Head to head · Speech-to-text · October 2026 research run

Granite Speech 5.0 2B vs Ming-3.0-audio-tiny

Granite Speech 5.0 2B scores 61 (C) on agent readiness against Ming-3.0-audio-tiny's 52.1 (D), and leads in 3 of 4 scored categories. Ming-3.0-audio-tiny leads on agent interface. Both do speech-to-text.

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Which one, for what

Granite Speech 5.0 2B C

Best for Agents that need to transcribe or translate short recordings in the six supported languages on modest hardware

Ahead on

  • Reach, 95 against 45
  • Risk, 70 against 50
  • Documentation and upkeep, 92 against 61

Watch for

About two minutes of audio per request, so long recordings must be split

Ming-3.0-audio-tiny D

Best for Self-hosted Chinese and English transcription, audio questions and spoken replies with tool calls on one GPU with about 25 GB free

Ahead on

  • Agent interface, 65 against 55

Watch for

No named runtime runs it in a released version, only SGLang-Omni main and inclusionAI's own branches

Why the edge

Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.

Reach Granite Speech 5.0 2B +50

Granite Speech 5.0 2B 95

Memory 30, the smallest build listed that keeps 4 bits is IBM's Q4_K_M GGUF, 1.14 GB with the 1.16 GB audio projector, and 2.30 GB × 1.2 = 2.8 GB, within 16 GB. IBM recommends Q8_0, 3.12 GB with the projector. Runtimes 20, Released Transformers 5.22.2 and vLLM 0.33.0 have the granite_speech model, llama.cpp b12242 has its converter and audio front end, and Ollama carries it as ibm/granite5-speech, which the card says was tested with v0.40.15. Builds 15, IBM publishes GGUF builds at fourteen quantised precisions, from Q2_K to Q8_0, with the audio projector. Terms 15, Apache 2.0 in the metadata and on the card, with nothing added. Download 10, the Hub API reports gated false for both repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, IBM runs a Hugging Face Inference Endpoint that serves this model through an OpenAI-compatible API, with the address and code on the card. 30 + 20 + 15 + 15 + 10 + 5 = 95.

Ming-3.0-audio-tiny 45

Memory 20, the only build inclusionAI publishes is BF16 at 20.78 GB, which with a fifth on top is 24.9 GB, just past the 24 GB band, so it lands in the 32 GB band. Runtimes 0, none of the six named runtimes runs the audio architecture in a released version on 4 November 2026 (vLLM v0.33.0, SGLang v0.5.23, Transformers v5.22.1, llama.cpp b12116 with the text backbone only, Ollama through its llama.cpp pin; LM Studio per 21 October). SGLang-Omni serves it from main but isn't on the list. Builds 0, no quantised build from inclusionAI. Terms 15, plain MIT, with a bundled fastText file under its own CC BY-SA 3.0 notice. Download 10, no gate. Hosted 0, no inference provider on Hugging Face and no inclusionAI API found. 20 + 0 + 0 + 15 + 10 + 0 = 45.

Documentation and upkeep Granite Speech 5.0 2B +31

Granite Speech 5.0 2B 92

Model card 17, Intended use, limitations, the architecture, the size, the 16K positions and two-minute segments, and the languages are on the card. Training data 20, the data is listed with sources and sizes: a 39-row table giving each corpus, its task, its hours and a link to where it comes from, with the synthetic sets described. Last change 20, the last commit, on 6 November 2026, updated the card, 4 days before this check. Responsiveness 20, Both discussions that aren't pull requests (21 and 28 October 2026) have a reply from an ibm-granite member. Version policy 5, granite-speech-4.1-2b stays in its own repository and 5.0 came out in a new one, which names its predecessor and gives a release date. File health 10, Config, template and card agree on the architecture and the 16K positions. 17 + 20 + 20 + 20 + 5 + 10 = 92.

Ming-3.0-audio-tiny 61

Model card 11, it covers intended use, architecture, parameters and languages and leaves out limitations, context length and a knowledge cut-off. Training data 15, a summary on the EU AI Act template added on 21 October 2026 names the mix, Ant Spider crawls from March 2024 to May 2026 and the filtering, with sizes only as ranges. Last change 20, the card changed on 29 October 2026. Responsiveness 0, the only discussion is a merged pull request and nobody outside inclusionAI has asked anything yet, and GitHub issues couldn't be checked. Version policy 5, a first release. File health 10, config, template, card and profiles agree and no broken-file report waits. 11 + 15 + 20 + 0 + 5 + 10 = 61.

Risk Granite Speech 5.0 2B +20

Granite Speech 5.0 2B 70

File safety 25, safetensors and GGUF only, no .bin, .pt or .pth files and no Python in either repository. Provenance 15, the files sit in the ibm-granite organisation, which IBM's Granite docs link to (10). Safety evaluation 0, no safety results on the card. Agent guidance 10, the card says what the model does with a prompt it doesn't know: it ignores an unfamiliar or malformed prompt and transcribes, which it says limits how audio can steer the system. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, www.ibm.com/.well-known/security.txt names IBM PSIRT, read for the Granite 4.2 3B listing on 4 November 2026 (5). 25 + 15 + 0 + 10 + 15 + 5 = 70.

