and heavy accents, and strongest on ordinary conversational speech, so a transcript can sit at 0.0717 overall while being effectively perfect on the body text and wrong on exactly the terms you wanted to search for. If accuracy on domain vocabulary matters, evaluate on your own audio and consider biasing the decoder toward a term list rather than judging by the headline figure alone.
GLM-ASR is a speech recognition model and does not appear in the published text-LLM comparison table. Its published figure is the 0.0717 character error rate quoted above; no LLM benchmark column exists for it, and none is invented here.
The 17-benchmark comparison table on this site covers text models — reasoning, coding and agentic evaluations — and a speech recogniser is not comparable on any of those axes. That is why this page reports the character error rate and stops there. If you need the text side of the family, the flagship page carries the full published column and the benchmarks hub carries all 17 evaluations across 8 models.
Comparison on Terminal-Bench 2.1 (Terminus-2), as published by the model authors. Shown for orientation only — these are text models, not speech models.
Speech recognition is usually one stage of a pipeline: transcribe, then reason over the transcript. If that is what you are building, GLM-ASR handles the first stage and a text GLM model handles the second, and the chart above shows the published gap between the two most recent text releases on Terminal-Bench 2.1 — 81.0 for GLM-5.2 against 63.5 for GLM-5.1. Both stages can run on your own hardware under the same MIT license.
GLM-ASR sits outside the text lineage entirely — it is the family's speech branch rather than a step between GLM-4.7 and GLM-5. Pair it with a text GLM model when you need transcription followed by reasoning; the lightweight GLM-4.7-Flash tier is a natural partner when the whole pipeline has to run locally, and GLM-5.2 when the reasoning stage is genuinely hard.
The lightweight 30B tier. Efficient and fast enough for local inference, and reported to outrank open models of a similar scale.
The 1M-token context flagship. It is the open-source state of the art on coding and long-horizon evaluations, with explicit Non-Thinking, High and Max effort levels.
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