# Mistral Large 4 vs Qwen-AgentWorld-35B-A3B (slim)

> Mistral Large 4 scores 54.6 (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.

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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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Mistral Large 4 scores 54.6 (C) 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.

- Mistral Large 4: C 54.6, rank #128 of 160 open-weight models · https://www.anchorterminal.com/tools/mistral-large-4.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
- Mistral Large 4, good for Operators with an 8-GPU Blackwell or H200 node who want a frontier-sized open model for coding agents and defensive security, with a hosted twin on Mistral's API. Ahead on Risk, 75 against 55; Agent interface, 95 against 50; Documentation and upkeep, 64 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 | Mistral Large 4 | Qwen-AgentWorld-35B-A3B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 50 | 55 |
| Risk (20%) | 75 | 55 |
| Agent interface (10%) | 95 | 50 |
| Documentation and upkeep (10%) | 64 | 41 |

| Fact (where they differ) | Mistral Large 4 | Qwen-AgentWorld-35B-A3B |
| --- | --- | --- |
| Vendor | Mistral AI | Alibaba (Qwen team) |
| Licence | Mistral Ecosystem Open-Weight License (modified MIT) | Apache-2.0 |
| Last release | 2026-10-29 | 2026-06-25 |

Why the edge:
- Agent interface, Mistral Large 4 +45: Mistral Large 4 95 (The chat template is in the repository (20).); 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, Mistral Large 4 +23: Mistral Large 4 64 (The card covers intended use, architecture, parameters, context and languages, and misses limitations and a cut-off (14).); Qwen-AgentWorld-35B-A3B 41 (Model card 11, the card gives intended use (simulating agent environments), architecture, parameters and context length.)
- Risk, Mistral Large 4 +20: Mistral Large 4 75 (File safety scores 25, safetensors only and loaded by Transformers' own code with a Jinja template.); 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: Mistral Large 4 50 (Memory scores 0, since the smallest 4-bit build, Mistral's NVFP4, is 610 GB, about 732 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.)