Ming-3.0-audio-tiny 50

File safety 15, safetensors only with no pickle weights, less 10 because every documented way to load it runs the repository's code, and 40 compiled .pyc files sit under talker/. Provenance 15, the files are in inclusionAI's own organisation, which its GitHub repository and card link, and every file has a checksum. Safety evaluation 0, none published. Agent guidance 0, the only caution is about reviewing remote code before loading it. Licence risk 15, MIT with no policy, threshold or revocable terms, and the card's licence link resolves. Security programme 5, for Ant Group's AntSRC reporting centre, read as a company-wide route the way Google's was for Gemma, with no SECURITY.md, security.txt or advisories found.

Agent interface Ming-3.0-audio-tiny +10

Granite Speech 5.0 2B 55

Chat template 20, chat_template.jinja in the repository (193 characters). Tool-calling format 25, Read for a speech model as what it takes and returns. Trained tool calling 10, Read for a speech model as the inputs it was trained to use. Structured output 0, Read for a speech model as typed outputs. Reasoning control 0, A speech model with no reasoning. Context 0, the language model is extended to 16K positions, per the card and config.json (16,384), below the 32K band, and the card says to send audio in segments of about two minutes. 20 + 25 + 10 + 0 + 0 + 0 = 55.

Ming-3.0-audio-tiny 65

Read for open weights and, as a speech model, for what it takes and returns, while this model also has a chat interface with function calls. Chat template 20, a Jinja file in the repository. Tool-calling format 10, the format of tool calls comes only from the template and no parser is named, although inclusionAI does document its audio inputs, voices, cloning and output modalities for its own serving branches. Trained tool calling 20, the template renders several calls in one turn and inclusionAI reports function-calling results for Tiny. Structured output 5, transcripts come back in a documented typed form (language label, tab, text) and speech as WAV or streamed PCM, but no JSON, JSON Schema or timestamps are documented. Reasoning control 0, thinking can be turned on per request but nothing says where the reasoning text comes back. Context 10, 131,072 positions in config.json with no scaling, matched by the publisher's vLLM-Omni profiles.

Score by category

CategoryWeight this runGranite Speech 5.0 2BMing-3.0-audio-tinyEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%259545Granite Speech 5.0 2B +50
Risk20%207050Granite Speech 5.0 2B +20
Agent interface10%105565Ming-3.0-audio-tiny +10
Documentation and upkeep10%109261Granite Speech 5.0 2B +31
Negative events≤1500
TotalC 61/100D 52.1/100

Facts side by side

FactGranite Speech 5.0 2BMing-3.0-audio-tiny
KindOpen-weight modelOpen-weight model
VendorIBMinclusionAI (Ant Group)
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0MIT
Read-only variant documentednono
llms.txtnono
Last release2026-11-062026-10-20
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviewsnone2.5/5 (4)

How the scores have moved

Verdicts

Granite Speech 5.0 2B

A small, well-kept speech model with a clean licence: six languages, translation, keyword lists, IBM's own GGUF and an Ollama entry, a signed repository and training data listed corpus by corpus. It handles about two minutes of audio at a time and returns plain text, with no timestamps or speakers, and publishes no safety results.

Ming-3.0-audio-tiny

A small MIT-licensed audio-language model that transcribes Chinese and English, answers questions about audio and replies in a built-in or cloned voice. It needs about 25 GB and SGLang-Omni from main or inclusionAI's own vLLM-Omni and SGLang branches. Every loading path runs repository code, and nothing is published on safety.

Before you call either

Granite Speech 5.0 2B

  1. Split recordings into segments of up to about two minutes of 16 kHz mono audio before sending them
  2. Use the card's prompt for the task word for word, such as transcribe the speech with proper punctuation and capitalization.
  3. Use Q8_0. IBM says lower precisions lose accuracy on audio over about a minute
  4. To spell names and jargon right, add a keyword list to the prompt, up to 2,000 terms
  5. With vLLM, serve with --max-model-len 2048, as the card does
  6. An unknown prompt falls back to transcription, so check that the output is the task you asked for

Ming-3.0-audio-tiny

  1. Fastest start is SGLang-Omni from main, sgl-omni serve --model-path inclusionAI/Ming-3.0-audio-tiny --talker.gpu 0, which exposes OpenAI-style /v1/audio/transcriptions, /v1/audio/speech and /v1/chat/completions
  2. For transcripts use greedy decoding with 10-gram blocking and the validated ASR prompt, and expect a language label, a tab, then the text
  3. Parse tool calls yourself from <tool_call>name with <arg_key> and <arg_value> pairs, since no runtime parser is named
  4. Thinking is off by default, turn it on with chat_template_kwargs enable_thinking true and strip the <think> block from the reply yourself
  5. In vLLM-Omni pick a voice with audio.voice id from docs/voices.md, and don't combine it with a reference recording
  6. Review the repository's Python before passing trust_remote_code, and consider deleting the __pycache__ folders under talker/ so only readable source runs

Questions

Which is better for AI agents, Granite Speech 5.0 2B or Ming-3.0-audio-tiny?

Granite Speech 5.0 2B scores 61 (C) on agent readiness against Ming-3.0-audio-tiny's 52.1 (D), and leads in 3 of 4 scored categories. Ming-3.0-audio-tiny leads on agent interface.

Other comparisons with Granite Speech 5.0 2B or Ming-3.0-audio-tiny

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