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

Inkling 2 Small vs Qwen3-Omni-30B-A3B-Instruct

Inkling 2 Small scores 58.8 (C) on agent readiness against Qwen3-Omni-30B-A3B-Instruct's 46.8 (D), and leads in 3 of 4 scored categories. Qwen3-Omni-30B-A3B-Instruct leads on reach. Both do open-weight models you run yourself.

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Which one, for what

Inkling 2 Small C

Best for Teams with 512 GB or more of datacentre GPU memory who want Thinking Machines' newest open multimodal agent model and can run a runtime's pre-release image.

Ahead on

  • Risk, 65 against 55
  • Agent interface, 85 against 60
  • Documentation and upkeep, 67 against 26

Watch for

The smaller checkpoint needs about 472 GB, and Thinking Machines says 500 GB of GPU memory

Qwen3-Omni-30B-A3B-Instruct D

Best for Agents that need one local model to hear, see and answer, in text on about 24 GB with llama.cpp's GGUF or by voice on about 96 GB with Transformers

Ahead on

  • Reach, 75 against 40

Watch for

No quantised build from Alibaba; its only build is 70.5 GB of BF16, about 85 GB to run

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 Inkling 2 Small +41

Inkling 2 Small 67

Model card 17 of 20, everything but a dated knowledge cut-off. Training data 15, an EU training-content summary with sources, mix, an October 2026 collection cut-off and filtering. Last change 20, the files were uploaded on 12 November 2026. Responsiveness 0, the repository has no questions yet, which reads as none answered. Version policy 5, a first release. File health 10, the files agree and no broken-file report waits. 17 + 15 + 20 + 0 + 5 + 10 = 67.

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, the files last changed on 22 September 2025 and the GitHub code since only in its README. Responsiveness 0, no Qwen reply in any of the 45 threads. Version policy 5, Qwen2.5-Omni kept without notes. File health 5, contradictory licence and language metadata and an unanswered report about long audio. 11 + 5 + 0 + 0 + 5 + 5 = 26.

Reach Qwen3-Omni-30B-A3B-Instruct +35

Inkling 2 Small 40

Memory 0 of 30, the smallest build is Thinking Machines' NVFP4 checkpoint at 393.5 GB, about 472 GB with a fifth added, above the 256 GB line. Runtimes 0, no released version of the six runs it; vLLM and SGLang serve it from their own images and Transformers has it on its main branch only. Builds 15, two precisions from the publisher (NVFP4 and MXFP8). Terms 10, Apache 2.0 with a binding acceptable use policy on top, read as a custom licence. Download 10, no gate. Hosted 5, Thinking Machines' own Tinker serves it as beta serverless inference, and Together is live on Hugging Face. 0 + 0 + 15 + 10 + 10 + 5 = 40.

Qwen3-Omni-30B-A3B-Instruct 75

Memory earns 25, because llama.cpp's own ggml-org organisation publishes a Q4_K_M GGUF of 18.56 GB, which with its 1.33 GB projector is 23.9 GB with a fifth added, within 24 GB; the 21 October check missed it. Runtimes earn 25. Transformers runs the whole model, and released vLLM, SGLang and llama.cpp (b12177) run its Thinker with text out, and Ollama v0.40.16 runs it through its pinned llama.cpp b12177. 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. 25 + 25 + 0 + 15 + 10 + 0 = 75.

Agent interface Inkling 2 Small +25

Inkling 2 Small 85

Chat template 20, a Jinja file in the repository. Tool-calling format 25, Thinking Machines' tml-renderers guide shows how tools are declared and how calls come back for this model, and its package ships the parser. Trained tool calling 20, several calls in one turn. Structured output 0, not documented. Reasoning control 10, thinking effort is set per request from none to xhigh, and the reasoning comes back in reasoning_content. Context 10, up to 1M tokens. 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. 20 + 10 + 20 + 5 + 0 + 5 = 60.

Risk Inkling 2 Small +10

Inkling 2 Small 65

File safety 25, safetensors only and no repository code to run. Provenance 15, the files are in the organisation Thinking Machines links to, with a SHA-256 on every LFS file. Safety evaluation 10, Thinking Machines' own StrongREJECT, FORTRESS, HoneyBench and DistressBench results and described outside red-teaming, but nothing an outside party published or a harness to rerun. Agent guidance 5, the card names safeguards to add but doesn't say what its refusals cover, with nothing on prompt injection. Licence risk 5, 10 off for a use policy that reaches beyond the law and that can change at any time. Security programme 5, a security.txt and a disclosure policy, with public advisories unchecked and scored as absent. 25 + 15 + 10 + 5 + 5 + 5 = 65.

