Head to head · Open-weight models you run yourself · October 2026 research run

Qwen3-Omni-30B-A3B-Instruct vs ZAYA1-8B

ZAYA1-8B scores 61 (C) on agent readiness against Qwen3-Omni-30B-A3B-Instruct's 41.8 (E), and leads in 2 of 4 scored categories. Both do open-weight models you run yourself.

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Which one, for what

Qwen3-Omni-30B-A3B-Instruct E

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

ZAYA1-8B C

Best for People who want a small, fast reasoning model for maths and code and can live without tool calling

Ahead on

  • Reach, 80 against 55
  • Documentation and upkeep, 50 against 26

Watch for

Tool calling needs a parser that only Zyphra's vLLM fork has

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 ZAYA1-8B +25

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.

ZAYA1-8B 80

Memory 30, the smallest build from Zyphra is the MXFP4-Experts repository, 5.85 GB, and 5.85 GB × 1.2 = 7.0 GB, within 16 GB. Runtimes 10, Released Transformers 5.22.2 has the zaya model and SGLang 0.5.23 has srt/models/zaya.py. Builds 15, Quantised builds from Zyphra at two precisions, FP8 and MXFP4 for the experts, each in its own repository. Terms 15, Apache 2.0 in the card's metadata, an OSI licence with nothing added. Download 10, the Hub API reports gated false for the model and its builds on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API. Zyphra's site lists a cloud product for its open models, which wasn't checked for this model. 30 + 10 + 15 + 15 + 10 + 0 = 80.

Documentation and upkeep ZAYA1-8B +24

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.

ZAYA1-8B 50

Model card 5, Intended use and the parameter counts are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the main repository last changed on 26 June 2026 (the checkpoint reshaped for Transformers), 137 days before this check, and the quantised repositories on 2 July, 131 days. Responsiveness 20, Of the 16 most recent discussions that aren't pull requests (7 May to 14 August 2026), 10 have a reply from a Zyphra member, which is most. Version policy 5, the original checkpoint is kept as ZAYA1-8B-legacy and the card says so at the top, without a date. File health 10, Config, template and card agree on the architecture after the reshape. 5 + 0 + 10 + 20 + 5 + 10 = 50.

Score by category

CategoryWeight this runQwen3-Omni-30B-A3B-InstructZAYA1-8BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%255580ZAYA1-8B +25
Risk20%205555even
Agent interface10%106060even
Documentation and upkeep10%102650ZAYA1-8B +24
Negative events≤1500
TotalE 41.8/100C 61/100

Facts side by side

FactQwen3-Omni-30B-A3B-InstructZAYA1-8B
KindOpen-weight modelOpen-weight model
VendorAlibaba (Qwen team)Zyphra
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0Apache-2.0
Read-only variant documentednono
llms.txtnono
Last release2025-09-222026-07-02
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviews2/5 (4)none

Verdicts

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.

ZAYA1-8B

A small reasoning model trained from scratch by one lab, with a clean licence, 4-bit and FP8 builds and a team that answers questions. It's the hardest of the small models to run: its own architecture, a card that points at forks, and tool calling that needs a parser no release has. Little on limits, data or safety.

Before you call either

Qwen3-Omni-30B-A3B-Instruct

  1. 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
  2. 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
  3. Send audio as audio_url and images as image_url content parts; vLLM serve doesn't take use_audio_in_video, so pass a video's audio separately
  4. 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
  5. Call model.disable_talker() or pass return_audio=False when you only need text, which saves about 10 GB and time
  6. Parse tool calls as JSON inside <tool_call> tags yourself, since no parser is named, and confirm actions triggered by speech before running them
  7. Keep use_audio_in_video the same across every step of a conversation, the card warns results go wrong otherwise
  8. 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

ZAYA1-8B

  1. Use it for reasoning, maths and code. Don't count on tool calling outside Zyphra's vLLM fork
  2. Run it with Transformers 5.22.2 or SGLang 0.5.23, which have the model built in
  3. Use temperature 1.0 and top_p 0.95 in general, and temperature 0.6 for agent and code work
  4. Download Zyphra/ZAYA1-8B, not the legacy repository, for the released runtimes
  5. The quantised repositories have no licence tag. The main one is Apache 2.0

Questions

Which is better for AI agents, Qwen3-Omni-30B-A3B-Instruct or ZAYA1-8B?

ZAYA1-8B scores 61 (C) on agent readiness against Qwen3-Omni-30B-A3B-Instruct's 41.8 (E), and leads in 2 of 4 scored categories.

Other comparisons with Qwen3-Omni-30B-A3B-Instruct or ZAYA1-8B

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