Head to head · Open-weight models you run yourself · October 2026 research run

Nemotron 3 Super 120B A12B vs OTel-2.0-LLM-31B-IT

Nemotron 3 Super 120B A12B scores 66.7 (B) on agent readiness against OTel-2.0-LLM-31B-IT's 56.8 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.

Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons

Which one, for what

Nemotron 3 Super 120B A12B B

Best for Agents on a 128 GB unified machine or Blackwell GPUs that want a long-context, tool-calling reasoning model with documented training data

Ahead on

  • Reach, 75 against 50
  • Risk, 65 against 55
  • Agent interface, 90 against 60

Watch for

The card's serving commands pass --trust-remote-code

OTel-2.0-LLM-31B-IT C

Best for Telecoms teams that want an open model for standards questions and RAG on their own hardware

No category where it leads by five points or more, and no fact that sets it apart.

Watch for

BF16 only at 62.6 GB, with no GGUF or other quantised build from anyone we checked

Why the edge

Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.

Agent interface Nemotron 3 Super 120B A12B +30

Nemotron 3 Super 120B A12B 90

The chat template earns 20, a Jinja file in the repository. The tool-calling format earns 25, since NVIDIA documents the format, names the qwen3_coder parser for three runtimes and shows a tool call in its cookbook. Trained tool calling earns 20, with tool-use reinforcement learning and several calls per turn in the template. Structured output earns 5, claimed from training with no instructions. Reasoning control earns 10, thinking off, low effort or a budget per request with the trace returned as reasoning_content. Context earns 10 for 262,144 tokens in the config and up to 1M per the card.

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.

Reach Nemotron 3 Super 120B A12B +25

Nemotron 3 Super 120B A12B 75

Memory earns 10, because the smallest 4-bit build, NVIDIA's 80.3 GB NVFP4, needs about 96 GB with the fifth added, which fits the reference machine but not 64 GB, and Ollama's 87 GB build lands in the same band. Runtimes earn the full 25 from released Transformers, vLLM, SGLang, llama.cpp and Ollama. Builds earn 15 for NVIDIA's own FP8 and NVFP4. Terms earn 10, since the Nemotron licence is custom even though it's free for commercial use with no threshold or use policy. Download earns 10, with no gate or account. Hosted earns 5, since NVIDIA's own API serves it.

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.

Risk Nemotron 3 Super 120B A12B +10

Nemotron 3 Super 120B A12B 65

File safety earns 15, safetensors only but with 10 off because the documented serving commands pass trust-remote-code and the repository ships model and parser code. Provenance earns 15, files in NVIDIA's linked organisation with a hash for every file. Safety evaluation earns 0, since NVIDIA says it evaluated content safety but publishes no results. Agent guidance earns 10, a subcard that names prompt injection and says what safeguards to add, without an instruction hierarchy or concrete mitigations. Licence risk keeps all 15. The security programme earns 10 for the vulnerability submission route on the card and NVIDIA's public bulletins.

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.

Score by category

CategoryWeight this runNemotron 3 Super 120B A12BOTel-2.0-LLM-31B-ITEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%257550Nemotron 3 Super 120B A12B +25
Risk20%206555Nemotron 3 Super 120B A12B +10
Agent interface10%109060Nemotron 3 Super 120B A12B +30
Documentation and upkeep10%107074OTel-2.0-LLM-31B-IT +4
Negative events≤1500
TotalB 66.7/100C 56.8/100

Facts side by side

FactNemotron 3 Super 120B A12BOTel-2.0-LLM-31B-IT
KindOpen-weight modelOpen-weight model
VendorNVIDIAAT&T (Open Telco AI)
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceNVIDIA Nemotron Open Model LicenseApache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-03-112026-11-01
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviews2.8/5 (4)none

How the scores have moved

Verdicts

Nemotron 3 Super 120B A12B

A long-context agent cousin with trained parallel tool calls, a reasoning switch and a full account of its training data, under a free commercial licence. It needs about 100 GB of GPU memory, a 128 GB unified machine or Blackwell hardware for NVIDIA's NVFP4. The main risks are serving commands that run repository code and no published safety results.

OTel-2.0-LLM-31B-IT

A real telecoms post-training of Gemma 4 31B IT with an unusually candid card on what was and wasn't trained. It fits a 128 GB machine in BF16 only, with no quantised build, sits in a personal account, and its weights have been replaced in place. Tool-call format, structured output and safety results aren't documented.

Before you call either

Nemotron 3 Super 120B A12B

  1. On the reference machine start it with ollama run nemotron-3-super, the 87 GB q4_K_M build, the only one that fits (about 104 GB with the margin)
  2. Raise the Strix Halo GPU memory limit first with amd-ttm --set 100, since the default of about 62.8 GiB is far below what it needs
  3. With vLLM serve it with --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder (SGLang uses --reasoning-parser nemotron_3)
  4. Use temperature=1.0 and top_p=0.95 for every task, as the card says
  5. Thinking is on by default. Pass enable_thinking: false or low_effort: true in chat_template_kwargs for short tasks
  6. For coding agents pass force_nonempty_content: true in chat_template_kwargs, so a cut-off reasoning trace still returns content
  7. Raise context past 262,144 only with the card's flags (VLLM_ALLOW_LONG_MAX_MODEL_LEN=1, --max-model-len 1048576)

OTel-2.0-LLM-31B-IT

  1. Download the 15 shards the index names, not the whole repository, which is twice the size
  2. Pin a revision. The card says the weights may change under the same name
  3. Serve it with vLLM 0.19.0 or later and the repository's own chat template, not Gemma 4's
  4. Send no thinking flags. It answers directly and wasn't trained on Gemma's thought channel
  5. Use it as the generation stage of a RAG pipeline over telecom standards, with source attribution and human review
  6. Don't use it to run network tools unattended. The card says that hasn't been evaluated

Questions

Which is better for AI agents, Nemotron 3 Super 120B A12B or OTel-2.0-LLM-31B-IT?

Nemotron 3 Super 120B A12B scores 66.7 (B) on agent readiness against OTel-2.0-LLM-31B-IT's 56.8 (C), and leads in 3 of 4 scored categories.

Other comparisons with Nemotron 3 Super 120B A12B or OTel-2.0-LLM-31B-IT

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