# Cosmos-Reason2-2B vs Ling-3.0-tiny

> Cosmos-Reason2-2B scores 59.5 (C) to Ling-3.0-tiny's 55 (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-ling-3-0-tiny
- Markdown: https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ling-3-0-tiny.md (~6,350 tokens)
- Slim: https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ling-3-0-tiny.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ling-3-0-tiny.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 Ling-3.0-tiny's 55 (C), and leads in 2 of 4 scored categories. Ling-3.0-tiny leads on reach 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
- Ling-3.0-tiny: grade C, 55/100, rank #81 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ling-3-0-tiny.md · JSON https://www.anchorterminal.com/api/v1/tools/ling-3-0-tiny.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

Ahead on:
- Risk, 60 against 50
- Agent interface, 65 against 60

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

### Ling-3.0-tiny (C)

Good for: Agents on a laptop or small GPU that want a fast local model for tool calls and short reasoning, under MIT

Ahead on:
- Reach, 90 against 65
- Documentation and upkeep, 56 against 39

Watch for: No hosted provider today, and Ollama support is only an unmerged pull request


## Score by category

| Category | Weight | Cosmos-Reason2-2B | Ling-3.0-tiny | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 65 | 90 | Ling-3.0-tiny +25 |
| Risk | 20% (20 this run) | 60 | 50 | Cosmos-Reason2-2B +10 |
| Agent interface | 10% (10 this run) | 65 | 60 | Cosmos-Reason2-2B +5 |
| Documentation and upkeep | 10% (10 this run) | 39 | 56 | Ling-3.0-tiny +17 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **59.5 · C** | **55 · C** | |

## Facts side by side

| Fact | Cosmos-Reason2-2B | Ling-3.0-tiny |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | NVIDIA | inclusionAI (Ant Group) |
| 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 | MIT |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-03-10 | 2026-08-31 |
| 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.

**Ling-3.0-tiny.** A small, quick hybrid reasoning cousin for local agent work, tool calls and coding, under MIT. It runs in about 6 GB from inclusionAI's 4-bit GGUF in released Transformers, vLLM, SGLang and llama.cpp, with no host. The main risks are a card that doesn't match the files in places, the vLLM route running repository code, and no model 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

### Ling-3.0-tiny

1. Start it with `llama-server -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M --jinja -c 32768`, which needs llama.cpp b11772 or a recent release with bailingmoe3
2. With vLLM 0.32.0, `vllm serve inclusionAI/Ling-3.0-tiny --trust-remote-code --enable-prefix-caching --mamba-cache-mode align --enable-auto-tool-choice --tool-call-parser ling3 --reasoning-parser ling3`, which runs the repository's config code
3. With SGLang 0.5.22, its cookbook uses `--reasoning-parser deepseek-r1 --tool-call-parser glm45` for this model, and drop the card's NEXTN flags since tiny has no MTP layer
4. Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks
5. Sampling per the card is temperature 1.0, top_p 0.95 and top_k 20
6. The template keeps earlier reasoning in the prompt, which grows long agent sessions
7. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented
8. For a guardrail, inclusionAI's Ling-3.0-tiny-singprobe scores intent, unsafe output and hallucination per token, but only through SGLang and vLLM integration branches

## Questions

### Which is better for AI agents, Cosmos-Reason2-2B or Ling-3.0-tiny?

Cosmos-Reason2-2B scores 59.5 (C) on agent readiness against Ling-3.0-tiny's 55 (C), and leads in 2 of 4 scored categories. Ling-3.0-tiny leads on reach and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ling-3-0-tiny.json, and with the fewest tokens: https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ling-3-0-tiny.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "cosmos-reason2-2b", "b": "ling-3-0-tiny"}`. From a terminal: `anchor compare cosmos-reason2-2b ling-3-0-tiny`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/cosmos-reason2-2b.json and https://www.anchorterminal.com/api/v1/tools/ling-3-0-tiny.json

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