GLM ASR Nano 2512
Z.ai · released Dec 9, 2025 · zai-org/GLM-ASR-Nano-2512
Speech to textTranscribes a recording into words
- Type
- Open weightsMIT License
- Languages
- 2
- Size
- 2.3B
Context measured in tokens
Our take
Written Aug 2, 2026GLM ASR Nano 2512 is a tiny downloadable speech-to-text model from Z.ai that turns audio into written words at exceptional speed. It handles English and Chinese under a permissive MIT licence, though its accuracy drops sharply in noisy or accented conditions.
Pick this for offline or edge transcription where speed matters most — it processes an hour of audio in about eleven seconds on benchmark hardware. Use it for clean read-aloud or scripted audio, or projects needing a permissive open licence. Skip it if you are transcribing meetings, accented speech, or podcasts and video, where its error rate rises more than eightfold; or if you need hosted inference, as no providers are currently listed.
The case for it
- Extremely fast: processes an hour of audio in roughly eleven seconds on benchmark hardware.
- Strong on clean read-aloud speech, with a word error rate roughly 3.6 times better than its own overall average.
- Permissive MIT licence allows commercial use, modification and redistribution.
- Small enough for edge deployment at 2 billion parameters.
The case against it
- Accuracy collapses in challenging conditions: recorded meetings show an error rate more than eight times higher than clean read speech.
- No hosted inference options currently listed in our data.
- Overall word error rate of roughly one word in seventeen, with no comparison data to judge it leading for general use.
How good is it?
TranscriptionTurning speech into text2.5 of 5Open ASR WER · 42nd of 74
94%
Misses roughly one word in 17, averaged over nine English test sets.
333×45th of 62
an hour of audio in 11 seconds, on the board's own hardware. Your machine will differ.
2
Listed on the model card. The accuracy above is English only.
Percentage of words wrong on each set, lower better. Bars are scaled to this model's own worst case, not to the board.
Which languages ↓ ↑
English · Chinese
The figures above come from the Open ASR Leaderboard, an independent public test that runs every model on the same recordings. It is the only measurement of transcription quality we know of, so there are no other scores to show.
These tests check whether a model follows instructions — a precondition for all the work above, but not a measure of how well that work is done, which is why they get no rating.
Every published score for this model9 scoresEvery figure we hold, from 9 boards, with who ran it and a link to the source — including the boards no rating above is built on.
Can you run it yourself?
- Fits in memory
- weights load entirely on the card
- Spills to system RAM
- some weights offload; much slower
- Too large
- will not load even with offload
- est
- size is calculated; the verdict could change by 10%
Comfortable fit
GeForce RTX 4090 · 24 GB
Room to spare. 20 GB spare means a 10% error in the size would not change the answer.
GeForce RTX 5090 · 32 GB
Room to spare. 28 GB spare means a 10% error in the size would not change the answer.
Apple M2 (8-core GPU, 8GB unified) · 8 GB
Room to spare. 3.2 GB spare means a 10% error in the size would not change the answer.
Memory use by level
Against a 24 GB card.
All 3 sizes here are calculated, not measured. We hold no measured file for this model, so each size comes from the parameter count and every verdict above inherits that uncertainty.
Check against your own machine → · All 71 devices, with every size →
When we formed this view
Dates behind this page
Prices last checked 6h ago
What we do not know about this model yet
- We hold no measured file for it, so all 3 sizes on this page are calculated from the parameter count.
- Nothing we hold says whether an endpoint streams, so we do not show it either way.
Licence and identifiers
What the licence allowsMIT License, what it allows commercially, and the identifiers you need to pull this model — its Hugging Face repo, our slug and a machine-readable card.
Licence
MIT License
Fully permissive: do anything with attribution. No patent grant, unlike Apache-2.0.
Identifiers
- Hugging Face
- zai-org/GLM-ASR-Nano-2512
- Architecture
- Dense
- Modality record
- audio->text
- Catalogue slug
- zai-org-glm-asr-nano-2512