> For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://docs.nvidia.com/nemoclaw/llms.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemoclaw/_mcp/server. # Choose a Model > Compare curated cloud models by task fit, latency, tool use, context, and relative cost. Use the curated model choices as starter guidance when selecting a cloud model during onboarding. The provider catalog remains authoritative for exact context-window limits and current pricing. Runtime route validation determines current availability because catalog entries can outlive their backing endpoints. ## Catalog Selection During interactive NVIDIA Endpoints onboarding, NemoClaw loads NVIDIA's public featured model catalog once per onboarding session and reports progress before displaying the model picker. OpenRouter uses the same catalog-backed picker flow and featured model list with its own provider route and credential check. NVIDIA Endpoints excludes NVIDIA-retired or unsafe choices and corrects known catalog lag before displaying the result. If the catalog is unavailable, malformed, or contains no safe model IDs, the wizard warns you and uses its bundled fallback list. Nemotron 3 Super remains the shared default for OpenClaw and Hermes when it is present. LangChain Deep Agents Code uses Nemotron 3 Ultra as its NVIDIA Endpoints default. If an agent's default is unavailable, the first live featured model becomes the interactive default. If `NEMOCLAW_MODEL` contains a safe custom model ID that is absent from the live catalog, it does not replace the live menu default. Choose **Other** to use that value as the pre-filled manual entry. NemoClaw validates the manual entry against the selected provider before continuing. NemoClaw does not display or accept an unsafe value as the manual-entry prefill. ## Model Task Fit The relative labels compare models within the curated onboarding choices rather than across every model that a provider offers. | Model | Best for | Relative latency | Tool use | Context fit | Relative cost | | ----------------------------------- | ----------------------------------------------------------------------------- | ---------------- | -------------------------------------------------------------- | ----------------------- | -------------- | | `nvidia/nemotron-3-ultra-550b-a55b` | Quality-sensitive reasoning, careful synthesis, and complex reviews | Higher | Strong for complex tool plans | Large agent context | Higher | | `nvidia/nemotron-3-super-120b-a12b` | Hosted agent work, multi-step planning, and tool-heavy shell workflows | Medium | Strong default for OpenClaw tool loops | Large agent context | Medium | | `minimaxai/minimax-m3` | Long-form writing, multi-turn assistant work, and broad instruction following | Medium | Good for structured assistant turns | Large agent context | Medium | | `gpt-5.4` | General OpenAI-backed agent work and high-quality reasoning | Medium | Strong | Large agent context | Medium to high | | `gpt-5.4-mini` | Latency-sensitive routine automation and repeated helper calls | Low | Good | Medium to large context | Low | | `gpt-5.4-nano` | Classification, routing, extraction, and small helper tasks | Very low | Basic to good for simple tool loops | Medium context | Very low | | `gpt-5.4-pro-2026-03-05` | Quality-first complex reasoning where latency and cost are secondary | Highest | Validate Responses API support before long tool loops | Large agent context | Highest | | `claude-sonnet-4-6` | Balanced coding, writing, analysis, and multi-step tool work | Medium | Strong | Large agent context | Medium to high | | `claude-haiku-4-5` | Fast summarization, routing, extraction, and lightweight assistant turns | Low | Good for simple tool loops | Medium to large context | Low | | `claude-opus-4-6` | Deep analysis, careful writing, and quality-first planning | Higher | Strong | Large agent context | Higher | | `gemini-3.1-pro-preview` | Large-context analysis, synthesis, and preview-feature evaluation | Medium to high | Good, with tool continuation validation for the selected route | Extensive context | Medium to high | | `gemini-3.1-flash-lite-preview` | Low-cost extraction, classification, and simple helper calls | Low | Basic to good for simple tool loops | Medium to large context | Low | | `gemini-3-flash-preview` | Fast general assistant tasks and preview-feature evaluation | Low | Good for simple tool loops | Large context | Low | | `gemini-2.5-pro` | Large-context analysis, long-document synthesis, and complex reasoning | Medium to high | Good | Extensive context | Medium to high | | `gemini-2.5-flash` | Latency-sensitive general assistant and multimodal tasks | Low | Good for simple tool loops | Large context | Low | | `gemini-2.5-flash-lite` | Lowest-cost helper calls, extraction, and classification | Very low | Basic to good for simple tool loops | Medium to large context | Very low | ## Nemotron Deployment Choice Nemotron models expose OpenAI-compatible APIs across the supported deployment surfaces. Choose the onboarding option that matches the host. | Nemotron host | Onboarding option | | ------------------------------------------------- | -------------------------------- | | NVIDIA-hosted on `build.nvidia.com` | NVIDIA Endpoints | | Self-hosted NIM container | Other OpenAI-compatible endpoint | | Enterprise NVIDIA AI Enterprise gateway | Other OpenAI-compatible endpoint | | vLLM, SGLang, or TRT-LLM serving Nemotron weights | Other OpenAI-compatible endpoint | | Local NIM started by the wizard | Local NVIDIA NIM | ## Related Topics * [Choose an Inference Provider](choose-inference-provider) compares the deployment routes. * [Use NVIDIA Endpoints](../hosted-inference/use-nvidia-endpoints) explains the hosted NVIDIA catalog flow. * [Understand Provider Validation](../validate-inference/understand-provider-validation) explains how NemoClaw checks a selected model.