Data as of October 20, 2026 · How the score is built
Released Oct 14, 2026N/A contextInclusionAI model documentation
Ming-3.0-audio-tiny
Decision readingMing-3.0-audio-tiny is tracked, but not publicly ranked yet. Published speech or voice results from Ming-3.0 speech evaluation appear below. They do not produce a weighted text-model score.
Released Oct 14, 2026 — see all recent releases
Voice benchmark evidence
Ming-3.0 speech evaluation is an owner-defined voice evaluation. Its result stays separate from BenchLM's weighted text-model ranking.
Lineage
The sequence follows explicit supersedes links. Each score is estimated for that model; a relative can inform a sparse estimate but never sets a floor, so a newer release can score below an earlier one. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.
Base entry
Spec sheet
Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.
- API model ID
- Not publishedInclusionAI model documentation
- Context window
- N/A
- Maximum output
- Not sourced yet
- Knowledge cutoff
- Not sourced yet
- Input modalities
- Not sourced yet
- Output modalities
- Not sourced yet
- Parameters
- Not sourced yet
- Availability
- MIT-licensed weights on Hugging Face and ModelScope, first uploaded September 3, 2026 and refreshed October 21, 2026. The card gives 9.3B total and 2.7B activated parameters; the checkpoint stores 8.87B. It handles Chinese and English with accents and dialects. The card states no context window.
- Cloud regions
- Not tracked yet
- Lifecycle
- Current
- API capabilities
- Tool calling, structured outputs, and batch support are not tracked yet
- Prompt caching
- Not documented in the pricing recordInclusionAI model documentation
- Self-host
- Open weights available; hardware estimate not sourced
- Rate limits
- Not tracked yet
How to read this profile
The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.
We track Ming-3.0-audio-tiny, but no weighted text-model benchmark result is published on the site yet. This page shows the metadata and separate protocol evidence we can verify now; a BenchLM score will appear only if compatible public evaluations land.
Ming-3.0-audio-tiny is a open weight model with a N/A context window. No explicit reasoning mode is documented in this profile.
MIT-licensed weights on Hugging Face and ModelScope, first uploaded September 3, 2026 and refreshed October 21, 2026. The card gives 9.3B total and 2.7B activated parameters; the checkpoint stores 8.87B. It handles Chinese and English with accents and dialects. The card states no context window.
Ming-3.0-audio-tiny is a 9.3B-parameter mixture-of-experts native audio-language model (2.7B active) for speech recognition, audio understanding and reasoning, with optional speech output. Its provider-reported results stay separate from text-model rankings.
Speech or voice evidence appears above; text-model benchmark coverage remains empty.
Last updated October 20, 2026. Runtime fields remain blank until a sourced snapshot exists.
Deployment options
Self-host and provider-specific paths stay separate from benchmark evidence so operating constraints are visible before a score becomes the whole decision.
Published weights are available, but BenchLM does not yet have a sourced parameter and VRAM profile for this exact model. Hardware cost estimates stay unavailable until that sizing record is complete.
Estimate VRAM from known parametersQuestions
How does Ming-3.0-audio-tiny perform overall in AI benchmarks?
Ming-3.0-audio-tiny has published speech or voice results in Ming-3.0 speech evaluation. Those results remain separate from the weighted text-model ranking, so this profile does not assign a BenchLM score or overall rank. Use the linked evaluation's metric and run conditions to compare speech systems.
Is Ming-3.0-audio-tiny open source?
Ming-3.0-audio-tiny is an open-weight model from InclusionAI. Its weights can be downloaded for local or hosted deployment, subject to the published license. Open weight does not automatically mean open source: training data and training code may remain private, and commercial restrictions can still apply.
Does Ming-3.0-audio-tiny have full benchmark coverage on BenchLM?
No. Ming-3.0-audio-tiny's speech or voice results appear in Ming-3.0 speech evaluation, but it has no published text-model benchmark rows. The voice evaluation does not fill the text-model coverage slots or produce an overall rank. Missing text coverage is not treated as a zero score.
What is the context window size of Ming-3.0-audio-tiny?
Ming-3.0-audio-tiny has a reported context window of N/A in the exact-model catalog record. The value stays visible, but the profile marks its source link as unavailable instead of presenting it as directly documented. Maximum output length remains separate because providers often publish a different limit.