# Kolibri-1 vs Nemotron 3 Nano 4B

> Kolibri-1 scores 61 (C) to Nemotron 3 Nano 4B's 56.8 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-nano-4b
- Markdown: https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-nano-4b.md (~6,150 tokens)
- Slim: https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-nano-4b.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-nano-4b.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

Kolibri-1 scores 61 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 2 of 4 scored categories. Nemotron 3 Nano 4B leads on reach. Both do open-weight models you run yourself.

- Kolibri-1: grade C, 61/100, rank #38 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/kolibri-1.md · JSON https://www.anchorterminal.com/api/v1/tools/kolibri-1.json
- Nemotron 3 Nano 4B: grade C, 56.8/100, rank #73 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-nano-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-4b.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

## Which one, for what

### Kolibri-1 (C)

Good for: German and English agent work with tools on a machine with about 96 GB of accelerator memory and vLLM, where a permissive licence matters.

Ahead on:
- Agent interface, 90 against 70
- Documentation and upkeep, 70 against 55

Watch for: The smallest publisher build is FP8 at 78.8 GB, so it needs about 95 GB and won't fit a 64 GB machine

### Nemotron 3 Nano 4B (C)

Good for: Agents on laptops, edge boards and small GPUs that need a cheap local tool-calling helper in English

Ahead on:
- Reach, 90 against 60

Watch for: The card's Transformers and vLLM commands pass trust_remote_code and load a parser plugin from the repository


## Score by category

| Category | Weight | Kolibri-1 | Nemotron 3 Nano 4B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 60 | 90 | Nemotron 3 Nano 4B +30 |
| Risk | 20% (20 this run) | 65 | 65 | even |
| Agent interface | 10% (10 this run) | 90 | 70 | Kolibri-1 +20 |
| Documentation and upkeep | 10% (10 this run) | 70 | 55 | Kolibri-1 +15 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **61 · C** | **56.8 · C** | |

## Facts side by side

| Fact | Kolibri-1 | Nemotron 3 Nano 4B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Aleph Alpha | NVIDIA |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | NVIDIA Nemotron Open Model License |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-19 | 2026-03-16 |
| Terms last updated | no document linked | 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 | 3.3/5 (4) | 3.3/5 (4) |

## Verdicts

**Kolibri-1.** Kolibri-1 is a German and English reasoning model with a documented tool-call format, switchable thinking and a 262,144-token context, under Apache 2.0. Aleph Alpha's FP8 build needs about 95 GB and runs on vLLM through its plugin, Transformers 5.20 or recent llama.cpp. The main risk is no published safety results or security contact, and few repository answers.

**Nemotron 3 Nano 4B.** A tiny reasoning cousin with trained tool calls, a thinking switch and 262,144 tokens of context, running in about 4 GB from NVIDIA's own GGUF on almost any machine. The main risks are serving commands that run repository code, no published safety results, a tool format left to the template, and files untouched and questions unanswered since March.

## Before you call either

### Kolibri-1

1. Start it with pip install 'aleph-alpha-inference>=1' then vllm serve Aleph-Alpha/Kolibri-1 --kv-cache-dtype fp8 --reasoning-parser kolibri1 --tool-call-parser kolibri1 --enable-auto-tool-choice
2. Without the kolibri1 tool-call parser and enable-auto-tool-choice, tool calls come back as text in tool_call tags
3. The plugin supports one vLLM minor version, 0.29, so don't upgrade vLLM while it's installed
4. Thinking is on by default at high effort. Pass reasoning_effort none or enable_thinking false in chat_template_kwargs for short tasks
5. Recommended sampling is temperature 1.0, top_p 0.97 and top_k 128
6. Past 262,144 tokens, add --max-model-len 1048576 and the max_position_embeddings override in --hf-overrides, though Aleph Alpha recommends staying at or below 262,144
7. With Transformers 5.20 or later, tokenizer.parse_response returns the answer in content and the reasoning in thinking
8. On the reference machine the FP8 build fits by size, but no document confirms vLLM runs FP8 on gfx1151, and Aleph Alpha ships no GGUF for llama.cpp
9. Validate tool results in your own code, as the card asks

### Nemotron 3 Nano 4B

1. Quickest start is `ollama run nemotron-3-nano:4b`, a 2.8 GB build
2. With llama.cpp use `llama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja`, NVIDIA's own Q4_K_M file
3. With vLLM, download nano_v3_reasoning_parser.py from the repository first and serve with `--enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3`
4. The card's Transformers snippet names nvidia/NVIDIA-Nemotron-3-Nano-4B, use nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 instead
5. Thinking is on by default. Pass `enable_thinking=False` to the chat template for short tasks
6. Use temperature 1.0 and top_p 0.95 for reasoning and temperature 0.6 with top_p 0.95 for tool calling, as the card says
7. It's tuned for English only, and validate JSON yourself, since structured output isn't documented

## Questions

### Which is better for AI agents, Kolibri-1 or Nemotron 3 Nano 4B?

Kolibri-1 scores 61 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 2 of 4 scored categories. Nemotron 3 Nano 4B leads on reach.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-nano-4b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-nano-4b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "kolibri-1", "b": "nemotron-3-nano-4b"}`. From a terminal: `anchor compare kolibri-1 nemotron-3-nano-4b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/kolibri-1.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-4b.json

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