# OTel-2.0-LLM-31B-IT vs Swift 1.5 Qwen3.8-Flash-Next (slim)

> OTel-2.0-LLM-31B-IT scores 56.8 (C) to Swift 1.5 Qwen3.8-Flash-Next's 54.2 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

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- 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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OTel-2.0-LLM-31B-IT scores 56.8 (C) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 54.2 (C), and leads in 2 of 4 scored categories. Swift 1.5 Qwen3.8-Flash-Next leads on reach. Both do open-weight models you run yourself.

- OTel-2.0-LLM-31B-IT: C 56.8, rank #111 of 160 open-weight models · https://www.anchorterminal.com/tools/otel-2-0-llm-31b-it.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
- OTel-2.0-LLM-31B-IT, good for Telecoms teams that want an open model for standards questions and RAG on their own hardware Ahead on Risk, 55 against 45; Documentation and upkeep, 74 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 50.

| Category | OTel-2.0-LLM-31B-IT | Swift 1.5 Qwen3.8-Flash-Next |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 50 | 65 |
| Risk (20%) | 55 | 45 |
| Agent interface (10%) | 60 | 60 |
| Documentation and upkeep (10%) | 74 | 40 |

| Fact (where they differ) | OTel-2.0-LLM-31B-IT | Swift 1.5 Qwen3.8-Flash-Next |
| --- | --- | --- |
| Vendor | AT&T (Open Telco AI) | UkisAI |
| Licence | Apache-2.0 | 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 | 2026-11-01 | 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, OTel-2.0-LLM-31B-IT +34: OTel-2.0-LLM-31B-IT 74 (Model card 14, Intended use, a long limitations section, the architecture and training method, the parameters (31B) and the language (English) are on the card.); 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 +15: OTel-2.0-LLM-31B-IT 50 (Memory 10, the publisher's only build is the BF16 safetensors, 62.55 GB for the 15 shards the index names, and 62.55 GB × 1.2 = 75.1 GB, above 64 GB and inside…); 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…)
- Risk, OTel-2.0-LLM-31B-IT +10: OTel-2.0-LLM-31B-IT 55 (File safety 25, safetensors only, with no .bin, .pt or .pth files and no Python files.); Swift 1.5 Qwen3.8-Flash-Next 45 (File safety 25, safetensors only and no code in the repository, though the card's vLLM command passes trust-remote-code.)