# Cosmos-Reason2-2B vs Nemotron 3 Super 120B A12B

> Nemotron 3 Super 120B A12B scores 66.2 (B) to Cosmos-Reason2-2B's 59.5 (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-super-120b-a12b
- Markdown: https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-super-120b-a12b.md (~6,800 tokens)
- Slim: https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-super-120b-a12b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-super-120b-a12b.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

Nemotron 3 Super 120B A12B scores 66.2 (B) on agent readiness against Cosmos-Reason2-2B's 59.5 (C), and leads in every scored category. 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 Super 120B A12B: grade B, 66.2/100, rank #15 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-super-120b-a12b.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 Super 120B A12B (B)

Good for: Agents on a 128 GB unified machine or Blackwell GPUs that want a long-context, tool-calling reasoning model with documented training data

Ahead on:
- Reach, 75 against 65
- Risk, 65 against 60
- Agent interface, 90 against 65
- Documentation and upkeep, 65 against 39

Watch for: The card's serving commands pass --trust-remote-code


## Score by category

| Category | Weight | Cosmos-Reason2-2B | Nemotron 3 Super 120B A12B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 65 | 75 | Nemotron 3 Super 120B A12B +10 |
| Risk | 20% (20 this run) | 60 | 65 | Nemotron 3 Super 120B A12B +5 |
| Agent interface | 10% (10 this run) | 65 | 90 | Nemotron 3 Super 120B A12B +25 |
| Documentation and upkeep | 10% (10 this run) | 39 | 65 | Nemotron 3 Super 120B A12B +26 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **59.5 · C** | **66.2 · B** | |

## Facts side by side

| Fact | Cosmos-Reason2-2B | Nemotron 3 Super 120B A12B |
| --- | --- | --- |
| 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 | 2026-03-11 |
| 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) | 2.8/5 (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 Super 120B A12B.** A long-context agent cousin with trained parallel tool calls, a reasoning switch and a full account of its training data, under a free commercial licence. It needs about 100 GB of GPU memory, a 128 GB unified machine or Blackwell hardware for NVIDIA's NVFP4. The main risks are serving commands that run repository code and no published safety results.

## 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 Super 120B A12B

1. On the reference machine start it with `ollama run nemotron-3-super`, the 87 GB q4_K_M build, the only one that fits (about 104 GB with the margin)
2. Raise the Strix Halo GPU memory limit first with `amd-ttm --set 100`, since the default of about 62.8 GiB is far below what it needs
3. With vLLM serve it with `--reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` (SGLang uses `--reasoning-parser nemotron_3`)
4. Use `temperature=1.0` and `top_p=0.95` for every task, as the card says
5. Thinking is on by default. Pass `enable_thinking: false` or `low_effort: true` in `chat_template_kwargs` for short tasks
6. For coding agents pass `force_nonempty_content: true` in `chat_template_kwargs`, so a cut-off reasoning trace still returns content
7. Raise context past 262,144 only with the card's flags (`VLLM_ALLOW_LONG_MAX_MODEL_LEN=1`, `--max-model-len 1048576`)

## Questions

### Which is better for AI agents, Cosmos-Reason2-2B or Nemotron 3 Super 120B A12B?

Nemotron 3 Super 120B A12B scores 66.2 (B) on agent readiness against Cosmos-Reason2-2B's 59.5 (C), and leads in every scored category.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-super-120b-a12b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-super-120b-a12b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "cosmos-reason2-2b", "b": "nemotron-3-super-120b-a12b"}`. From a terminal: `anchor compare cosmos-reason2-2b nemotron-3-super-120b-a12b`
- 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-super-120b-a12b.json

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