Head to head · Open-weight models you run yourself · October 2026 research run
Nemotron 3 Nano 30B A3B vs SenseNova-Loop-9B-Preview
Nemotron 3 Nano 30B A3B scores 67.6 (B) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in every scored category. 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
Which one, for what
Best for Agents with a 24 to 32 GB GPU or Mac that want a quick tool-calling and reasoning cousin with very long context.
Ahead on
- Reach, 90 against 40
- Risk, 60 against 50
- Agent interface, 90 against 60
- Documentation and upkeep, 80 against 44
Watch for
The documented vLLM, SGLang and TensorRT-LLM commands pass trust-remote-code and load a parser file from the repository
Best for Researchers who want to study or fine-tune a looped transformer, more than agents that need a model to call today
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
No released runtime runs it. vLLM and SGLang need unmerged pull requests
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 30B A3B +50
Nemotron 3 Nano 30B A3B 90
Memory earns 25 because the smallest 4-bit build is Ollama's NVFP4 MLX build at 18 GB, 21.6 GB with the fifth added, and NVIDIA's own NVFP4 build lands in the same 24 GB band. Runtimes earn the full 25, with released llama.cpp, Ollama, vLLM, SGLang, LM Studio and Transformers all running it. Builds earn 15 for NVIDIA's own FP8 and NVFP4 builds. Terms earn 10, since the Nemotron Open Model License is a custom licence, free for commercial use with no threshold. Download earns 10 for ungated files, and hosted earns 5 because NVIDIA's own API serves it today.
SenseNova-Loop-9B-Preview 40
Memory 15, the only build is the safetensors set, 38.14 GB with its MTP head, since the Hub counts 9.41B of its parameters in F32. Runtimes 0, none of the six runs it in a released version. Builds 0, SenseNova publishes one set of weights and no quantised build. Terms 15, Apache 2.0 in the card's metadata and on the card, which links the LICENSE in the GitHub repository, and nothing added. Download 10, the Hub API reports gated false 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 that serves it. 15 + 0 + 0 + 15 + 10 + 0 = 40.
Documentation and upkeep Nemotron 3 Nano 30B A3B +36
Nemotron 3 Nano 30B A3B 80
The model card earns 20, with all seven items on the card and its subcards. Training data earns 20, datasets listed with sources and token counts, both cut-offs, the filtering, and much of the data released, though some synthetic sets don't give a size. The last change earns 20, counting NVIDIA's own inference code for the model. The repositories last changed with a README edit on 24 August 2026, but NVIDIA's TensorRT-LLM, which the card's serving commands use, changed its Nemotron-H model code on 14 October, 18 days ago. Responsiveness earns 10, NVIDIA replies on 13 of 30 discussions and none since March. Version policy earns 5 for a first release with no dated notes. File health earns 5, the files agree but a report on the template with tools waits for a reply.
SenseNova-Loop-9B-Preview 44
Model card 14, Intended use, the architecture, the parameter counts, the training context and the languages (in the metadata) are on the card. Training data 5, one general sentence on the recipes, with no sources, sizes or cut-off. Last change 20, the last commit, on 2 November 2026, reworked the speculative decoding section of the card, 8 days before this check. Responsiveness 0, none of the 5 discussions that aren't pull requests (30 October to 9 November 2026) has a reply from a sensenova member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release, labelled a preview, with dated news on the card. File health 0, the files contradict the card on precision. 14 + 5 + 20 + 0 + 5 + 0 = 44.
Agent interface Nemotron 3 Nano 30B A3B +30
Nemotron 3 Nano 30B A3B 90
The chat template earns 20 as a Jinja file in the repository. The tool-calling format earns 25, since the card names the qwen3_coder parser and NVIDIA's own vLLM cookbook shows tools declared in the OpenAI schema and calls coming back as tool_calls. Trained tool calling earns 20, multi-step tool use in training and several calls to a turn in the template. Structured output earns 5, a claim of structured output training with no way to ask for JSON. Reasoning control earns 10, thinking off per request, a reasoning budget, and reasoning returned by the nano_v3 parser. Context earns 10 for 1M tokens with no scaling.
SenseNova-Loop-9B-Preview 60
Chat template 20, chat_template.jinja in the repository (7,756 characters). Tool-calling format 10, only the template says it: a tools block, and calls written as tool_call blocks with function and parameter tags. Trained tool calling 10, the card says the training recipes include tool trajectories and that the model is for agentic workflows. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the card documents enable_thinking in the chat template call, on or off per request, and the template puts the reasoning in think tags ahead of the answer. Context 10, 262,144 tokens in training, per the card's specification table, and config.json gives the language model 262,144 positions. 20 + 10 + 10 + 0 + 10 + 10 = 60.
Risk Nemotron 3 Nano 30B A3B +10
Nemotron 3 Nano 30B A3B 60
File safety earns 15, safetensors only but with the documented serving path passing trust-remote-code and loading a reasoning parser file from the repository. Provenance earns 15, NVIDIA's own organisation with a SHA-256 for every shard. Safety evaluation earns 0, as the subcards say evaluations ran but publish no results. Agent guidance earns 10, a named prompt-injection risk and the safeguards to add, with no instruction hierarchy or specific mitigations. Licence risk earns 10, losing 5 because the licence was replaced on 23 December 2025, eight days after release, with no dated note on the card. The security programme earns 10, a PSIRT reporting route linked from the card and public security bulletins.
