# Ling-3.1-flash-1019 vs Swift 1.5 Qwen3.8-Flash-Next (slim)

> Ling-3.1-flash-1019 and Swift 1.5 Qwen3.8-Flash-Next score within a point of each other for open-weight models you run yourself, 54.8 and 54.2 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-swift-1-5-qwen3-8-flash-next.md (~10,400 tokens) · this version ~830 tokens · JSON https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-swift-1-5-qwen3-8-flash-next.json · canonical https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-swift-1-5-qwen3-8-flash-next
- 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 Swift 1.5 Qwen3.8-Flash-Next score within a point of each other on agent readiness, 54.8 (C) and 54.2 (C). Swift 1.5 Qwen3.8-Flash-Next leads on reach. 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
- Swift 1.5 Qwen3.8-Flash-Next: C 54.2, rank #132 of 160 open-weight models · https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-flash-next.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 40.
- Swift 1.5 Qwen3.8-Flash-Next, good for Teams under US$1M revenue, or researchers, with an 8-GPU node who want Qwen3.8-Flash-Next with shorter thinking for coding and agent work. Ahead on Reach, 65 against 60.

| Category | Ling-3.1-flash-1019 | Swift 1.5 Qwen3.8-Flash-Next |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 60 | 65 |
| Risk (20%) | 45 | 45 |
| Agent interface (10%) | 60 | 60 |
| Documentation and upkeep (10%) | 56 | 40 |

| Fact (where they differ) | Ling-3.1-flash-1019 | Swift 1.5 Qwen3.8-Flash-Next |
| --- | --- | --- |
| Vendor | inclusionAI (Ant Group) | UkisAI |
| Licence | MIT | Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue, plus the Qwen Community License 1.0 on the base, which needs a separate licence from Qwen for a Model as a Service or AI Work Assistant business and the model name shown above 100M monthly users or US$20M monthly revenue |
| Last release | 2026-11-04 | 2026-09-24 |
| Terms last updated | no document linked | no date given |
| 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 | 1.8/5 (4) |

Why the edge:
- Documentation and upkeep, Ling-3.1-flash-1019 +16: 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…); Swift 1.5 Qwen3.8-Flash-Next 40 (Model card 5 of 20, intended use and context only.)
- Reach, Swift 1.5 Qwen3.8-Flash-Next +5: 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.); Swift 1.5 Qwen3.8-Flash-Next 65 (Memory 5 of 30, the smallest 4-bit build is UkisAI's IQ4_XS GGUF at 98.6 GB with the vision projector, 118.3 GB with a fifth, above the reference machine's GPU…)