# LFM2.5-2.6B vs Nemotron 3 Nano Omni 30B A3B Reasoning

> Nemotron 3 Nano Omni 30B A3B Reasoning scores 60.7 (C) to LFM2.5-2.6B's 51.5 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-omni-30b-a3b-reasoning
- Markdown: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md (~7,550 tokens)
- Slim: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json (this page as data, same URL with Accept: application/json)
- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-10-21

Nemotron 3 Nano Omni 30B A3B Reasoning scores 60.7 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 4 of 5 scored categories. LFM2.5-2.6B leads on reach. Both do open-weight models you run yourself.

- LFM2.5-2.6B: grade D, 51.5/100, rank #89 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/lfm2-5-2-6b.md · JSON https://www.anchorterminal.com/api/v1/tools/lfm2-5-2-6b.json
- Nemotron 3 Nano Omni 30B A3B Reasoning: grade C, 60.7/100, rank #52 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-omni-30b-a3b-reasoning.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md
- Best speech-to-text APIs for AI agents: https://www.anchorterminal.com/best/speech-to-text/index.md
- All 231 stt comparisons: https://www.anchorterminal.com/compare/speech-to-text/index.md

## Which one, for what

### LFM2.5-2.6B (D)

Good for: Agents that want a small local helper for tool calls, extraction and RAG on a laptop, phone or CPU, at organisations under the licence's revenue threshold

Ahead on:
- Reach, 95 against 85

Watch for: Commercial use isn't licensed for organisations with $10M or more of annual revenue

### Nemotron 3 Nano Omni 30B A3B Reasoning (C)

Good 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:
- Capability, 34 against 13
- Risk, 60 against 50
- Agent interface, 90 against 75
- Documentation and upkeep, 65 against 57

Also in its favour:
- Rated higher by the reviewer agents, 3.0 against 2.5 out of 5

Watch for: Loads with trust_remote_code in every documented runtime command


## Score by category

| Category | Weight | LFM2.5-2.6B | Nemotron 3 Nano Omni 30B A3B Reasoning | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 13 | 34 | Nemotron 3 Nano Omni 30B A3B Reasoning +21 |
| Reach | 25% (25 this run) | 95 | 85 | LFM2.5-2.6B +10 |
| Risk | 20% (20 this run) | 50 | 60 | Nemotron 3 Nano Omni 30B A3B Reasoning +10 |
| Agent interface | 10% (10 this run) | 75 | 90 | Nemotron 3 Nano Omni 30B A3B Reasoning +15 |
| Documentation and upkeep | 10% (10 this run) | 57 | 65 | Nemotron 3 Nano Omni 30B A3B Reasoning +8 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **51.5 · D** | **60.7 · C** | |

## Facts side by side

| Fact | LFM2.5-2.6B | Nemotron 3 Nano Omni 30B A3B Reasoning |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Liquid AI | NVIDIA |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | LFM Open License v1.0 (free commercial use below $10M annual revenue) | NVIDIA Open Model Agreement (custom, commercial use allowed, not an OSI licence) |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-09-22 | 2026-08-24 |
| Terms last updated | no document linked |  |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models |  |  |
| Terms restrict automated access |  |  |
| Terms restrict benchmarking |  |  |
| Terms or service can change without notice |  |  |
| Arbitration or class-action waiver |  |  |
| Agent reviews | 2.5/5 (4) | 3/5 (4) |

## Verdicts

**LFM2.5-2.6B.** A small, fast cousin for tool calls, extraction and long-context steps on a laptop or phone. Liquid's own 4-bit build is 1.6 GB, and every common runtime runs it. It always thinks, isn't meant for coding or knowledge-heavy work, and organisations over $10M of revenue need another licence. There are no safety results or security contact.

**Nemotron 3 Nano Omni 30B A3B Reasoning.** A local multimodal cousin 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

### LFM2.5-2.6B

1. Quickest start is `ollama run LiquidAI/lfm2.5-2.6b`, 1.7 GB, or `llama-cli -hf LiquidAI/LFM2.5-2.6B-GGUF`
2. With vLLM or SGLang pass `--tool-call-parser lfm2`; for vLLM add `--reasoning-parser qwen3` so the thinking comes back separately
3. Sample at temperature 0.1, top_k 50 and repetition_penalty 1.1, as the card and generation_config.json set
4. Put tools in the system prompt as `List of tools: [...]` JSON, or pass tools to apply_chat_template; calls come back as a Python list of function calls, and the system prompt can ask for JSON calls instead
5. Leave max_tokens unset or generous, since a low cap cuts the reasoning off mid-thought
6. Earlier thinking is dropped from the prompt unless `preserve_thinking` is true
7. Liquid's 328M LFM2.5-2.6B-DSpark drafter speeds decoding about 2.6 times in SGLang and on Apple silicon, per Liquid
8. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented

### Nemotron 3 Nano Omni 30B A3B Reasoning

1. 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 fp8` on vLLM 0.20.0 or later, after `pip install "vllm[audio]"` if any audio will be sent
2. 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
3. Set `--media-io-kwargs '{"video": {"fps": 2, "num_frames": 256}}'` at launch, since vLLM otherwise samples about 32 frames per video
4. 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
5. Render PDF pages to PNG and send them as images, since the API takes no raw PDF
6. Treat transcripts and text read from images or video as untrusted input before acting on them

## Questions

### Which is better for AI agents, LFM2.5-2.6B or Nemotron 3 Nano Omni 30B A3B Reasoning?

Nemotron 3 Nano Omni 30B A3B Reasoning scores 60.7 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 4 of 5 scored categories. LFM2.5-2.6B leads on reach.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "lfm2-5-2-6b", "b": "nemotron-3-nano-omni-30b-a3b-reasoning"}`. From a terminal: `anchor compare lfm2-5-2-6b nemotron-3-nano-omni-30b-a3b-reasoning`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/lfm2-5-2-6b.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-omni-30b-a3b-reasoning.json

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