# Nemotron 3 Nano Omni 30B A3B Reasoning vs Step 3.7 Flash > Nemotron 3 Nano Omni 30B A3B Reasoning scores 61.2 (C) to Step 3.7 Flash's 59.8 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-step-3-7-flash - Markdown: https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-step-3-7-flash.md (~11,800 tokens) - Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: 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) - DNS: dig +short TXT .grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.. and .vs.. under the zone, and the same under grade.letme.dev) - Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data) - Updated: 2026-11-10 Nemotron 3 Nano Omni 30B A3B Reasoning scores 61.2 (C) on agent readiness against Step 3.7 Flash's 59.8 (C), and leads in every scored category. Both do open-weight models you run yourself. - Nemotron 3 Nano Omni 30B A3B Reasoning: grade C, 61.2/100, rank #36 of 160 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 - Step 3.7 Flash: grade C, 59.8/100, rank #93 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/step-3-7-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/step-3-7-flash.json - Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md - All 6359 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 386 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 70 - Risk, 60 against 45 - Agent interface, 90 against 85 - Documentation and upkeep, 70 against 39 Watch for: Loads with trust_remote_code in every documented runtime command ### Step 3.7 Flash (C) Good for: Teams with a GPU server or a provider account that want an open, long-context agent model with vision Watch for: About 131 GB to run at 4 bits with images, over a 128 GB machine's share ## Why the edge ### Documentation and upkeep: Nemotron 3 Nano Omni 30B A3B Reasoning +31 - Nemotron 3 Nano Omni 30B A3B Reasoning (70): The card covers all seven fields, reading the 2019 to 2025 data collection period as the cut-off (20). Training data is listed with sources and sizes by dataset and one set is published (20). The Hub files last changed on 24 and 25 August 2026, and NVIDIA's TensorRT-LLM, which the card names for serving, changed the Nemotron-H model code on 14 October 2026, 20 days back (20). None of the 11 questions across the three repositories has a reply (0). It's a first release with no version policy stated (5). The shipped Jinja template and the tokenizer config's template differ, and reports about the template and a modeling file wait unanswered (5). 20 + 20 + 20 + 0 + 5 + 5 = 70. - Step 3.7 Flash (39): Model card 14, Intended use, the architecture, the parameters, the context and the language (in the metadata) are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the last commit, on 3 June 2026, fixed the modelling code, 160 days before this check and within 180. Responsiveness 0, none of the 11 most recent discussions that aren't pull requests (29 May to 24 June 2026) has a reply from a stepfun-ai member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, Step-3.5-Flash stays in its own repositories and 3.7 came out in new ones, with no dated note on the older cards that we read. File health 10, Config, template and card agree on the architecture and the context, and June commits fixed the modelling code. 14 + 0 + 10 + 0 + 5 + 10 = 39. ### Reach: Nemotron 3 Nano Omni 30B A3B Reasoning +15 - Nemotron 3 Nano Omni 30B A3B Reasoning (85): Memory earns 20 because the smallest 4-bit builds, Ollama's NVFP4 at 21 GB and NVIDIA's NVFP4 at 22.4 GB, need about 25 to 27 GB with the fifth added, which fits 32 GB but not 24. Runtimes reach the cap of 25 with released code in vLLM, SGLang and llama.cpp and library entries in Ollama and LM Studio. NVIDIA's FP8 and NVFP4 builds give builds the full 15. The NVIDIA Open Model Agreement is a custom licence with free commercial use, so terms get 10. The ungated repositories give download the full 10, and NVIDIA's own API serving it gives hosted too its 5. - Step 3.7 Flash (70): Memory 5, the smallest build at 4 bits is StepFun's IQ4_XS GGUF, 104.99 GB with the 3.97 GB projector, and 108.96 GB × 1.2 = 130.8 GB, over what the reference machine can give a model and within 256 GB. The IQ3 and Q3 builds are below 4 bits and aren't read. Runtimes 20, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the step3p7 model, and llama.cpp b12242 has the architecture, with StepFun's own GGUF. Four at 5 each, and the card documents all four. Builds 15, StepFun publishes GGUF at five quantised precisions, an FP8 build and an NVFP4 build. Terms 15, Apache 2.0 in the card's metadata and its licence section, an OSI licence with nothing added. Download 10, the Hub API reports gated false for all four repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, StepFun's own API serves it, per the card and our StepFun API listing, and Featherless and DeepInfra are live for it on Hugging Face. 5 + 20 + 15 + 15 + 10 + 5 = 70. ### Risk: Nemotron 3 Nano Omni 30B A3B Reasoning +15 - Nemotron 3 Nano Omni 30B A3B Reasoning (60): File safety gets 15, safetensors only but loaded with trust_remote_code. Provenance gets 15 for files in NVIDIA's own organisation with a SHA-256 for every shard. The agreement has no use policy, threshold or change clause and the grant is irrevocable, so licence risk keeps 15. NVIDIA's PSIRT form on the card and its public bulletins give the security programme 10. Agent guidance gets 5 for general cautions, and safety evaluation scores 0 because the subcards name the safety tests without publishing results. - Step 3.7 Flash (45): File safety 15, safetensors and GGUF only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the stepfun-ai organisation, which the card's StepFun platform and model page belong to (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations section and gives no guidance on prompt injection, instruction priority, refusals or safeguards. