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

Inkling 2 Small vs Nemotron 3 Nano Omni 30B A3B Reasoning

Nemotron 3 Nano Omni 30B A3B Reasoning scores 61.2 (C) on agent readiness against Inkling 2 Small's 58.8 (C), and leads in 3 of 4 scored categories. Inkling 2 Small leads on risk. 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 60

Watch for

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

Nemotron 3 Nano Omni 30B A3B Reasoning C

Best for Agents with a 32 to 48 GB NVIDIA GPU that need one local model to read video, audio, documents and screens and call tools

Ahead on

  • Reach, 85 against 40
  • Agent interface, 90 against 85

Watch for

Loads with trust_remote_code in every documented runtime command

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 Nemotron 3 Nano Omni 30B A3B Reasoning +45

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.

Nemotron 3 Nano Omni 30B A3B Reasoning 85

Memory earns 20 because the smallest 4-bit builds, Ollama's NVFP4 at 21 GB and NVIDIA's NVFP4 at 22.4 GB, need about 25 to 27 GB with the fifth added, which fits 32 GB but not 24. Runtimes reach the cap of 25 with released code in vLLM, SGLang and llama.cpp and library entries in Ollama and LM Studio. NVIDIA's FP8 and NVFP4 builds give builds the full 15. The NVIDIA Open Model Agreement is a custom licence with free commercial use, so terms get 10. The ungated repositories give download the full 10, and NVIDIA's own API serving it gives hosted too its 5.

Risk Inkling 2 Small +5

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.

Nemotron 3 Nano Omni 30B A3B Reasoning 60

File safety gets 15, safetensors only but loaded with trust_remote_code. Provenance gets 15 for files in NVIDIA's own organisation with a SHA-256 for every shard. The agreement has no use policy, threshold or change clause and the grant is irrevocable, so licence risk keeps 15. NVIDIA's PSIRT form on the card and its public bulletins give the security programme 10. Agent guidance gets 5 for general cautions, and safety evaluation scores 0 because the subcards name the safety tests without publishing results.

Agent interface Nemotron 3 Nano Omni 30B A3B Reasoning +5

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.

Nemotron 3 Nano Omni 30B A3B Reasoning 90

A Jinja template ships in the repository (20). The card names the qwen3_coder parser and NVIDIA's cookbooks show tools declared with the OpenAI schema and tool_calls read back (25). The template renders several calls per turn and the card says it supports tool calling (20). JSON output is claimed with no word on how to ask for it (5). Thinking turns off per request, a budget caps it and the parser returns the reasoning apart (10). The context is 262,144 tokens, trained in (10).

Score by category

CategoryWeight this runInkling 2 SmallNemotron 3 Nano Omni 30B A3B ReasoningEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%254085Nemotron 3 Nano Omni 30B A3B Reasoning +45
Risk20%206560Inkling 2 Small +5
Agent interface10%108590Nemotron 3 Nano Omni 30B A3B Reasoning +5
Documentation and upkeep10%106770Nemotron 3 Nano Omni 30B A3B Reasoning +3
Negative events≤1500
TotalC 58.8/100C 61.2/100

Facts side by side

FactInkling 2 SmallNemotron 3 Nano Omni 30B A3B Reasoning
KindOpen-weight modelOpen-weight model
VendorThinking Machines LabNVIDIA
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0NVIDIA Open Model Agreement (custom, commercial use allowed, not an OSI licence)
Read-only variant documentednono
llms.txtnono
Last release2026-11-122026-08-24
Terms last updatedno document linkedno date given
Privacy policy last updatedno document linkedno document linked
Customer content may train modelsnot read yetnot found in the text
Terms restrict automated accessnot read yetnot found in the text
Terms restrict benchmarkingnot read yetnot found in the text
Terms or service can change without noticenot read yetnot found in the text
Arbitration or class-action waivernot read yetnot found in the text
Agent reviewsnone3/5 (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.

Nemotron 3 Nano Omni 30B A3B Reasoning

A local multimodal model for video with speech, long documents, charts, screens and English transcription, with tool calling, a reasoning switch and a 262,144-token context. It needs about 27 GB for NVIDIA's NVFP4 build on Blackwell, or 42 GB for FP8. The main risks are the remote code it loads and a repository where nobody answers questions.

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

Nemotron 3 Nano Omni 30B A3B Reasoning

  1. Start it with vllm serve nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8 --max-model-len 131072 --trust-remote-code --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder --kv-cache-dtype fp8 on vLLM 0.20.0 or later, after pip install "vllm[audio]" if any audio will be sent
  2. Or run ollama run nemotron3; the 33b-nvfp4 and 33b-mlx tags are MLX builds for Apple silicon, and the q4_K_M tag is 28 GB
  3. Set --media-io-kwargs '{"video": {"fps": 2, "num_frames": 256}}' at launch, since vLLM otherwise samples about 32 frames per video
  4. Send chat_template_kwargs: {"enable_thinking": false} for transcription and quick answers, and use temperature 1.0 with top_k 1 for ASR as the card recommends
  5. Render PDF pages to PNG and send them as images, since the API takes no raw PDF
  6. Treat transcripts and text read from images or video as untrusted input before acting on them

Questions

Which is better for AI agents, Inkling 2 Small or Nemotron 3 Nano Omni 30B A3B Reasoning?

Nemotron 3 Nano Omni 30B A3B Reasoning scores 61.2 (C) on agent readiness against Inkling 2 Small's 58.8 (C), and leads in 3 of 4 scored categories. Inkling 2 Small leads on risk.

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