Qwen3-Omni-30B-A3B-Instruct 55

File safety 25, safetensors only and no code in the repository, so the cookbook's trust_remote_code flag runs nothing. Provenance 15, files in the Qwen organisation with a checksum on every file. Safety evaluation 0 and agent guidance 0, nothing published, though spoken input can trigger tool calls. Licence risk 15, plain Apache 2.0. Security programme 0, no security contact, disclosure policy or advisories for Qwen. 25 + 15 + 0 + 0 + 15 + 0 = 55.

Score by category

CategoryWeight this runInkling 2 SmallQwen3-Omni-30B-A3B-InstructEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%254075Qwen3-Omni-30B-A3B-Instruct +35
Risk20%206555Inkling 2 Small +10
Agent interface10%108560Inkling 2 Small +25
Documentation and upkeep10%106726Inkling 2 Small +41
Negative events≤1500
TotalC 58.8/100D 46.8/100

Facts side by side

FactInkling 2 SmallQwen3-Omni-30B-A3B-Instruct
KindOpen-weight modelOpen-weight model
VendorThinking Machines LabAlibaba (Qwen team)
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0Apache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-11-122025-09-22
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviewsnone2/5 (4)

How the scores have moved

Verdicts

Inkling 2 Small

Thinking Machines' newest open model, a 594B mixture-of-experts generalist with images in, several tool calls a turn and per-request thinking effort. The NVFP4 checkpoint needs about 472 GB, and no released runtime runs it yet, so today that means vLLM's or SGLang's own images, or Tinker and Together hosted. The use policy binds on download and can change.

Qwen3-Omni-30B-A3B-Instruct

Alibaba's omni-modal model takes text, images, audio and video and answers in text or one of three voices, with tool calls from spoken requests, under plain Apache 2.0. llama.cpp's own 4-bit GGUF needs about 24 GB for text answers; speech output needs Transformers and the 70.5 GB BF16 files. Qwen has answered none of 45 discussions.

Before you call either

Inkling 2 Small

  1. No released runtime runs it yet. Serve the NVFP4 checkpoint from SGLang's dev-inkling-2-small image with the cookbook's recipe for your hardware, or from vLLM's vllm/vllm-openai:inkling-2-small image
  2. In vLLM pass --reasoning-parser inkling --enable-auto-tool-choice --tool-call-parser inkling. In SGLang the tool-call parser is inkling2 and the reasoning parser inkling
  3. Transformers' main branch loads only thinkingmachines/Inkling-2-Small-MXFP8, the 622.8 GB checkpoint, and no release has the code yet
  4. Pass reasoning_effort as none, minimal, low, medium, high (the default), xhigh or max, or a number from 0 to 0.99
  5. Effort none conditions the model toward no reasoning but doesn't forbid it, and higher effort needs a larger max_tokens
  6. Tool call arguments must reach the template as an object, not a JSON string, or it raises an error
  7. Images should be 40 to 4096 px a side. The model takes no audio, and the template raises an error for an audio part
  8. Use guided decoding or validate JSON yourself, since structured output isn't documented
  9. Without the hardware, call it on Tinker's OpenAI-compatible endpoint with a Tinker key, at $0.30 input and $1.00 output per million tokens as read on 6 November 2026
  10. Read the Model Acceptable Use Policy before deploying, it binds on download and bars some uses that are legal

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. On a smaller machine, run ggml-org's Q4_K_M GGUF with its projector in llama.cpp b12177 or later, or Ollama v0.40.16, for text answers to audio, image and text input
  3. 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
  4. 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
  5. 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
  6. Call model.disable_talker() or pass return_audio=False when you only need text, which saves about 10 GB and time
  7. Parse tool calls as JSON inside <tool_call> tags yourself, since no parser is named, and confirm actions triggered by speech before running them
  8. Keep use_audio_in_video the same across every step of a conversation, the card warns results go wrong otherwise
  9. 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, Inkling 2 Small or Qwen3-Omni-30B-A3B-Instruct?

Inkling 2 Small scores 58.8 (C) on agent readiness against Qwen3-Omni-30B-A3B-Instruct's 46.8 (D), and leads in 3 of 4 scored categories. Qwen3-Omni-30B-A3B-Instruct leads on reach.

Other comparisons with Inkling 2 Small or Qwen3-Omni-30B-A3B-Instruct

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