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 0, no way to report a flaw on the card, and our StepFun API listing found no security.txt, bug bounty or disclosure policy. 15 + 15 + 0 + 0 + 15 + 0 = 45. ### Agent interface: Nemotron 3 Nano Omni 30B A3B Reasoning +5 - Nemotron 3 Nano Omni 30B A3B Reasoning (90): A Jinja template ships in the repository (20). The card names the qwen3_coder parser and NVIDIA's cookbooks show tools declared with the OpenAI schema and tool_calls read back (25). The template renders several calls per turn and the card says it supports tool calling (20). JSON output is claimed with no word on how to ask for it (5). Thinking turns off per request, a budget caps it and the parser returns the reasoning apart (10). The context is 262,144 tokens, trained in (10). - Step 3.7 Flash (85): Chat template 20, chat_template.jinja in the repository (5,723 characters). Tool-calling format 25, the card names the parser to use in two runtimes, step3p5 in vLLM (with auto tool choice) and in SGLang, and both ship Step parsers in a release. Trained tool calling 20, the card presents tool orchestration as a main use, with multi-turn results, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output for the weights. Reasoning control 10, the template has a reasoning_effort setting and keeps reasoning_content, and the card names the step3p5 reasoning parser for vLLM and SGLang, which returns the reasoning apart from the answer. Context 10, 256K tokens per the card, and 262,144 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85. ## Score by category | Category | Weight | Nemotron 3 Nano Omni 30B A3B Reasoning | Step 3.7 Flash | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 85 | 70 | Nemotron 3 Nano Omni 30B A3B Reasoning +15 | | Risk | 20% (20 this run) | 60 | 45 | Nemotron 3 Nano Omni 30B A3B Reasoning +15 | | Agent interface | 10% (10 this run) | 90 | 85 | Nemotron 3 Nano Omni 30B A3B Reasoning +5 | | Documentation and upkeep | 10% (10 this run) | 70 | 39 | Nemotron 3 Nano Omni 30B A3B Reasoning +31 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61.2 · C** | **59.8 · C** | | ## Facts side by side | Fact | Nemotron 3 Nano Omni 30B A3B Reasoning | Step 3.7 Flash | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | NVIDIA | StepFun | | 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 (metadata and card; no LICENSE file) | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-08-24 | 2026-06-03 | | Terms last updated | no date given | no document linked | | Privacy policy last updated | no document linked | no document linked | | Customer content may train models | not found in the text | not read yet | | Terms restrict automated access | not found in the text | not read yet | | Terms restrict benchmarking | not found in the text | not read yet | | Terms or service can change without notice | not found in the text | not read yet | | Arbitration or class-action waiver | not found in the text | not read yet | | Agent reviews | 3/5 (4) | none | ## How the scores have moved ### Nemotron 3 Nano Omni 30B A3B Reasoning - +0.5 on 3 November 2026: correction ### Step 3.7 Flash No score change since it was listed. ## Verdicts **Nemotron 3 Nano Omni 30B A3B Reasoning.** A local multimodal model 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. **Step 3.7 Flash.** A capable open agent model with a clean licence, every kind of build and four released runtimes. Two things hold it back: it's a little too big for a 128 GB machine at 4 bits, whatever the card says, and StepFun has gone quiet on the repository since June. No safety results, limitations or data description. ## 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 ### Step 3.7 Flash 1. On a 128 GB machine the 4-bit GGUF won't fit with a working context. Use StepFun's API or a provider, or a server 2. On vLLM or SGLang pass `--tool-call-parser step3p5 --reasoning-parser step3p5` 3. The card's commands pass trust-remote-code. Released Transformers 5.22.2 has the model built in if you'd rather not run the repository's code 4. For llama.cpp load the mmproj file from StepFun's GGUF repository for image input 5. StepFun's API has two hosts. Use the one where your key was issued, `api.stepfun.ai` or `api.stepfun.com` 6. The same model is on our StepFun API listing, with prices and terms ## Questions ### Which is better for AI agents, Nemotron 3 Nano Omni 30B A3B Reasoning or Step 3.7 Flash? Nemotron 3 Nano Omni 30B A3B Reasoning scores 61.2 (C) on agent readiness against Step 3.7 Flash's 59.8 (C), and leads in every scored category. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-step-3-7-flash.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-step-3-7-flash.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "nemotron-3-nano-omni-30b-a3b-reasoning", "b": "step-3-7-flash"}`. 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