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

Inkling 2 Small vs Nemotron 3 Nano 4B

Inkling 2 Small scores 58.8 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 2 of 4 scored categories. Nemotron 3 Nano 4B leads on reach. Both do open-weight models you run yourself.

Best open-weight models you download and run yourself for AI agents · All 6472 open weights comparisons

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

  • Agent interface, 85 against 70
  • Documentation and upkeep, 67 against 55

Watch for

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

Nemotron 3 Nano 4B C

Best for Agents on laptops, edge boards and small GPUs that need a cheap local tool-calling helper in English

Ahead on

  • Reach, 90 against 40

Watch for

The card's Transformers and vLLM commands pass trust_remote_code and load a parser plugin from the repository

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 4B +50

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 4B 90

Memory earns the full 30, since NVIDIA's own 2.84 GB GGUF needs under 4 GB with the fifth added. Runtimes earn the full 25 from released llama.cpp, vLLM, SGLang, Transformers, Ollama and LM Studio. Builds earn 15 for NVIDIA's FP8 and GGUF. Terms earn 10, since the Nemotron licence is custom even though it's free for commercial use with no threshold or use policy. Download earns 10, with no gate or account. Hosted earns 0, as no Hugging Face provider serves it and no NVIDIA hosting was found.

Agent interface Inkling 2 Small +15

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 4B 70

The chat template earns 20, a Jinja file in the repository. The tool-calling format earns 10 of 25, as only the template shows the format and the parser name appears only in a serving command. Trained tool calling earns 20, with tool-use benchmarks and data and several calls per turn in the template. Structured output earns 0, not claimed or documented. Reasoning control earns 10, thinking off per request with a parser that returns the reasoning apart. Context earns 10 for 262,144 tokens.

Documentation and upkeep Inkling 2 Small +12

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.

Nemotron 3 Nano 4B 55

The model card earns 20, all seven items with limitations in the linked subcard. Training data earns 20, datasets listed with sources and dates, crawled sets with token counts, a cut-off and published NVIDIA datasets, though most synthetic sizes are undisclosed. Last change earns 5, as the files last changed on 20 March 2026, and TensorRT-LLM's Nemotron-H commits since 14 October touch MoE, NVFP4 and Puzzle paths this dense model doesn't use, in release candidates or no release. Responsiveness earns 0, with no NVIDIA reply to any of six questions, re-counted on 4 November. Version policy earns 5 as a first release. File health earns 5, since the LICENSE file is empty, reports and fixes wait without a reply, and the card's Transformers snippet names a repository that isn't public.

Score by category

CategoryWeight this runInkling 2 SmallNemotron 3 Nano 4BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%254090Nemotron 3 Nano 4B +50
Risk20%206565even
Agent interface10%108570Inkling 2 Small +15
Documentation and upkeep10%106755Inkling 2 Small +12
Negative events≤1500
TotalC 58.8/100C 56.8/100

Facts side by side

FactInkling 2 SmallNemotron 3 Nano 4B
KindOpen-weight modelOpen-weight model
VendorThinking Machines LabNVIDIA
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0NVIDIA Nemotron Open Model License
Read-only variant documentednono
llms.txtnono
Last release2026-11-122026-03-16
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviewsnone3.3/5 (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 4B

A 3.97-billion-parameter reasoning model with trained tool calls, a thinking switch and 262,144 tokens of context, which runs in about 4 GB from NVIDIA's own GGUF. The main risks are serving commands that run repository code, no published safety results, a tool format left to the template, and files unchanged and questions unanswered since March 2026.

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 4B

  1. Quickest start is ollama run nemotron-3-nano:4b, a 2.8 GB build
  2. With llama.cpp use llama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja, NVIDIA's own Q4_K_M file
  3. With vLLM, download nano_v3_reasoning_parser.py from the repository first and serve with --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3
  4. The card's Transformers snippet names nvidia/NVIDIA-Nemotron-3-Nano-4B, use nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 instead
  5. Thinking is on by default. Pass enable_thinking=False to the chat template for short tasks
  6. Use temperature 1.0 and top_p 0.95 for reasoning and temperature 0.6 with top_p 0.95 for tool calling, as the card says
  7. It's tuned for English only, and validate JSON yourself, since structured output isn't documented

Questions

Which is better for AI agents, Inkling 2 Small or Nemotron 3 Nano 4B?

Inkling 2 Small scores 58.8 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 2 of 4 scored categories. Nemotron 3 Nano 4B leads on reach.

Other comparisons with Inkling 2 Small or Nemotron 3 Nano 4B

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