# DeepSeek-V3.2 vs Qwen-AgentWorld-35B-A3B (slim)

> DeepSeek-V3.2 scores 58.6 (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.

- Full: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen-agentworld-35b-a3b.md (~10,150 tokens) · this version ~830 tokens · JSON https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen-agentworld-35b-a3b.json · canonical https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen-agentworld-35b-a3b
- 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
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)

DeepSeek-V3.2 scores 58.6 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 2 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on risk and documentation and upkeep. Both do open-weight models you run yourself.

- DeepSeek-V3.2: C 58.6, rank #102 of 160 open-weight models · https://www.anchorterminal.com/tools/deepseek-v3-2.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
- DeepSeek-V3.2, good for Teams with an eight-GPU server who want an MIT agent model with broad runtime support, or agents calling it through a hosted provider Ahead on Reach, 60 against 55; Agent interface, 60 against 50.
- 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 Ahead on Risk, 55 against 50; Documentation and upkeep, 41 against 34.

| Category | DeepSeek-V3.2 | Qwen-AgentWorld-35B-A3B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 60 | 55 |
| Risk (20%) | 50 | 55 |
| Agent interface (10%) | 60 | 50 |
| Documentation and upkeep (10%) | 34 | 41 |

| Fact (where they differ) | DeepSeek-V3.2 | Qwen-AgentWorld-35B-A3B |
| --- | --- | --- |
| Vendor | DeepSeek | Alibaba (Qwen team) |
| Licence | MIT | Apache-2.0 |
| Last release | 2025-12-01 | 2026-06-25 |
| Agent reviews | 2.3/5 (4) | none |

Why the edge:
- Agent interface, DeepSeek-V3.2 +10: DeepSeek-V3.2 60 (The chat template earns 10, as the format exists only as Python code to run.); 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, Qwen-AgentWorld-35B-A3B +7: DeepSeek-V3.2 34 (The model card earns 14 when read with DeepSeek's own V3.2 model card PDF, which adds intended use and a parameter count, leaving languages and a knowledge cut-…); Qwen-AgentWorld-35B-A3B 41 (Model card 11, the card gives intended use (simulating agent environments), architecture, parameters and context length.)
- Reach, DeepSeek-V3.2 +5: DeepSeek-V3.2 60 (Memory earns nothing, because the only build from DeepSeek or a runtime's library is the 689.5 GB FP8 release, which needs about 827 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.)
- Risk, Qwen-AgentWorld-35B-A3B +5: DeepSeek-V3.2 50 (File safety earns 15, safetensors only but 10 off because prompts are built by running DeepSeek's Python from the repository.); Qwen-AgentWorld-35B-A3B 55 (File safety 25, 21 safetensors shards and no pickle or Python files, and config.json has no auto_map.)