Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen-AgentWorld-35B-A3B

Nemotron 3 Nano Omni 30B A3B Reasoning 90

A Jinja template ships in the repository (20). The card names the qwen3_coder parser and NVIDIA's cookbooks show tools declared with the OpenAI schema and tool_calls read back (25). The template renders several calls per turn and the card says it supports tool calling (20). JSON output is claimed with no word on how to ask for it (5). Thinking turns off per request, a budget caps it and the parser returns the reasoning apart (10). The context is 262,144 tokens, trained in (10).

Qwen-AgentWorld-35B-A3B 50

Chat template 20, chat_template.jinja in the repository, the same as the template in tokenizer_config.json. Tool-calling format 10, only the template says how tools are declared and how calls come back. Trained tool calling 10, the template renders several tool_call blocks in one turn, but the card presents the model as a simulator of what tools and environments return, not as a tool caller. Structured output 0, no JSON or JSON Schema output documented for the model. Reasoning control 0, the card says thinking is on by default and names the qwen3 reasoning parser, but doesn't say how to turn thinking off or budget it. Context 10, 262,144 tokens native per the card and the text config. 20 + 10 + 10 + 0 + 0 + 10 = 50.

Nemotron 3 Nano Omni 30B A3B Reasoning 85

Memory earns 20 because the smallest 4-bit builds, Ollama's NVFP4 at 21 GB and NVIDIA's NVFP4 at 22.4 GB, need about 25 to 27 GB with the fifth added, which fits 32 GB but not 24. Runtimes reach the cap of 25 with released code in vLLM, SGLang and llama.cpp and library entries in Ollama and LM Studio. NVIDIA's FP8 and NVFP4 builds give builds the full 15. The NVIDIA Open Model Agreement is a custom licence with free commercial use, so terms get 10. The ungated repositories give download the full 10, and NVIDIA's own API serving it gives hosted too its 5.

Qwen-AgentWorld-35B-A3B 55

Memory 10, Alibaba's only build is the BF16 safetensors, 69.32 GB, and 69.32 GB × 1.2 = 83.2 GB, above 64 GB and inside the reference machine's GPU share. Runtimes 20, Four released runtimes run the architecture, vLLM 0.33.0 (Qwen3_5MoeForConditionalGeneration), SGLang 0.5.23, Transformers 5.22.2 (qwen3_5_moe) and llama.cpp b12242 (its Qwen conversion code). Builds 0, Alibaba publishes the BF16 weights only. Terms 15, Apache 2.0, an OSI licence, in the metadata and as a LICENSE file, with no use policy or other terms added. Download 10, the repository isn't gated, so the files download with no account and no terms to accept. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026). 10 + 20 + 0 + 15 + 10 + 0 = 55.

Nemotron 3 Nano Omni 30B A3B Reasoning 70

The card covers all seven fields, reading the 2019 to 2025 data collection period as the cut-off (20). Training data is listed with sources and sizes by dataset and one set is published (20). The Hub files last changed on 24 and 25 August 2026, and NVIDIA's TensorRT-LLM, which the card names for serving, changed the Nemotron-H model code on 14 October 2026, 20 days back (20). None of the 11 questions across the three repositories has a reply (0). It's a first release with no version policy stated (5). The shipped Jinja template and the tokenizer config's template differ, and reports about the template and a modeling file wait unanswered (5). 20 + 20 + 20 + 0 + 5 + 5 = 70.

Qwen-AgentWorld-35B-A3B 41

Model card 11, the card gives intended use (simulating agent environments), architecture, parameters and context length. Training data 5, A general description without a cut-off, the three training stages and a statement that no outputs from external API services were included. Last change 10, the last commit was on 25 June 2026, an update to the card, 138 days before this check, past 90 days and within 180. Responsiveness 0, 10 discussions that aren't pull requests, opened between 24 June and 5 July 2026. Version policy 5, A first release, with nothing replaced. File health 10, Config, both copies of the template and the card agree on the architecture and the context. 11 + 5 + 10 + 0 + 5 + 10 = 41.

Nemotron 3 Nano Omni 30B A3B Reasoning 60

File safety gets 15, safetensors only but loaded with trust_remote_code. Provenance gets 15 for files in NVIDIA's own organisation with a SHA-256 for every shard. The agreement has no use policy, threshold or change clause and the grant is irrevocable, so licence risk keeps 15. NVIDIA's PSIRT form on the card and its public bulletins give the security programme 10. Agent guidance gets 5 for general cautions, and safety evaluation scores 0 because the subcards name the safety tests without publishing results.

Qwen-AgentWorld-35B-A3B 55

File safety 25, 21 safetensors shards and no pickle or Python files, and config.json has no auto_map. Provenance 15, files sit in the Qwen organisation, the one Alibaba's Qwen repositories on GitHub link to per the Qwen3.6-35B-A3B listing (10), and the Hub gives a SHA-256 for every large file (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, no prompt-injection guidance, instruction hierarchy, refusal statement or general cautions on the card. Licence risk 15, Apache 2.0 has no use policy, no threshold and no terms the licensor can change or revoke, and the card's licence link points at the LICENSE file in the repository. Security programme 0, no reporting route on the card. 25 + 15 + 0 + 0 + 15 + 0 = 55.