Head to head · Open-weight models you run yourself · October 2026 research run
Nemotron 3 Super 120B A12B vs North Mini Code 1.0
Nemotron 3 Super 120B A12B scores 66.7 (B) on agent readiness against North Mini Code 1.0's 61 (C), and leads in 3 of 4 scored categories. North Mini Code 1.0 leads on reach. 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
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
- Risk, 65 against 60
- Agent interface, 90 against 85
- Documentation and upkeep, 70 against 36
Watch for
The card's serving commands pass --trust-remote-code
Best for Developers who want a coding agent on their own GPU with a permissive licence
Ahead on
- Reach, 85 against 75
Watch for
No limitations, safety results, languages or data description on the card
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.
Documentation and upkeep Nemotron 3 Super 120B A12B +34
Nemotron 3 Super 120B A12B 70
The model card earns 20, all seven items with limitations in the linked subcard. Training data earns 20, datasets listed with sources and token counts and the corpora published. Last change earns 20, counting NVIDIA's own inference code for the model, as on Nemotron 3 Ultra and Nano. The repositories last changed with a card edit on 25 August 2026, but NVIDIA's TensorRT-LLM, which the card's serving commands use, changed its Nemotron-H model code on 14 October, 18 days ago. Responsiveness earns 0, with no NVIDIA reply in any of the nine discussions, re-counted on 1 November. Version policy earns 5 as a first release. File health earns 5, since a generation report waits without a reply and one card link is dead. 20+20+20+0+5+5 = 70.
North Mini Code 1.0 36
Model card 11, Intended use, the architecture, the parameters and the context are on the card. Training data 0, the card gives the training method and nothing about the data. Last change 10, the main repository last changed on 15 June 2026 (outdated chat templates removed), 148 days before this check, and the draft model's repository on 29 July, 104 days. Responsiveness 0, none of the 11 most recent discussions that aren't pull requests (7 June to 6 November 2026) has a reply from a CohereLabs member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release, with each build in its own repository. File health 10, Config, template and card agree on the architecture, and the June commit that removed broken templates says so. 11 + 0 + 10 + 0 + 5 + 10 = 36.
Reach North Mini Code 1.0 +10
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.
North Mini Code 1.0 85
Memory 25, the smallest build from Cohere is the w4a16 repository, 19.38 GB, and 19.38 GB × 1.2 = 23.3 GB, within 24 GB. Unsloth's GGUF isn't read for this line. Runtimes 20, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the cohere2_moe model, and llama.cpp b12242 has the cohere2moe architecture. Builds 15, Quantised builds from Cohere at two precisions, FP8 and 4-bit weights (w4a16), each in its own repository. Terms 15, Apache 2.0 in the metadata and on the card, an OSI licence with nothing added. Download 10, the Hub API reports gated false for the model and its builds on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 25 + 20 + 15 + 15 + 10 + 0 = 85.
Risk Nemotron 3 Super 120B A12B +5
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.
North Mini Code 1.0 60
File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in Cohere's verified CohereLabs organisation, which cohere.com links to per our other Cohere listings (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on prompt injection, instruction priority or what the model refuses. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 5, Cohere's trust centre holds a responsible disclosure policy with a bug bounty, as recorded on our Cohere listings (5). 25 + 15 + 0 + 0 + 15 + 5 = 60.
Agent interface Nemotron 3 Super 120B A12B +5
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.
North Mini Code 1.0 85
Chat template 20, chat_template.jinja in the repository (12,397 characters). Tool-calling format 25, the card documents how tools are declared (JSON schema through the chat template), how the call and its thinking go back into the history and how results are appended, and names the cohere_command4 tool-call parser for vLLM, which released vLLM has. Trained tool calling 20, the card says the model was specifically trained with tool-use capabilities for agentic coding, with reinforcement learning on verifiable tasks, and the template renders every call in a turn with thinking between them. Structured output 0, JSON schema is for describing tools. Reasoning control 10, the template has a reasoning_effort setting, and the card names the cohere_command4 reasoning parser, which returns thinking apart from the answer. Context 10, 256K tokens per the card, with 64K of output, and 500,000 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.
Score by category
| Category | Weight this run | Nemotron 3 Super 120B A12B | North Mini Code 1.0 | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 75 | 85 | North Mini Code 1.0 +10 |
| Risk | 20%20 | 65 | 60 | Nemotron 3 Super 120B A12B +5 |
| Agent interface | 10%10 | 90 | 85 | Nemotron 3 Super 120B A12B +5 |
| Documentation and upkeep | 10%10 | 70 | 36 | Nemotron 3 Super 120B A12B +34 |
| Negative events | ≤15 | 0 | 0 | |
| Total | B 66.7/100 | C 61/100 |
Facts side by side
| Fact | Nemotron 3 Super 120B A12B | North Mini Code 1.0 |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | NVIDIA | Cohere |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | NVIDIA Nemotron Open Model License | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-03-11 | 2026-07-29 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 2.8/5 (4) | none |
How the scores have moved
North Mini Code 1.0
No score change since it was listed.
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.
North Mini Code 1.0
A coding agent model that fits one 24 GB GPU at 4 bits, trained for tool use, with a 256K context, Apache 2.0 and four released runtimes. It's a research release and reads like one: nothing on limitations, safety or data, the card's run instructions are out of date, and Cohere hasn't answered a question on the repository.
Before you call either
Nemotron 3 Super 120B A12B
- 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) - 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 - With vLLM serve it with
--reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder(SGLang uses--reasoning-parser nemotron_3) - Use
temperature=1.0andtop_p=0.95for every task, as the card says - Thinking is on by default. Pass
enable_thinking: falseorlow_effort: trueinchat_template_kwargsfor short tasks - For coding agents pass
force_nonempty_content: trueinchat_template_kwargs, so a cut-off reasoning trace still returns content - Raise context past 262,144 only with the card's flags (
VLLM_ALLOW_LONG_MAX_MODEL_LEN=1,--max-model-len 1048576)
North Mini Code 1.0
- Serve it with vLLM 0.33.0 or later and
--tool-call-parser cohere_command4 --reasoning-parser cohere_command4 --enable-auto-tool-choice, withcohere_melodyinstalled - Pass the model's thinking back with its tool calls on every turn. The card says results drop without it
- Describe tools in JSON schema and append tool results as a dictionary with the tool role
- Use temperature 1.0 and top_p 0.95
- For llama.cpp the GGUF files are Unsloth's or bartowski's conversions, not Cohere's
- It's text only. Don't send images
Questions
Which is better for AI agents, Nemotron 3 Super 120B A12B or North Mini Code 1.0?
Nemotron 3 Super 120B A12B scores 66.7 (B) on agent readiness against North Mini Code 1.0's 61 (C), and leads in 3 of 4 scored categories. North Mini Code 1.0 leads on reach.
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Machine-readable
- This page as Markdown
/compare/nemotron-3-super-120b-a12b-vs-north-mini-code-1-0.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/nemotron-3-super-120b-a12b.json·/api/v1/tools/north-mini-code-1-0.json - From a terminal
anchor compare nemotron-3-super-120b-a12b north-mini-code-1-0(the CLI) - Over MCP
compare_tools {"a": "nemotron-3-super-120b-a12b", "b": "north-mini-code-1-0"}at/mcp, no key