Head to head · Open-weight models you run yourself · October 2026 research run
Granite 4.2 8B vs Qwen3-Omni-30B-A3B-Instruct
Granite 4.2 8B scores 61 (C) on agent readiness against Qwen3-Omni-30B-A3B-Instruct's 41.8 (E), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons
Best conversational voice-agent APIs · All 581 voice agents comparisons
Which one, for what
Best for Agents that want a documented, signed, Apache 2.0 reasoning model with tool calling on a 16 GB machine
Ahead on
- Reach, 95 against 55
- Agent interface, 85 against 60
- Documentation and upkeep, 92 against 26
Watch for
No safety results published for it
Best for Agents on a machine with about 96 GB to spare that need one local model to hear, see and answer by voice
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
Only a 70.5 GB BF16 build, about 85 GB to run, with no quantised build from Alibaba
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.
Documentation and upkeep Granite 4.2 8B +66
Granite 4.2 8B 92
Model card 17, Intended use (best for), limitations (ethical considerations and limitations), architecture, parameters (8B), context length (128K native) and 12 tested languages are on the card. Training data 20, IBM's disclosure file for the 8B lists the datasets used in pre-training and post-training as machine-readable metadata, and the card describes the three training stages. Last change 20, the last commit, on 20 October 2026, replaced model.safetensors.index.json (titled "granite-4.2 index file fix"), 21 days before this check. Responsiveness 20, Of the 5 discussions that aren't pull requests (13 September to 5 November 2026), 4 have a reply from an ibm-granite member. Version policy 5, Granite 4.1 8B stays in its own repository and 4.2 came out in new ones. File health 10, Config, template and card agree on the architecture, the 131,072-token context and the thinking switch. 17 + 20 + 20 + 20 + 5 + 10 = 92.
Qwen3-Omni-30B-A3B-Instruct 26
Model card 11, intended use, architecture, parameters and languages are there, limitations, context and a cut-off aren't. Training data 5, the report's description has no cut-off. Last change 0, nothing that runs the model has changed for over a year. Responsiveness 0, Qwen answered none of the 40 most recent discussions. Version policy 5, Qwen2.5-Omni kept without notes. File health 5, contradictory licence and language metadata and an unanswered report about long audio.
Reach Granite 4.2 8B +40
Granite 4.2 8B 95
Memory 30, IBM's own 4-bit MLX build granite-4.2-8b-q4-mlx is 4.95 GB, and 4.95 GB × 1.2 = 5.9 GB, within 8 GB. Its Q4_0 GGUF is 5.06 GB and Ollama's library build granite4.2:8b (5.3 GB) fits 8 GB too. Runtimes 25, Released versions run the granite architecture in llama.cpp (granite, b12242), vLLM (GraniteForCausalLM in the v0.33.0 registry), SGLang (granite.py at v0.5.23), Transformers (v5.22.2) and Ollama (its own granite4.2 library entry, 8b tag). Builds 15, IBM publishes FP8, MXFP4 and NVFP4 safetensors builds, a GGUF repository with Q4_0 to Q8_0 and BF16 files, and 4-bit, 8-bit and BF16 MLX builds, so quantised builds at two precisions or more. Terms 15, Apache 2.0, an OSI-approved licence, with no use policy or other terms added on the card. Download 10, the Hub API reports gated false for the BF16, FP8, MXFP4, NVFP4, GGUF and MLX repositories, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps one inference provider (DeepInfra, live), short of the two the line needs. 30 + 25 + 15 + 15 + 10 + 0 = 95.
Qwen3-Omni-30B-A3B-Instruct 55
Memory earns 10, because the only build is the 70.5 GB BF16 checkpoint, about 85 GB with a fifth added, which fits only the reference machine's band. Runtimes earn 20. Transformers runs the whole model, and released vLLM, SGLang and llama.cpp run its Thinker with text out, while Ollama and LM Studio have nothing. Builds score 0, with no FP8, 4-bit or GGUF build from Alibaba. Apache 2.0 with nothing added gives terms the full 15 and the ungated repository gives download the full 10. Hosted too scores 0, since no provider serves this checkpoint and Alibaba's API serves a different Flash model.
Agent interface Granite 4.2 8B +25
Granite 4.2 8B 85
Chat template 20, chat_template.jinja in the repository, with roles, tools, tool responses, the thinking switch and low effort. Tool-calling format 25, the card has tool-calling sections for vLLM and SGLang, declares tools with OpenAI's function schema and names the parser to use, qwen3_coder, which both runtimes ship in the released versions read. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so it renders several calls in one turn, and the card names tool calling and agentic workflows as what it's best for. Structured output 0, the card doesn't document JSON or JSON Schema output or how to ask for it. Reasoning control 10, the card documents thinking on by default, non-thinking and low-effort modes per request through the template (enable_thinking and low_effort), with the reasoning returned apart from the answer. Context 10, 131,072 tokens natively per the card and config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.
Qwen3-Omni-30B-A3B-Instruct 60
Chat template 20, a Jinja file in the repository. Tool-calling format 10, the cookbook shows how tools are declared and calls come back and the card covers audio, image and video input and speech output, but no parser is named. Trained tool calling 20, two calls in one turn from speech in the cookbook. Structured output 5, speech in a chosen voice and text-only output are documented, with no JSON. Reasoning control 0, no thinking in this edition. Context 5, 65,536 tokens in the config and trained at 32,768.
