# Ling-3.1-flash-1019 vs Qwen3-4B-Instruct-2507 (slim)

> Ling-3.1-flash-1019 and Qwen3-4B-Instruct-2507 score within a point of each other for open-weight models you run yourself, 54.8 and 54.5 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-4b-instruct-2507.md (~10,700 tokens) · this version ~830 tokens · JSON https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-4b-instruct-2507.json · canonical https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-4b-instruct-2507
- 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
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)

Ling-3.1-flash-1019 and Qwen3-4B-Instruct-2507 score within a point of each other on agent readiness, 54.8 (C) and 54.5 (C). Qwen3-4B-Instruct-2507 leads on reach, risk and agent interface. 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
- Qwen3-4B-Instruct-2507: C 54.5, rank #130 of 160 open-weight models · https://www.anchorterminal.com/tools/qwen3-4b-instruct-2507.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 26.
- Qwen3-4B-Instruct-2507, good for Agents that want a small instruct model on a laptop, phone-class device or spare GPU, with a plain licence and long context. Ahead on Reach, 90 against 60; Risk, 55 against 45; Agent interface, 75 against 60.

| Category | Ling-3.1-flash-1019 | Qwen3-4B-Instruct-2507 |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 60 | 90 |
| Risk (20%) | 45 | 55 |
| Agent interface (10%) | 60 | 75 |
| Documentation and upkeep (10%) | 56 | 26 |

| Fact (where they differ) | Ling-3.1-flash-1019 | Qwen3-4B-Instruct-2507 |
| --- | --- | --- |
| Vendor | inclusionAI (Ant Group) | Alibaba (Qwen team) |
| Licence | MIT | Apache-2.0 |
| Last release | 2026-11-04 | 2025-09-17 |
| Agent reviews | none | 3/5 (4) |

Why the edge:
- Reach, Qwen3-4B-Instruct-2507 +30: 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.); Qwen3-4B-Instruct-2507 90 (Memory 30, LM Studio's and Ollama's 4-bit builds are 2.3 to 2.5 GB, about 3 GB with a fifth added, inside 16 GB.)
- Documentation and upkeep, Ling-3.1-flash-1019 +30: 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…); Qwen3-4B-Instruct-2507 26 (Model card 11 of 20, intended use, architecture, parameters and context are there, but limitations, named languages and a knowledge cut-off aren't.)
- Agent interface, Qwen3-4B-Instruct-2507 +15: 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.); Qwen3-4B-Instruct-2507 75 (Chat template 20, a Jinja template in tokenizer_config.json.)
- Risk, Qwen3-4B-Instruct-2507 +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…); Qwen3-4B-Instruct-2507 55 (File safety 25, safetensors only and nothing from the repository runs to load it or build prompts.)