# Nemotron 3 Nano Omni 30B A3B Reasoning vs Recursive Flint > Recursive Flint and Nemotron 3 Nano Omni 30B A3B Reasoning score within a point of each other for open-weight models you run yourself, 61 and 60.7 out of 100. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-recursive-flint - Markdown: https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-recursive-flint.md (~7,800 tokens) - Slim: https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-recursive-flint.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-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 Recursive Flint and Nemotron 3 Nano Omni 30B A3B Reasoning score within a point of each other on agent readiness, 61 (C) and 60.7 (C). Nemotron 3 Nano Omni 30B A3B Reasoning leads on reach. Both do open-weight models you run yourself. - Nemotron 3 Nano Omni 30B A3B Reasoning: grade C, 60.7/100, rank #55 of 107 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 - 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 - Best speech-to-text APIs for AI agents: https://www.anchorterminal.com/best/speech-to-text/index.md - All 233 stt comparisons: https://www.anchorterminal.com/compare/speech-to-text/index.md ## Which one, for what ### 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: - Reach, 85 against 75 Watch for: Loads with trust_remote_code in every documented runtime command ### 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 3.0 out of 5 Watch for: No independent benchmark results yet, every published score is Recursive's ## Score by category | Category | Weight | Nemotron 3 Nano Omni 30B A3B Reasoning | Recursive Flint | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 85 | 75 | Nemotron 3 Nano Omni 30B A3B Reasoning +10 | | Risk | 20% (20 this run) | 60 | 60 | even | | 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** | | **60.7 · C** | **61 · C** | | ## Facts side by side | Fact | Nemotron 3 Nano Omni 30B A3B Reasoning | 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 Open Model Agreement (custom, 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 | 3/5 (4) | 3.5/5 (4) | ## Verdicts **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. **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 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 ### 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 , , and from untrusted tool output, the template doesn't escape them and a copied 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 Omni 30B A3B Reasoning or Recursive Flint? Recursive Flint and Nemotron 3 Nano Omni 30B A3B Reasoning score within a point of each other on agent readiness, 61 (C) and 60.7 (C). Nemotron 3 Nano Omni 30B A3B Reasoning leads on reach. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-recursive-flint.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-recursive-flint.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "nemotron-3-nano-omni-30b-a3b-reasoning", "b": "recursive-flint"}`. 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