# Granite 4.2 3B vs Ling-3.1-flash-1019 (slim)

> Granite 4.2 3B scores 59.8 (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/granite-4-2-3b-vs-ling-3-1-flash-1019.md (~10,100 tokens) · this version ~780 tokens · JSON https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ling-3-1-flash-1019.json · canonical https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ling-3-1-flash-1019
- 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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Granite 4.2 3B scores 59.8 (C) 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.

- Granite 4.2 3B: C 59.8, rank #92 of 160 open-weight models · https://www.anchorterminal.com/tools/granite-4-2-3b.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
- Granite 4.2 3B, good for Agents on small hardware that want a permissive reasoning model for tool calls and coding, with thinking they can switch off Ahead on Reach, 95 against 60; Risk, 55 against 45; Agent interface, 85 against 60; Documentation and upkeep, 92 against 56.
- 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

| Category | Granite 4.2 3B | Ling-3.1-flash-1019 |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 95 | 60 |
| Risk (20%) | 55 | 45 |
| Agent interface (10%) | 85 | 60 |
| Documentation and upkeep (10%) | 92 | 56 |

| Fact (where they differ) | Granite 4.2 3B | Ling-3.1-flash-1019 |
| --- | --- | --- |
| Vendor | IBM | inclusionAI (Ant Group) |
| Licence | Apache-2.0 | MIT |
| Last release | 2026-10-20 | 2026-11-04 |
| Agent reviews | 3.8/5 (4) | none |

Why the edge:
- Documentation and upkeep, Granite 4.2 3B +36: Granite 4.2 3B 92 (The card covers everything but a knowledge cut-off (17).); 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…)
- Reach, Granite 4.2 3B +35: Granite 4.2 3B 95 (Memory earns the full 30, since IBM's 4-bit MLX build is 2.07 GB and Ollama's Q4_K_M build 2.2 GB, about 2.5 to 2.6 GB with the fifth added.); 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.)
- Agent interface, Granite 4.2 3B +25: Granite 4.2 3B 85 (The template ships in the repository (20).); 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.)
- Risk, Granite 4.2 3B +10: Granite 4.2 3B 55 (File safety gets 15 of 25, safetensors only, less 10 because the card's vLLM command loads the repository's parser plugin.); 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…)