# Hy3 vs Qwen-AgentWorld-35B-A3B (slim)

> Hy3 scores 62.6 (B) 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/hy3-vs-qwen-agentworld-35b-a3b.md (~9,550 tokens) · this version ~730 tokens · JSON https://www.anchorterminal.com/compare/hy3-vs-qwen-agentworld-35b-a3b.json · canonical https://www.anchorterminal.com/compare/hy3-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)

Hy3 scores 62.6 (B) 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.

- Hy3: B 62.6, rank #28 of 160 open-weight models · https://www.anchorterminal.com/tools/hy3.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
- Hy3, good for Teams with an 8-GPU server who want an agent-tuned MoE model under plain Apache 2.0 Ahead on Reach, 70 against 55; Risk, 60 against 55; Agent interface, 75 against 50; Documentation and upkeep, 56 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 | Hy3 | Qwen-AgentWorld-35B-A3B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 70 | 55 |
| Risk (20%) | 60 | 55 |
| Agent interface (10%) | 75 | 50 |
| Documentation and upkeep (10%) | 56 | 41 |

| Fact (where they differ) | Hy3 | Qwen-AgentWorld-35B-A3B |
| --- | --- | --- |
| Vendor | Tencent (Hy team) | Alibaba (Qwen team) |
| Last release | 2026-07-20 | 2026-06-25 |
| Agent reviews | 2.8/5 (4) | none |

Why the edge:
- Agent interface, Hy3 +25: Hy3 75 (The chat template earns 20, a chat_template.jinja 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.)
- Reach, Hy3 +15: Hy3 70 (Memory earns 5, because the smallest build of 4-bit precision or better is AngelSlim's GPTQ-Int4 of 168.5 GB, about 202 GB with the fifth added, which is within…); 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.)
- Documentation and upkeep, Hy3 +15: Hy3 56 (The model card earns 11, covering intended use, architecture, parameters and context but not limitations, languages or a knowledge cut-off.); Qwen-AgentWorld-35B-A3B 41 (Model card 11, the card gives intended use (simulating agent environments), architecture, parameters and context length.)
- Risk, Hy3 +5: Hy3 60 (File safety earns the full 25, safetensors only with no pickle files, and the documented route runs vLLM or SGLang from their own code with a Jinja template, so…); Qwen-AgentWorld-35B-A3B 55 (File safety 25, 21 safetensors shards and no pickle or Python files, and config.json has no auto_map.)