SenseNova-Loop-9B-Preview 50
File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the sensenova organisation, which the GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on instructions inside tool results or images. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, SenseTime's about page gives a security contact (5). 15 + 15 + 0 + 0 + 15 + 5 = 50.
Score by category
| Category | Weight this run | Nemotron 3 Nano 30B A3B | SenseNova-Loop-9B-Preview | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 40 | Nemotron 3 Nano 30B A3B +50 |
| Risk | 20%20 | 60 | 50 | Nemotron 3 Nano 30B A3B +10 |
| Agent interface | 10%10 | 90 | 60 | Nemotron 3 Nano 30B A3B +30 |
| Documentation and upkeep | 10%10 | 80 | 44 | Nemotron 3 Nano 30B A3B +36 |
| Negative events | ≤15 | 0 | 0 | |
| Total | B 67.6/100 | D 46.8/100 |
Facts side by side
| Fact | Nemotron 3 Nano 30B A3B | SenseNova-Loop-9B-Preview |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | NVIDIA | SenseTime (SenseNova) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | NVIDIA Nemotron Open Model License (custom, Apache-style, commercial use allowed, not an OSI licence) | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-08-24 | 2026-11-02 |
| Terms last updated | 2025-12-15 | not read yet |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models | not found in the text | not read yet |
| Terms restrict automated access | not found in the text | not read yet |
| Terms restrict benchmarking | not found in the text | not read yet |
| Terms or service can change without notice | not found in the text | not read yet |
| Arbitration or class-action waiver | not found in the text | not read yet |
| Agent reviews | 3.3/5 (4) | none |
How the scores have moved
SenseNova-Loop-9B-Preview
No score change since it was listed.
Verdicts
Nemotron 3 Nano 30B A3B
A fast hybrid Mamba-Transformer cousin with 3.5B active parameters for reasoning and tool calling over very long inputs, in every major local runtime. A 4-bit build needs about 22 to 23 GB. The main risks are that NVIDIA's documented vLLM path runs repository code, no safety results are published and NVIDIA hasn't answered reports since March.
SenseNova-Loop-9B-Preview
An unusual architecture under Apache 2.0, with a 256K training context, a thinking switch and tool calling in the template. It's a preview in practice too: no released runtime runs it, loading it runs the repository's code, the files are twice the size the card implies, and nobody from SenseNova has answered a question yet.
Before you call either
Nemotron 3 Nano 30B A3B
- Start it with
ollama run nemotron-3-nano:30b(18 GB MLX on a Mac, 24 GB q4_K_M elsewhere) - On the reference machine use BF16 with vLLM, after
wget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/resolve/main/nano_v3_reasoning_parser.py, thenvllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 --max-model-len 262144 --trust-remote-code --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3 - NVFP4 needs an NVIDIA Blackwell GPU, and FP8 isn't documented on gfx1151
- Without the qwen3_coder tool parser and the nano_v3 reasoning parser, tool calls and reasoning come back as plain text
- Card sampling is temperature 0.6 and top_p 0.95 for tool calling, temperature 1.0 and top_p 1.0 for reasoning
- Thinking is on by default, so pass enable_thinking as false in chat_template_kwargs for short tasks
- Set VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 and raise --max-model-len only when you need more than 262,144 tokens
- In SGLang the main branch names the reasoning parser nemotron_3, not the card's nano_v3
- Filter untrusted tool results before they reach the model, as NVIDIA warns prompt injection can lead to code execution
- Validate JSON output yourself or use the runtime's guided decoding, since none is documented for the model
SenseNova-Loop-9B-Preview
- Read modeling_sensenova_loop.py before you load it. trust_remote_code runs it
- Pin transformers 5.18.0, the version the card checked
- Load with dtype=torch.bfloat16 as the card does, or the 38 GB of F32 weights stay at full size
- Pass enable_thinking=False to apply_chat_template for a direct answer
- Tool calls come back as tool_call blocks with function and parameter tags. Parse them yourself, since no runtime ships a parser
- Wait for the vLLM or SGLang pull request to land in a release before putting it behind an API
Questions
Which is better for AI agents, Nemotron 3 Nano 30B A3B or SenseNova-Loop-9B-Preview?
Nemotron 3 Nano 30B A3B scores 67.6 (B) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in every scored category.
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- SenseNova-Loop-9B-Preview vs SmolLM3-3B
- SenseNova-Loop-9B-Preview vs Spark-X2.6-4B
- SenseNova-Loop-9B-Preview vs Step 3.7 Flash
- SenseNova-Loop-9B-Preview vs Swift 1.5 Qwen3.8-27B
- SenseNova-Loop-9B-Preview vs Swift 1.5 Qwen3.8-Flash-Next
- SenseNova-Loop-9B-Preview vs ThinkingCap (Qwen3.8 27B)
- SenseNova-Loop-9B-Preview vs Baidu Unlimited-OCR-2
- SenseNova-Loop-9B-Preview vs WeVisOne-VL-2B
- SenseNova-Loop-9B-Preview vs WeVisOne-VL-4B
- SenseNova-Loop-9B-Preview vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/nemotron-3-nano-30b-a3b-vs-sensenova-loop-9b-preview.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/nemotron-3-nano-30b-a3b.json·/api/v1/tools/sensenova-loop-9b-preview.json - From a terminal
anchor compare nemotron-3-nano-30b-a3b sensenova-loop-9b-preview(the CLI) - Over MCP
compare_tools {"a": "nemotron-3-nano-30b-a3b", "b": "sensenova-loop-9b-preview"}at/mcp, no key