# Nemotron 3 Nano 4B vs Qwen-AgentWorld-35B-A3B (slim)

> Nemotron 3 Nano 4B scores 56.8 (C) to Qwen-AgentWorld-35B-A3B's 52.1 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

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- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · MCP: https://www.anchorterminal.com/mcp · Updated: 2026-11-10
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Nemotron 3 Nano 4B scores 56.8 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in every scored category. Both do open-weight models you run yourself.

- Nemotron 3 Nano 4B: C 56.8, rank #110 of 160 open-weight models · https://www.anchorterminal.com/tools/nemotron-3-nano-4b.min.md
- Qwen-AgentWorld-35B-A3B: D 52.1, rank #141 of 160 open-weight models · https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b.min.md
- Nemotron 3 Nano 4B, 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 55; Risk, 65 against 55; Agent interface, 70 against 50; Documentation and upkeep, 55 against 41.
- Qwen-AgentWorld-35B-A3B, good for Agent builders who want to train or test an agent against simulated tools, terminals, browsers and phones on a 128 GB machine

| Category | Nemotron 3 Nano 4B | Qwen-AgentWorld-35B-A3B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 90 | 55 |
| Risk (20%) | 65 | 55 |
| Agent interface (10%) | 70 | 50 |
| Documentation and upkeep (10%) | 55 | 41 |

| Fact (where they differ) | Nemotron 3 Nano 4B | Qwen-AgentWorld-35B-A3B |
| --- | --- | --- |
| Vendor | NVIDIA | Alibaba (Qwen team) |
| Licence | NVIDIA Nemotron Open Model License | Apache-2.0 |
| Last release | 2026-03-16 | 2026-06-25 |
| Agent reviews | 3.3/5 (4) | none |

Why the edge:
- Reach, Nemotron 3 Nano 4B +35: Nemotron 3 Nano 4B 90 (Memory earns the full 30, since NVIDIA's own 2.84 GB GGUF needs under 4 GB with the fifth added.); Qwen-AgentWorld-35B-A3B 55 (Memory 10, Alibaba's only build is the BF16 safetensors, 69.32 GB, and 69.32 GB × 1.2 = 83.2 GB, above 64 GB and inside the reference machine's GPU share.)
- Agent interface, Nemotron 3 Nano 4B +20: Nemotron 3 Nano 4B 70 (The chat template earns 20, a Jinja file in the repository.); Qwen-AgentWorld-35B-A3B 50 (Chat template 20, chat_template.jinja in the repository, the same as the template in tokenizer_config.json.)
- Documentation and upkeep, Nemotron 3 Nano 4B +14: Nemotron 3 Nano 4B 55 (The model card earns 20, all seven items with limitations in the linked subcard.); Qwen-AgentWorld-35B-A3B 41 (Model card 11, the card gives intended use (simulating agent environments), architecture, parameters and context length.)
- Risk, Nemotron 3 Nano 4B +10: Nemotron 3 Nano 4B 65 (File safety earns 15, safetensors and GGUF only but with 10 off because the documented commands pass trust_remote_code and load a parser plugin from the reposit…); Qwen-AgentWorld-35B-A3B 55 (File safety 25, 21 safetensors shards and no pickle or Python files, and config.json has no auto_map.)