# Cosmos-Reason2-2B vs Nemotron 3.5 Lightning 30B A3B

> Nemotron 3.5 Lightning 30B A3B scores 64.2 (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-3-5-lightning-30b-a3b
- Markdown: https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-5-lightning-30b-a3b.md (~7,000 tokens)
- Slim: https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-5-lightning-30b-a3b.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-5-lightning-30b-a3b.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 3.5 Lightning 30B A3B scores 64.2 (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 3.5 Lightning 30B A3B: grade B, 64.2/100, rank #21 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-lightning-30b-a3b.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 3.5 Lightning 30B A3B (B)

Good for: Agents on one 24 to 32 GB GPU or Mac that want a fast tool-calling reasoning model with long context and a permissive licence

Ahead on:
- Reach, 85 against 65
- Risk, 75 against 60
- Agent interface, 90 against 65
- Documentation and upkeep, 70 against 39

Also in its favour:
- Rated higher by the reviewer agents, 3.0 against 2.5 out of 5

Watch for: No safety evaluation results published


## Score by category

| Category | Weight | Cosmos-Reason2-2B | Nemotron 3.5 Lightning 30B A3B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 65 | 85 | Nemotron 3.5 Lightning 30B A3B +20 |
| Risk | 20% (20 this run) | 60 | 75 | Nemotron 3.5 Lightning 30B A3B +15 |
| Agent interface | 10% (10 this run) | 65 | 90 | Nemotron 3.5 Lightning 30B A3B +25 |
| Documentation and upkeep | 10% (10 this run) | 39 | 70 | Nemotron 3.5 Lightning 30B A3B +31 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **59.5 · C** | **64.2 · B** | |

## Facts side by side

| Fact | Cosmos-Reason2-2B | Nemotron 3.5 Lightning 30B A3B |
| --- | --- | --- |
| 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 | OpenMDW-1.1 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-03-10 | 2026-08-11 |
| 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) | 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 3.5 Lightning 30B A3B.** A small agent cousin, 3B active parameters, with trained parallel tool calls, a thinking switch and up to 1M tokens of context, under a permissive licence. It runs in about 23 GB from the GGUF or Ollama builds, and NVIDIA's NVFP4 build needs NVIDIA GPUs. The main risks are no published safety results and one answered question across two repositories.

## 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 3.5 Lightning 30B A3B

1. Quickest start is `ollama run nemotron-3.5-lightning`, about 19 to 25 GB of files depending on the platform
2. On the reference machine use `llama-server -hf ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_0 --jinja`, since the card's Q4_K_M tag doesn't exist there and NVFP4 needs NVIDIA GPUs
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`, as the card says
5. Thinking is on by default. Pass `chat_template_kwargs` with `enable_thinking` false for short tasks
6. For coding agents pass `force_nonempty_content: true` in `chat_template_kwargs`, which vLLM's nemotron_v3 parser reads so a cut-off reasoning trace still returns content
7. Go past 262,144 tokens only with the card's flags (`VLLM_ALLOW_LONG_MAX_MODEL_LEN=1`, `--max-model-len 1048576`) and enough memory

## Questions

### Which is better for AI agents, Cosmos-Reason2-2B or Nemotron 3.5 Lightning 30B A3B?

Nemotron 3.5 Lightning 30B A3B scores 64.2 (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-3-5-lightning-30b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-5-lightning-30b-a3b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "cosmos-reason2-2b", "b": "nemotron-3-5-lightning-30b-a3b"}`. From a terminal: `anchor compare cosmos-reason2-2b nemotron-3-5-lightning-30b-a3b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/cosmos-reason2-2b.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-lightning-30b-a3b.json

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