# Ling-3.1-flash-1019 vs Nemotron 3 Nano 30B A3B (slim)

> Nemotron 3 Nano 30B A3B scores 67.6 (B) 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-nemotron-3-nano-30b-a3b.md (~11,050 tokens) · this version ~930 tokens · JSON https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-nemotron-3-nano-30b-a3b.json · canonical https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-nemotron-3-nano-30b-a3b
- 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)

Nemotron 3 Nano 30B A3B scores 67.6 (B) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in every scored category. 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
- Nemotron 3 Nano 30B A3B: B 67.6, rank #12 of 160 open-weight models · https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.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
- Nemotron 3 Nano 30B A3B, good for Agents with a 24 to 32 GB GPU or Mac that want a quick tool-calling and reasoning cousin with very long context. Ahead on Reach, 90 against 60; Risk, 60 against 45; Agent interface, 90 against 60; Documentation and upkeep, 80 against 56.

| Category | Ling-3.1-flash-1019 | Nemotron 3 Nano 30B A3B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 60 | 90 |
| Risk (20%) | 45 | 60 |
| Agent interface (10%) | 60 | 90 |
| Documentation and upkeep (10%) | 56 | 80 |

| Fact (where they differ) | Ling-3.1-flash-1019 | Nemotron 3 Nano 30B A3B |
| --- | --- | --- |
| Vendor | inclusionAI (Ant Group) | NVIDIA |
| Licence | MIT | NVIDIA Nemotron Open Model License (custom, Apache-style, commercial use allowed, not an OSI licence) |
| Last release | 2026-11-04 | 2026-08-24 |
| Terms last updated | no document linked | 2025-12-15 |
| Customer content may train models | not read yet | not found in the text |
| Terms restrict automated access | not read yet | not found in the text |
| Terms restrict benchmarking | not read yet | not found in the text |
| Terms or service can change without notice | not read yet | not found in the text |
| Arbitration or class-action waiver | not read yet | not found in the text |
| Agent reviews | none | 3.3/5 (4) |

Why the edge:
- Reach, Nemotron 3 Nano 30B A3B +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.); Nemotron 3 Nano 30B A3B 90 (Memory earns 25 because the smallest 4-bit build is Ollama's NVFP4 MLX build at 18 GB, 21.6 GB with the fifth added, and NVIDIA's own NVFP4 build lands in the s…)
- Agent interface, Nemotron 3 Nano 30B A3B +30: 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.); Nemotron 3 Nano 30B A3B 90 (The chat template earns 20 as a Jinja file in the repository.)
- Documentation and upkeep, Nemotron 3 Nano 30B A3B +24: 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…); Nemotron 3 Nano 30B A3B 80 (The model card earns 20, with all seven items on the card and its subcards.)
- Risk, Nemotron 3 Nano 30B A3B +15: 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…); Nemotron 3 Nano 30B A3B 60 (File safety earns 15, safetensors only but with the documented serving path passing trust-remote-code and loading a reasoning parser file from the repository.)