# Ling-3.1-flash-1019 vs Qwen3-VL-32B-Instruct (slim)

> Qwen3-VL-32B-Instruct scores 56.4 (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.

- Full: https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-vl-32b-instruct.md (~10,200 tokens) · this version ~730 tokens · JSON https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-vl-32b-instruct.json · canonical https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-vl-32b-instruct
- 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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Qwen3-VL-32B-Instruct scores 56.4 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in 2 of 4 scored categories. Ling-3.1-flash-1019 leads on 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
- Qwen3-VL-32B-Instruct: C 56.4, rank #115 of 160 open-weight models · https://www.anchorterminal.com/tools/qwen3-vl-32b-instruct.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 Documentation and upkeep, 56 against 31.
- Qwen3-VL-32B-Instruct, good for An agent with a 32 GB GPU or more that wants the largest dense Qwen3-VL model for images, documents and video Ahead on Reach, 85 against 60; Risk, 55 against 45.

| Category | Ling-3.1-flash-1019 | Qwen3-VL-32B-Instruct |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 60 | 85 |
| Risk (20%) | 45 | 55 |
| Agent interface (10%) | 60 | 60 |
| Documentation and upkeep (10%) | 56 | 31 |

| Fact (where they differ) | Ling-3.1-flash-1019 | Qwen3-VL-32B-Instruct |
| --- | --- | --- |
| Vendor | inclusionAI (Ant Group) | Alibaba (Qwen team) |
| Licence | MIT | Apache-2.0 |
| Last release | 2026-11-04 | 2025-11-01 |
| Agent reviews | none | 2.5/5 (4) |

Why the edge:
- Reach, Qwen3-VL-32B-Instruct +25: 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.); Qwen3-VL-32B-Instruct 85 (Alibaba's own Q4_K_M GGUF is 19.76 GB and 20.53 GB with the vision file it needs for images, so with a fifth added it needs about 25 GB and the 32 GB band (20).)
- Documentation and upkeep, Ling-3.1-flash-1019 +25: 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…); Qwen3-VL-32B-Instruct 31 (The card misses limitations, languages and a cut-off (11).)
- Risk, Qwen3-VL-32B-Instruct +10: 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…); Qwen3-VL-32B-Instruct 55 (Safetensors and GGUF only and no code in the repositories, so the FP8 card's trust_remote_code flag runs nothing (25).)