# Kolibri-1 vs Ling-3.1-flash-1019 (slim)

> Kolibri-1 scores 61 (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/kolibri-1-vs-ling-3-1-flash-1019.md (~10,050 tokens) · this version ~730 tokens · JSON https://www.anchorterminal.com/compare/kolibri-1-vs-ling-3-1-flash-1019.json · canonical https://www.anchorterminal.com/compare/kolibri-1-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
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)

Kolibri-1 scores 61 (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.

- Kolibri-1: C 61, rank #44 of 160 open-weight models · https://www.anchorterminal.com/tools/kolibri-1.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
- Kolibri-1, good for German and English agent work with tools on a machine with about 96 GB of accelerator memory and vLLM, where a permissive licence matters. Ahead on Reach, 65 against 60; Risk, 65 against 45; Agent interface, 90 against 60; Documentation and upkeep, 70 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 | Kolibri-1 | Ling-3.1-flash-1019 |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 65 | 60 |
| Risk (20%) | 65 | 45 |
| Agent interface (10%) | 90 | 60 |
| Documentation and upkeep (10%) | 70 | 56 |

| Fact (where they differ) | Kolibri-1 | Ling-3.1-flash-1019 |
| --- | --- | --- |
| Vendor | Aleph Alpha | inclusionAI (Ant Group) |
| Licence | Apache-2.0 | MIT |
| Last release | 2026-10-19 | 2026-11-04 |
| Agent reviews | 3.3/5 (4) | none |

Why the edge:
- Agent interface, Kolibri-1 +30: Kolibri-1 90 (The chat template earns 20 as Jinja in tokenizer_config.json.); 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, Kolibri-1 +20: Kolibri-1 65 (File safety earns 25 for safetensors only, with no repository code run on load.); 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…)
- Documentation and upkeep, Kolibri-1 +14: Kolibri-1 70 (The model card earns 20 with all seven items.); 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, Kolibri-1 +5: Kolibri-1 65 (Memory earns 10 because the smallest qualifying build is Aleph Alpha's own FP8 one at 78.8 GB, which comes to 94.6 GB with the fifth added and so fits only the…); 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.)