Head to head · Open-weight models you run yourself · October 2026 research run
Cola2 vs Nemotron 3 Nano Omni 30B A3B Reasoning
Nemotron 3 Nano Omni 30B A3B Reasoning scores 61.2 (C) on agent readiness against Cola2's 33.9 (F), and leads in every scored category. Both do open-weight models you run yourself.
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Which one, for what
Cola2 F
Best for Researchers working on latent or diffusion language models who want a checkpoint to study
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
No licence stated for the weights
Nemotron 3 Nano Omni 30B A3B Reasoning C
Best for Agents with a 32 to 48 GB NVIDIA GPU that need one local model to read video, audio, documents and screens and call tools
Ahead on
- Reach, 85 against 25
- Risk, 60 against 40
- Agent interface, 90 against 20
- Documentation and upkeep, 70 against 58
Watch for
Loads with trust_remote_code in every documented runtime command
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 Nano Omni 30B A3B Reasoning +70
Cola2 20
Chat template 20, A chat template in tokenizer_config.json (4,116 characters), which came with the Qwen3 tokenizer. Tool-calling format 0, none. Trained tool calling 0, not trained for tool calling. Structured output 0, Nothing about JSON or schema-constrained output. Reasoning control 0, no reasoning mode. Context 0, no context length is stated. 20 + 0 + 0 + 0 + 0 + 0 = 20.
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).
Reach Nemotron 3 Nano Omni 30B A3B Reasoning +60
Cola2 25
Memory 15, the only build is the F32 safetensors set, 35.07 GB, and 35.07 GB × 1.2 = 42.1 GB, within 64 GB. Loaded as BF16, as the card's example does, it needs about half, but the line reads the file size. Runtimes 0, none of the six runs it in a released version. Builds 0, one set of weights and no quantised build. Terms 0, no licence for the weights. Download 10, the Hub API reports gated false 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, and nothing names an API that serves it. 15 + 0 + 0 + 0 + 10 + 0 = 25.
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.
Risk Nemotron 3 Nano Omni 30B A3B Reasoning +20
Cola2 40
File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the ByteDance-Seed organisation, which the ByteDance-Seed GitHub repository for the model links to (10). Safety evaluation 0, no safety results. Agent guidance 5, General cautions in the GitHub model card: not instruction-tuned or safety-aligned, not for use as an assistant, for facts or advice, or for high-impact decisions about people. Licence risk 0, With no licence for the weights there is nothing to hold ByteDance to. Security programme 5, the GitHub repository's SECURITY.md sends reports to ByteDance's security centre and says how they're assessed (5). 15 + 15 + 0 + 5 + 0 + 5 = 40.
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.
Documentation and upkeep Nemotron 3 Nano Omni 30B A3B Reasoning +12
Cola2 58
Model card 8, Intended use and limitations (the out-of-scope list, and the unconverged latent model) and the architecture are on the card and the GitHub model card it links. Training data 15, the GitHub model card names the source (OLMo 2 pretraining text, retokenised) and the mix by stage with token counts, 346.03B in all. Last change 20, All six commits are from 6 November 2026, four days before this check, the last one updating a checksum in the README. Responsiveness 0, no discussions on the repository, so nobody has asked anything yet and the line reads as none answered, as settled in calibration. Version policy 5, A first release. File health 10, Config, files and card agree: F32 weights, a float32 config and a card that says to load them as bfloat16, with SHA256SUMS to check the download. 8 + 15 + 20 + 0 + 5 + 10 = 58.
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.
Score by category
| Category | Weight this run | Cola2 | Nemotron 3 Nano Omni 30B A3B Reasoning | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 25 | 85 | Nemotron 3 Nano Omni 30B A3B Reasoning +60 |
| Risk | 20%20 | 40 | 60 | Nemotron 3 Nano Omni 30B A3B Reasoning +20 |
| Agent interface | 10%10 | 20 | 90 | Nemotron 3 Nano Omni 30B A3B Reasoning +70 |
| Documentation and upkeep | 10%10 | 58 | 70 | Nemotron 3 Nano Omni 30B A3B Reasoning +12 |
| Negative events | ≤15 | 0 | 0 | |
| Total | F 33.9/100 | C 61.2/100 |
Facts side by side
| Fact | Cola2 | Nemotron 3 Nano Omni 30B A3B Reasoning |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | ByteDance (Seed) | NVIDIA |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | None stated for the weights (the code is Apache 2.0) | NVIDIA Open Model Agreement (custom, commercial use allowed, not an OSI licence) |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-11-06 | 2026-08-24 |
| Terms last updated | no document linked | no date given |
| Privacy policy last updated | no document linked | no document linked |
| 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 | 3/5 (4) |
How the scores have moved
Cola2
No score change since it was listed.
Verdicts
Cola2
A research release, and an honest one: ByteDance Seed says it's a base model with an unconverged latent stage, scoring below Llama-3-8B on its own table. It's here for people studying continuous-latent generation. Agents can't use it as a model: 32-token completions, no chat or tools, the repository's own code to load it, and no licence on the weights.
Nemotron 3 Nano Omni 30B A3B Reasoning
A local multimodal model for video with speech, long documents, charts, screens and English transcription, with tool calling, a reasoning switch and a 262,144-token context. It needs about 27 GB for NVIDIA's NVFP4 build on Blackwell, or 42 GB for FP8. The main risks are the remote code it loads and a repository where nobody answers questions.
Before you call either
Cola2
- Don't plan to use this as your model. It completes short prompts and wasn't tuned to follow instructions
- Don't use it commercially on the strength of this listing. The weights have no stated licence
- If you study it, read modeling_cola2.py first: trust_remote_code runs it
- Pin Transformers below 5, as the card requires
- Pass a seeded torch.Generator to generate(). Output varies between runs even with greedy decoding
- Check the download with
sha256sum -c SHA256SUMS
Nemotron 3 Nano Omni 30B A3B Reasoning
- Start it with
vllm serve nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8 --max-model-len 131072 --trust-remote-code --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder --kv-cache-dtype fp8on vLLM 0.20.0 or later, afterpip install "vllm[audio]"if any audio will be sent - Or run
ollama run nemotron3; the 33b-nvfp4 and 33b-mlx tags are MLX builds for Apple silicon, and the q4_K_M tag is 28 GB - Set
--media-io-kwargs '{"video": {"fps": 2, "num_frames": 256}}'at launch, since vLLM otherwise samples about 32 frames per video - Send
chat_template_kwargs: {"enable_thinking": false}for transcription and quick answers, and use temperature 1.0 with top_k 1 for ASR as the card recommends - Render PDF pages to PNG and send them as images, since the API takes no raw PDF
- Treat transcripts and text read from images or video as untrusted input before acting on them
Questions
Which is better for AI agents, Cola2 or Nemotron 3 Nano Omni 30B A3B Reasoning?
Nemotron 3 Nano Omni 30B A3B Reasoning scores 61.2 (C) on agent readiness against Cola2's 33.9 (F), and leads in every scored category.
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/compare/cola2-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/cola2.json·/api/v1/tools/nemotron-3-nano-omni-30b-a3b-reasoning.json - From a terminal
anchor compare cola2 nemotron-3-nano-omni-30b-a3b-reasoning(the CLI) - Over MCP
compare_tools {"a": "cola2", "b": "nemotron-3-nano-omni-30b-a3b-reasoning"}at/mcp, no key