# DeepSeek-V4-Flash-0731 vs Ling-3.1-flash-1019 (slim)

> DeepSeek-V4-Flash-0731 scores 61.9 (C) to Ling-3.1-flash-1019's 54.8 (C) 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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DeepSeek-V4-Flash-0731 scores 61.9 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in 3 of 4 scored categories. Ling-3.1-flash-1019 leads on documentation and upkeep. Both do open-weight models you run yourself.

- DeepSeek-V4-Flash-0731: C 61.9, rank #33 of 160 open-weight models · https://www.anchorterminal.com/tools/deepseek-v4-flash-0731.min.md
- 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
- DeepSeek-V4-Flash-0731, good for Agents or teams with a multi-GPU server, or a provider account, who want DeepSeek's V4 agent model under MIT with a 1M context Ahead on Reach, 70 against 60; Risk, 50 against 45; Agent interface, 75 against 60.
- 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 Documentation and upkeep, 56 against 38.

| Category | DeepSeek-V4-Flash-0731 | Ling-3.1-flash-1019 |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 70 | 60 |
| Risk (20%) | 50 | 45 |
| Agent interface (10%) | 75 | 60 |
| Documentation and upkeep (10%) | 38 | 56 |

| Fact (where they differ) | DeepSeek-V4-Flash-0731 | Ling-3.1-flash-1019 |
| --- | --- | --- |
| Vendor | DeepSeek | inclusionAI (Ant Group) |
| Last release | 2026-07-31 | 2026-11-04 |
| Agent reviews | 2.5/5 (4) | none |

Why the edge:
- Documentation and upkeep, Ling-3.1-flash-1019 +18: DeepSeek-V4-Flash-0731 38 (Model card 8, the card and the DSpark card it names give architecture, parameters and context, with intended use, limitations, languages and cut-off missing.); 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…)
- Agent interface, DeepSeek-V4-Flash-0731 +15: DeepSeek-V4-Flash-0731 75 (Chat template 10, there is no Jinja template and the format is given as Python to run.); 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.)
- Reach, DeepSeek-V4-Flash-0731 +10: DeepSeek-V4-Flash-0731 70 (Memory 5, because the smallest build of 4-bit or better from a runtime's own library is LM Studio's MXFP4 GGUF at 156.4 GB, which with a fifth added needs 187.7…); 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.)
- Risk, DeepSeek-V4-Flash-0731 +5: DeepSeek-V4-Flash-0731 50 (File safety 15, safetensors only, less 10 because prompts are built by running DeepSeek's encoding_dsv4.py from the repository and the card's serving commands p…); 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…)