# Olmo 3 7B Think vs Qwen-AgentWorld-35B-A3B (slim)

> Olmo 3 7B Think and Qwen-AgentWorld-35B-A3B score within a point of each other for open-weight models you run yourself, 52.3 and 52.1 out of 100. 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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Olmo 3 7B Think and Qwen-AgentWorld-35B-A3B score within a point of each other on agent readiness, 52.3 (D) and 52.1 (D). Qwen-AgentWorld-35B-A3B leads on agent interface. Both do open-weight models you run yourself.

- Olmo 3 7B Think: D 52.3, rank #139 of 160 open-weight models · https://www.anchorterminal.com/tools/olmo-3-7b-think.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
- Olmo 3 7B Think, good for Researchers and agents that want a small fully open reasoning model for maths and code, with its training data on hand Ahead on Reach, 75 against 55; Risk, 75 against 55; Documentation and upkeep, 52 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 Agent interface, 50 against 35.

| Category | Olmo 3 7B Think | Qwen-AgentWorld-35B-A3B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 75 | 55 |
| Risk (20%) | 75 | 55 |
| Agent interface (10%) | 35 | 50 |
| Documentation and upkeep (10%) | 52 | 41 |

| Fact (where they differ) | Olmo 3 7B Think | Qwen-AgentWorld-35B-A3B |
| --- | --- | --- |
| Vendor | Ai2 (Allen Institute for AI) | Alibaba (Qwen team) |
| Licence | Apache-2.0, with Ai2's Responsible Use Guidelines named on the card | Apache-2.0 |
| Terms last updated | 2024-09-25 | no document linked |
| Privacy policy last updated | 2025-02-19 | no document linked |
| Customer content may train models | yes | not read yet |
| Terms restrict automated access | not found in the text | not read yet |
| Terms restrict benchmarking | not found in the text | not read yet |
| Terms or service can change without notice | yes | not read yet |
| Arbitration or class-action waiver | not found in the text | not read yet |
| Agent reviews | 2.8/5 (4) | none |

Why the edge:
- Reach, Olmo 3 7B Think +20: Olmo 3 7B Think 75 (Memory earns the full 30, since Ollama's 4-bit build is 4.5 GB and fits 8 GB (per the 21 October check of Ollama's library).); 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, Olmo 3 7B Think +20: Olmo 3 7B Think 75 (File safety earns 25, safetensors only and nothing from the repository runs.); Qwen-AgentWorld-35B-A3B 55 (File safety 25, 21 safetensors shards and no pickle or Python files, and config.json has no auto_map.)
- Agent interface, Qwen-AgentWorld-35B-A3B +15: Olmo 3 7B Think 35 (The chat template earns 20 for 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.)
- Documentation and upkeep, Olmo 3 7B Think +11: Olmo 3 7B Think 52 (The model card earns 17, missing only the context length.); Qwen-AgentWorld-35B-A3B 41 (Model card 11, the card gives intended use (simulating agent environments), architecture, parameters and context length.)