DeepSeek V4.1 Flash vs OTel-2.0-LLM-31B-IT

DeepSeek V4.1 Flash 48

The cards cover architecture, parameters and context but not intended use, limitations, languages or a cut-off, so 8. Training data is a general description with no cut-off, which caps it at 5. The files changed on 16 and 17 October and DeepSeek's deepseek-recipe 0.2.0 changed the V4.1 prompt encoding on 20 October, inside 30 days (20). None of the 40 most recent questions across both repositories had a DeepSeek reply (0). Every release keeps its own repository and the update names what it replaces (10). File health loses 5 for an open, unanswered pull request saying the September template trails the encoding changes. 8 + 5 + 20 + 0 + 10 + 5 = 48.

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. Training data 15, the card names the sources (3GPP, ETSI, ITU, GSMA, CAMARA, O-RAN and TM Forum material provided by GSMA), the overall sizes (15 billion raw tokens, over 1 trillion processed, about 440 billion used), the mix of example types and the processing tool, and lists what was left out. Last change 20, the last commit, on 1 November 2026, updated the card's links, resources, citation and contacts, nine days before this check. Responsiveness 20, Of the 3 discussions that aren't pull requests (7 August to 28 October 2026), 2 have a reply from the repository's owner, which is most. Version policy 0, the weights were replaced in place: uploads on 27 July, 3 August and 1 September 2026 under one name, with a standing notice on the card that they may change and that users should pin a revision, and no dated note of what changed. File health 5, the card describes 15 shards of about 62.6 GB while the repository still holds an earlier 14-shard set beside them, 126.8 GB in all. 14 + 15 + 20 + 20 + 0 + 5 = 74.

DeepSeek V4.1 Flash 75

There's no Jinja template in the 17 October repository, only code to run, so the template line gets 10. DeepSeek documents the DSML tool format both ways and ships its own parser in encoding.py and deepseek-recipe, but names no parser in a runtime (10, read as for DeepSeek-V3.2 and V4-Flash-0731), and one block can carry several calls (20). deepseek-recipe documents JSON Schema output via response_format (15). Thinking turns off in chat mode and takes an effort budget from 1 to 100, with reasoning returned in think tags (10). The 1M context was trained in (10).

OTel-2.0-LLM-31B-IT 60

Chat template 20, chat_template.jinja in the repository, 19,300 characters, the publisher's own. Tool-calling format 10, only the template says how tools are declared and how calls come back. Trained tool calling 20, the template loops over tool_calls, so it renders several calls in one turn, and the card says the training mix included general-purpose agentic tool-calling examples, while none were specific to telecoms. Structured output 0, the card doesn't document JSON or JSON Schema output. Reasoning control 0, the card says the model wasn't trained to produce or consume Gemma's thought channel and answers directly, so there is no thinking to switch or budget. Context 10, 262,144 positions in the text config, the base's native context. 20 + 10 + 20 + 0 + 0 + 10 = 60.

DeepSeek V4.1 Flash 60

Memory scores nothing, because the only build is DeepSeek's own at 510.3 GB, about 612 GB with the fifth added, and nobody's runtime library holds a smaller one at 4-bit or better. Four runtimes run it in a released version (5 each), vLLM 0.33.0, SGLang 0.5.23, which maps the V4.1 architecture name onto its V4 model, llama.cpp from b11870 (25 October 2026), which added the deepseek41 architecture and its converter, and Ollama 0.40.14, which builds llama.cpp b11963. Transformers 5.22.1 and LM Studio don't (0 each). The release itself is one quantised build, FP8 with FP4 experts (10). MIT with nothing added earns the full terms line (15), the repositories aren't gated (10), and Hugging Face maps three live inference providers for the update (5). 0 + 20 + 10 + 15 + 10 + 5 = 60.

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 the reference machine's GPU share. Runtimes 15, the architecture is Gemma4ForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors files, three at 5 each. Builds 0, the publisher ships the BF16 weights only. Terms 15, Apache 2.0 in the card's metadata, an OSI licence, with nothing added on the card. Download 10, gated is false on 10 November 2026, so the files download with no account or terms. Hosted 0, Hugging Face maps one inference provider, Featherless AI (live), short of the two the line needs. 10 + 15 + 0 + 15 + 10 + 0 = 50.

DeepSeek V4.1 Flash 50

The weights are safetensors only, but 10 comes off because the card's way to build prompts is running encoding.py from the repository, since there's no Jinja template. The files sit in DeepSeek's own organisation with a hash for every file. Nothing is published on safety and there's no guidance for agent use, so those two lines score 0. The security programme earns 5 for the security@deepseek.com reporting address in DeepSeek's site footer, read as for DeepSeek-V3.2, with no security.txt and no public advisories. The MIT licence carries no use policy, threshold or revocation, so licence risk keeps all 15.

OTel-2.0-LLM-31B-IT 55

File safety 25, safetensors only, with no .bin, .pt or .pth files and no Python files. Provenance 5, the files sit in a personal account, farbodtavakkoli, not in an organisation AT&T's or GSMA's site links to: GSMA's article names AT&T as the releaser without linking the repository, and the GitHub repository that links it is the same person's (0). Safety evaluation 0, no safety results on the card. Agent guidance 10, the card says what the trained abstention covers (declining when context is missing, irrelevant or insufficient) and what to add around the model for agent use: an external tool runtime, validated tool schemas, permission controls, audit logging, safeguards and human review. Licence risk 15, Apache 2.0 has no use policy, field-of-use limit, threshold or change clause, nothing is added on the card, and the licence link resolves (200 on 10 November 2026). Security programme 0, no way to report a flaw found. 25 + 5 + 0 + 10 + 15 + 0 = 55.