# Nemotron 3 Nano 30B A3B vs Recursive Flint

> Nemotron 3 Nano 30B A3B scores 67.1 (B) to Recursive Flint's 61 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-recursive-flint
- Markdown: https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-recursive-flint.md (~6,800 tokens)
- Slim: https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-recursive-flint.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-recursive-flint.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: 2026-10-22

Nemotron 3 Nano 30B A3B scores 67.1 (B) on agent readiness against Recursive Flint's 61 (C), and leads in 2 of 4 scored categories. Both do open-weight models you run yourself.

- Nemotron 3 Nano 30B A3B: grade B, 67.1/100, rank #12 of 107 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-30b-a3b.json
- Recursive Flint: grade C, 61/100, rank #49 of 107 open-weight models. Markdown https://www.anchorterminal.com/tools/recursive-flint.md · JSON https://www.anchorterminal.com/api/v1/tools/recursive-flint.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 3023 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### Nemotron 3 Nano 30B A3B (B)

Good for: Agents with a 24 to 32 GB GPU or Mac that want a quick tool-calling and reasoning cousin with very long context.

Ahead on:
- Reach, 90 against 75
- Documentation and upkeep, 75 against 67

Watch for: The documented vLLM, SGLang and TensorRT-LLM commands pass trust-remote-code and load a parser file from the repository

### Recursive Flint (C)

Good for: Agents doing repository work on a 32 to 48 GB Mac or NVIDIA box that want a fast sparse coder with documented tool calls and a plain licence.

Watch for: No independent benchmark results yet, every published score is Recursive's


## Score by category

| Category | Weight | Nemotron 3 Nano 30B A3B | Recursive Flint | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 90 | 75 | Nemotron 3 Nano 30B A3B +15 |
| Risk | 20% (20 this run) | 60 | 60 | even |
| Agent interface | 10% (10 this run) | 90 | 90 | even |
| Documentation and upkeep | 10% (10 this run) | 75 | 67 | Nemotron 3 Nano 30B A3B +8 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **67.1 · B** | **61 · C** | |

## Facts side by side

| Fact | Nemotron 3 Nano 30B A3B | Recursive Flint |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | NVIDIA | Recursive |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | NVIDIA Nemotron Open Model License (custom, Apache-style, commercial use allowed, not an OSI licence) | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-08-24 | 2026-10-21 |
| 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 | 3.3/5 (4) | 3.5/5 (4) |

## Verdicts

**Nemotron 3 Nano 30B A3B.** A fast hybrid Mamba-Transformer cousin with 3.5B active parameters for reasoning and tool calling over very long inputs, in every major local runtime. A 4-bit build needs about 22 to 23 GB. The main risks are that NVIDIA's documented vLLM path runs repository code, no safety results are published and NVIDIA hasn't answered reports since March.

**Recursive Flint.** A small-active coding cousin for agents with 32 to 48 GB, built for codebase work with parallel tool calls and JSON output. It needs llama.cpp b11803 or Ollama 0.40.8 or later, and the BF16 checkpoint needs a 141 GB GPU. Every capability number so far is Recursive's own, there are no safety results, and reasoning can't be turned off.

## Before you call either

### Nemotron 3 Nano 30B A3B

1. Start it with `ollama run nemotron-3-nano:30b` (18 GB MLX on a Mac, 24 GB q4_K_M elsewhere)
2. On the reference machine use BF16 with vLLM, after `wget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/resolve/main/nano_v3_reasoning_parser.py`, then `vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 --max-model-len 262144 --trust-remote-code --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3`
3. NVFP4 needs an NVIDIA Blackwell GPU, and FP8 isn't documented on gfx1151
4. Without the qwen3_coder tool parser and the nano_v3 reasoning parser, tool calls and reasoning come back as plain text
5. Card sampling is temperature 0.6 and top_p 0.95 for tool calling, temperature 1.0 and top_p 1.0 for reasoning
6. Thinking is on by default, so pass enable_thinking as false in chat_template_kwargs for short tasks
7. Set VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 and raise --max-model-len only when you need more than 262,144 tokens
8. In SGLang the main branch names the reasoning parser nemotron_3, not the card's nano_v3
9. Filter untrusted tool results before they reach the model, as NVIDIA warns prompt injection can lead to code execution
10. Validate JSON output yourself or use the runtime's guided decoding, since none is documented for the model

### Recursive Flint

1. Quickest start is `ollama run flint` (Ollama 0.40.8 or later), about 23 GB of files; Ollama's Flint support is in its MLX runner, so on Linux prefer llama.cpp
2. With llama.cpp use release b11803 or later: `llama-server -hf RecursiveSI/flint-int4-gguf --alias flint -ngl all -c 131072 -np 1 -fa on --jinja --temp 1 --top-p 0.95 --top-k 20 --min-p 0`, and keep --jinja on
3. Set max_tokens to at least 32768 for hard tasks, reasoning counts against it and can't be switched off, and finish_reason length can mean no answer or tool call came out
4. Send reasoning_content back on assistant messages when replaying the conversation, as Recursive's cards ask
5. Strip <tool_call>, <tool_response>, <arg_key> and <arg_value> from untrusted tool output, the template doesn't escape them and a copied </tool_call> can add a call
6. Message content must be a string, flatten content-part arrays first
7. Recursive's SGLang servers listen on 0.0.0.0 with no key, publish the port on localhost only or pass --api-key

## Questions

### Which is better for AI agents, Nemotron 3 Nano 30B A3B or Recursive Flint?

Nemotron 3 Nano 30B A3B scores 67.1 (B) on agent readiness against Recursive Flint's 61 (C), and leads in 2 of 4 scored categories.


## For agents

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

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