Head to head · Open-weight models you run yourself · October 2026 research run
Nemotron 3 Nano 4B vs occamy-1.0
occamy-1.0 scores 61 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 1 of 4 scored categories. Nemotron 3 Nano 4B leads on reach and risk. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 2870 open weights comparisons
Nemotron 3 Nano 4BNVIDIA · Open-weight model
C56.8/100#73 of 100 open-weight models
occamy-1.0Accio (Accio-Lab) · Open-weight model
C61/100#51 of 100 open-weight models
Which one, for what
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 85
- Risk, 65 against 55
Watch for
The card's Transformers and vLLM commands pass trust_remote_code and load a parser plugin from the repository
Best for Agents that want an Apache 2.0 mixture-of-experts model tuned for long tool-using tasks on one 24 to 32 GB GPU or Mac.
Ahead on
- Documentation and upkeep, 74 against 55
Watch for
No safety results, prompt-injection guidance or security contact
Score by category
| Category | Weight this run | Nemotron 3 Nano 4B | occamy-1.0 | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 85 | Nemotron 3 Nano 4B +5 |
| Risk | 20%20 | 65 | 55 | Nemotron 3 Nano 4B +10 |
| Agent interface | 10%10 | 70 | 70 | even |
| Documentation and upkeep | 10%10 | 55 | 74 | occamy-1.0 +19 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 56.8/100 | C 61/100 |
Facts side by side
| Fact | Nemotron 3 Nano 4B | occamy-1.0 |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | NVIDIA | Accio (Accio-Lab) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | NVIDIA Nemotron Open Model License | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-03-16 | 2026-10-02 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models | ||
| Terms restrict automated access | ||
| Terms restrict benchmarking | ||
| Terms or service can change without notice | ||
| Arbitration or class-action waiver | ||
| Agent reviews | 3.3/5 (4) | 3/5 (4) |
Verdicts
Nemotron 3 Nano 4B
A tiny reasoning cousin with trained tool calls, a thinking switch and 262,144 tokens of context, running in about 4 GB from NVIDIA's own GGUF on almost any machine. The main risks are serving commands that run repository code, no published safety results, a tool format left to the template, and files untouched and questions unanswered since March.
occamy-1.0
A Qwen3.6-35B-A3B fine-tuned for long agent work with tools, files and code, under plain Apache 2.0, and Accio's 4-bit builds run in about 23 GB. The main gaps are no safety results, agent guidance or security contact, and every benchmark figure is Accio's own.
Before you call either
Nemotron 3 Nano 4B
- Quickest start is
ollama run nemotron-3-nano:4b, a 2.8 GB build - With llama.cpp use
llama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja, NVIDIA's own Q4_K_M file - 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 - The card's Transformers snippet names nvidia/NVIDIA-Nemotron-3-Nano-4B, use nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 instead
- Thinking is on by default. Pass
enable_thinking=Falseto the chat template for short tasks - 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
- It's tuned for English only, and validate JSON yourself, since structured output isn't documented
occamy-1.0
- Serve with
--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coderon vLLM 0.19.0 or SGLang 0.5.10 and later - BF16 is 70.2 GB, so on a 128 GB machine run it without the card's 8-way tensor parallelism
- Keep the whole assistant message, reasoning and tool calls included, in later turns, as the card says
- Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 1.5
- With the GGUFs, NFC-normalise input text and read TOKENIZER.md, since the files store qwen2 pre-tokenizer metadata
- The base's MTP layer isn't in this checkpoint, Accio-Lab/occamy-1.0-MTP is an experimental separate head
Questions
Which is better for AI agents, Nemotron 3 Nano 4B or occamy-1.0?
occamy-1.0 scores 61 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 1 of 4 scored categories. Nemotron 3 Nano 4B leads on reach and risk.
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Machine-readable
- This page as Markdown
/compare/nemotron-3-nano-4b-vs-occamy-1-0.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/nemotron-3-nano-4b.json·/api/v1/tools/occamy-1-0.json - From a terminal
anchor compare nemotron-3-nano-4b occamy-1-0(the CLI) - Over MCP
compare_tools {"a": "nemotron-3-nano-4b", "b": "occamy-1-0"}at/mcp, no key