Score by category
| Category | Weight this run | Granite 4.2 8B | Qwen3-Omni-30B-A3B-Instruct | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 95 | 55 | Granite 4.2 8B +40 |
| Risk | 20%20 | 55 | 55 | even |
| Agent interface | 10%10 | 85 | 60 | Granite 4.2 8B +25 |
| Documentation and upkeep | 10%10 | 92 | 26 | Granite 4.2 8B +66 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | E 41.8/100 |
Facts side by side
| Fact | Granite 4.2 8B | Qwen3-Omni-30B-A3B-Instruct |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | IBM | Alibaba (Qwen team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-20 | 2025-09-22 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 2/5 (4) |
Verdicts
Granite 4.2 8B
IBM's mid-size reasoning cousin, with the same strengths as the 3B. It runs in every runtime we count from IBM's own builds on a 16 GB machine, is signed, plain Apache 2.0, and comes with a disclosure of its training data. No safety results are published and structured output isn't documented.
Qwen3-Omni-30B-A3B-Instruct
An omni-modal cousin that listens, watches and reads, then answers in text or one of three voices, with tool calls from spoken requests, under plain Apache 2.0. Its only build, BF16, needs about 85 GB, and speech out needs Transformers or vLLM-Omni. The main risk is no answers from Qwen and no safety work for speech-driven actions.
Before you call either
Granite 4.2 8B
- Start it with
ollama run granite4.2:8b, orllama-server -hf ibm-granite/granite-4.2-8b-GGUF:Q4_K_M --jinja - With vLLM 0.32.0 or later leave out
--reasoning-parser-plugin, since the parser is built in, and use--tool-call-parser qwen3_coder - Thinking is on by default. Pass
enable_thinkingfalse to turn it off orlow_efforttrue for short reasoning - The context is 131,072 tokens. The card's 512K extension isn't documented
- Validate JSON output yourself, since structured output isn't documented
- IBM recommends Granite Guardian beside it to flag risky inputs and outputs
Qwen3-Omni-30B-A3B-Instruct
- For text answers,
mkdir -p media && vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --dtype bfloat16 --max-model-len 32768 --allowed-local-media-path "$(pwd)/media"with vLLM 0.11.1 or later serves the Thinker on an OpenAI-compatible API - The card's command passes
--allowed-local-media-path /, which lets anyone who can reach the server have it read any file on the machine through a file:// URL. Point it at a dedicated folder that holds only the media you mean to send, as above, and give vLLM an absolute path - Send audio as
audio_urland images asimage_urlcontent parts; vLLM serve doesn't take use_audio_in_video, so pass a video's audio separately - For speech out, load Qwen3OmniMoeForConditionalGeneration in Transformers 4.57.0 or later with qwen-omni-utils, pick speaker Ethan, Chelsie or Aiden, and write the returned waveform at 24 kHz
- Call model.disable_talker() or pass return_audio=False when you only need text, which saves about 10 GB and time
- Parse tool calls as JSON inside
<tool_call>tags yourself, since no parser is named, and confirm actions triggered by speech before running them - Keep use_audio_in_video the same across every step of a conversation, the card warns results go wrong otherwise
- Budget memory for video, the card's table rises from 78.85 GB for 15 seconds to 144.81 GB for 120 seconds in BF16
Questions
Which is better for AI agents, Granite 4.2 8B or Qwen3-Omni-30B-A3B-Instruct?
Granite 4.2 8B scores 61 (C) on agent readiness against Qwen3-Omni-30B-A3B-Instruct's 41.8 (E), and leads in 3 of 4 scored categories.
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- Qwen3-Omni-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-27B
- Qwen3-Omni-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3-Omni-30B-A3B-Instruct vs ThinkingCap (Qwen3.8 27B)
- Qwen3-Omni-30B-A3B-Instruct vs WeVisOne-VL-2B
- Qwen3-Omni-30B-A3B-Instruct vs WeVisOne-VL-4B
- Qwen3-Omni-30B-A3B-Instruct vs ZAYA1-8B
- Cohere Transcribe 03-2026 vs Qwen3-Omni-30B-A3B-Instruct
- Granite Speech 5.0 2B vs Qwen3-Omni-30B-A3B-Instruct
- Ming-3.0-asr-tiny vs Qwen3-Omni-30B-A3B-Instruct
- Ming-3.0-audio-flash vs Qwen3-Omni-30B-A3B-Instruct
- Ming-3.0-audio-tiny vs Qwen3-Omni-30B-A3B-Instruct
- Nemotron 3.5 ASR Streaming 0.6B vs Qwen3-Omni-30B-A3B-Instruct
- Nemotron 3.5 Transcribe 30B-A4B vs Qwen3-Omni-30B-A3B-Instruct
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3-Omni-30B-A3B-Instruct
- Parakeet TDT 0.6B v3 vs Qwen3-Omni-30B-A3B-Instruct
- Parakeet Unified EN 0.6B vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3-ASR-0.6B vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3-ASR-1.7B vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3-Omni-30B-A3B-Instruct vs Underdog Bark 3.0
- Qwen3-Omni-30B-A3B-Instruct vs Voxtral Mini 4B Realtime 2602
Machine-readable
- This page as Markdown
/compare/granite-4-2-8b-vs-qwen3-omni-30b-a3b-instruct.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/granite-4-2-8b.json·/api/v1/tools/qwen3-omni-30b-a3b-instruct.json - From a terminal
anchor compare granite-4-2-8b qwen3-omni-30b-a3b-instruct(the CLI) - Over MCP
compare_tools {"a": "granite-4-2-8b", "b": "qwen3-omni-30b-a3b-instruct"}at/mcp, no key