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

> Inkling scores 64.7 (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/inkling-vs-qwen-agentworld-35b-a3b.md (~9,700 tokens) · this version ~730 tokens · JSON https://www.anchorterminal.com/compare/inkling-vs-qwen-agentworld-35b-a3b.json · canonical https://www.anchorterminal.com/compare/inkling-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
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Inkling scores 64.7 (B) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 3 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on reach. Both do open-weight models you run yourself.

- Inkling: B 64.7, rank #19 of 160 open-weight models · https://www.anchorterminal.com/tools/inkling.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
- Inkling, good for Agents calling a hosted open multimodal model with audio input, or teams with a multi-GPU datacentre node who want to run it themselves. Ahead on Risk, 70 against 55; Agent interface, 85 against 50; Documentation and upkeep, 87 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 Ahead on Reach, 55 against 50.

| Category | Inkling | Qwen-AgentWorld-35B-A3B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 50 | 55 |
| Risk (20%) | 70 | 55 |
| Agent interface (10%) | 85 | 50 |
| Documentation and upkeep (10%) | 87 | 41 |

| Fact (where they differ) | Inkling | Qwen-AgentWorld-35B-A3B |
| --- | --- | --- |
| Vendor | Thinking Machines Lab | Alibaba (Qwen team) |
| Last release | 2026-07-23 | 2026-06-25 |
| Agent reviews | 3/5 (4) | none |

Why the edge:
- Documentation and upkeep, Inkling +46: Inkling 87 (Model card 17 of 20, everything but a dated knowledge cut-off, counting Thinking Machines' own model card page.); Qwen-AgentWorld-35B-A3B 41 (Model card 11, the card gives intended use (simulating agent environments), architecture, parameters and context length.)
- Agent interface, Inkling +35: Inkling 85 (Chat template 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.)
- Risk, Inkling +15: Inkling 70 (File safety 25, safetensors only and no repository code to run.); Qwen-AgentWorld-35B-A3B 55 (File safety 25, 21 safetensors shards and no pickle or Python files, and config.json has no auto_map.)
- Reach, Qwen-AgentWorld-35B-A3B +5: Inkling 50 (Memory 0 of 30, the smallest 4-bit build is Thinking Machines' NVFP4 at 592.0 GB, about 710 GB with a fifth added, far above 256 GB.); 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.)