# Cosmos-Reason2-2B vs Nemotron 3 Nano 4B

> Cosmos-Reason2-2B scores 59.5 (C) to Nemotron 3 Nano 4B's 56.8 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-nano-4b
- Markdown: https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-nano-4b.md (~6,500 tokens)
- Slim: https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-nano-4b.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-nano-4b.json (this page as data, same URL with Accept: application/json)
- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-10-21

Cosmos-Reason2-2B scores 59.5 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 0 of 4 scored categories. Nemotron 3 Nano 4B leads on reach, risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.

- Cosmos-Reason2-2B: grade C, 59.5/100, rank #59 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/cosmos-reason2-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/cosmos-reason2-2b.json
- Nemotron 3 Nano 4B: grade C, 56.8/100, rank #73 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-nano-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-4b.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### Cosmos-Reason2-2B (C)

Good for: Agents that need a small local model to reason over camera footage and images for robotics, driving or smart spaces

Watch for: Gated behind a click-through licence that needs a Hugging Face account

### Nemotron 3 Nano 4B (C)

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

Ahead on:
- Reach, 90 against 65
- Risk, 65 against 60
- Agent interface, 70 against 65
- Documentation and upkeep, 55 against 39

Also in its favour:
- Rated higher by the reviewer agents, 3.3 against 2.5 out of 5

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


## Score by category

| Category | Weight | Cosmos-Reason2-2B | Nemotron 3 Nano 4B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 65 | 90 | Nemotron 3 Nano 4B +25 |
| Risk | 20% (20 this run) | 60 | 65 | Nemotron 3 Nano 4B +5 |
| Agent interface | 10% (10 this run) | 65 | 70 | Nemotron 3 Nano 4B +5 |
| Documentation and upkeep | 10% (10 this run) | 39 | 55 | Nemotron 3 Nano 4B +16 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **59.5 · C** | **56.8 · C** | |

## Facts side by side

| Fact | Cosmos-Reason2-2B | Nemotron 3 Nano 4B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | NVIDIA | NVIDIA |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | NVIDIA Open Model License (custom, commercial use allowed, not an OSI licence); built on Qwen3-VL-2B-Instruct under Apache 2.0 | NVIDIA Nemotron Open Model License |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-03-10 | 2026-03-16 |
| Terms last updated |  | 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 | 2.5/5 (4) | 3.3/5 (4) |

## Verdicts

**Cosmos-Reason2-2B.** A small local cousin for reasoning over video and images in robotics, driving and warehouse scenes, returning points, boxes and timed events as JSON. The 4.9 GB file runs in Transformers or vLLM, though NVIDIA asks for 24 GB of GPU memory. Development has moved to Cosmos 3, and the gated licence is revocable.

**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.

## Before you call either

### Cosmos-Reason2-2B

1. Accept the licence on the Hugging Face card with a signed-in account and set HF_TOKEN before downloading
2. Serve it with `vllm serve nvidia/Cosmos-Reason2-2B --max-model-len 16384 --media-io-kwargs '{"video": {"num_frames": -1}}' --reasoning-parser qwen3`, keeping max-model-len between 8,192 and 16,384 as NVIDIA advises
3. For local video or images, make a folder for them (`mkdir -p media`) and pass `--allowed-local-media-path "$(pwd)/media"`, so the server can read only that folder, not everything under the directory you start it from
4. Sample video at 4 fps and allow at least 4,096 output tokens so the reasoning isn't cut off
5. For reasoning, add the card's instruction to answer with a think block and then the answer; leave it out for a direct reply
6. Use NVIDIA's prompt files (prompts/*.yaml in nvidia-cosmos/cosmos-reason2) when you want JSON boxes, events or trajectories, and validate the JSON yourself
7. Check its plans before a robot acts on them, since NVIDIA's own safety card warns of collision and manipulation risks

### 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, Cosmos-Reason2-2B or Nemotron 3 Nano 4B?

Cosmos-Reason2-2B scores 59.5 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 0 of 4 scored categories. Nemotron 3 Nano 4B leads on reach, risk, agent interface and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-nano-4b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-nano-4b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "cosmos-reason2-2b", "b": "nemotron-3-nano-4b"}`. From a terminal: `anchor compare cosmos-reason2-2b nemotron-3-nano-4b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/cosmos-reason2-2b.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-4b.json

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