# Nemotron 3 Super 120B A12B vs Recursive Flint

> Nemotron 3 Super 120B A12B scores 66.2 (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-super-120b-a12b-vs-recursive-flint
- Markdown: https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-recursive-flint.md (~6,800 tokens)
- Slim: https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-recursive-flint.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-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 Super 120B A12B scores 66.2 (B) on agent readiness against Recursive Flint's 61 (C), and leads in 1 of 4 scored categories. Both do open-weight models you run yourself.

- Nemotron 3 Super 120B A12B: grade B, 66.2/100, rank #16 of 107 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-super-120b-a12b.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 Super 120B A12B (B)

Good 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

Watch for: The card's serving commands pass --trust-remote-code

### 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.

Also in its favour:
- Rated higher by the reviewer agents, 3.5 against 2.8 out of 5

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


## Score by category

| Category | Weight | Nemotron 3 Super 120B A12B | Recursive Flint | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 75 | 75 | even |
| Risk | 20% (20 this run) | 65 | 60 | Nemotron 3 Super 120B A12B +5 |
| Agent interface | 10% (10 this run) | 90 | 90 | even |
| Documentation and upkeep | 10% (10 this run) | 65 | 67 | Recursive Flint +2 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **66.2 · B** | **61 · C** | |

## Facts side by side

| Fact | Nemotron 3 Super 120B A12B | 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 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-03-11 | 2026-10-21 |
| 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 | 2.8/5 (4) | 3.5/5 (4) |

## 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.

**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 Super 120B A12B

1. 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)
2. 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
3. With vLLM serve it with `--reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` (SGLang uses `--reasoning-parser nemotron_3`)
4. Use `temperature=1.0` and `top_p=0.95` for every task, as the card says
5. Thinking is on by default. Pass `enable_thinking: false` or `low_effort: true` in `chat_template_kwargs` for short tasks
6. For coding agents pass `force_nonempty_content: true` in `chat_template_kwargs`, so a cut-off reasoning trace still returns content
7. Raise context past 262,144 only with the card's flags (`VLLM_ALLOW_LONG_MAX_MODEL_LEN=1`, `--max-model-len 1048576`)

### 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 Super 120B A12B or Recursive Flint?

Nemotron 3 Super 120B A12B scores 66.2 (B) on agent readiness against Recursive Flint's 61 (C), and leads in 1 of 4 scored categories.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-recursive-flint.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-recursive-flint.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "nemotron-3-super-120b-a12b", "b": "recursive-flint"}`. From a terminal: `anchor compare nemotron-3-super-120b-a12b recursive-flint`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nemotron-3-super-120b-a12b.json and https://www.anchorterminal.com/api/v1/tools/recursive-flint.json

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