# Ling-3.1-flash-1019 vs Mistral Large 4 (slim)

> Ling-3.1-flash-1019 and Mistral Large 4 score within a point of each other for open-weight models you run yourself, 54.8 and 54.6 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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Ling-3.1-flash-1019 and Mistral Large 4 score within a point of each other on agent readiness, 54.8 (C) and 54.6 (C). Mistral Large 4 leads on risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.

- Ling-3.1-flash-1019: C 54.8, rank #126 of 160 open-weight models · https://www.anchorterminal.com/tools/ling-3-1-flash-1019.min.md
- Mistral Large 4: C 54.6, rank #128 of 160 open-weight models · https://www.anchorterminal.com/tools/mistral-large-4.min.md
- Ling-3.1-flash-1019, good for Teams with a multi-GPU server that want an MIT-licensed flagship for long agent runs and coding Ahead on Reach, 60 against 50.
- 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 45; Agent interface, 95 against 60; Documentation and upkeep, 64 against 56.

| Category | Ling-3.1-flash-1019 | Mistral Large 4 |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 60 | 50 |
| Risk (20%) | 45 | 75 |
| Agent interface (10%) | 60 | 95 |
| Documentation and upkeep (10%) | 56 | 64 |

| Fact (where they differ) | Ling-3.1-flash-1019 | Mistral Large 4 |
| --- | --- | --- |
| Vendor | inclusionAI (Ant Group) | Mistral AI |
| Licence | MIT | Mistral Ecosystem Open-Weight License (modified MIT) |
| Last release | 2026-11-04 | 2026-10-29 |

Why the edge:
- Agent interface, Mistral Large 4 +35: Ling-3.1-flash-1019 60 (Chat template 20, chat_template.jinja in the repository (6,042 characters), which handles tools, the thinking switch and preserved reasoning.); Mistral Large 4 95 (The chat template is in the repository (20).)
- Risk, Mistral Large 4 +30: Ling-3.1-flash-1019 45 (File safety 15, safetensors only, no .bin, .pt or .pth files (25), less 10 because the card's documented vLLM command passes --trust-remote-code, which runs the…); Mistral Large 4 75 (File safety scores 25, safetensors only and loaded by Transformers' own code with a Jinja template.)
- Reach, Ling-3.1-flash-1019 +10: Ling-3.1-flash-1019 60 (Memory 0, inclusionAI's only build is the FP8 release at 573.78 GB, × 1.2 = 688.5 GB, above 256 GB, and no runtime's library has a build.); 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.)
- Documentation and upkeep, Mistral Large 4 +8: Ling-3.1-flash-1019 56 (Model card 11, Intended use (complex reasoning, multi-step tasks and agentic workflows), architecture, parameters (560B total, 25B active) and context (1M claim…); Mistral Large 4 64 (The card covers intended use, architecture, parameters, context and languages, and misses limitations and a cut-off (14).)