Head to head · LLM inference · October 2026 research run
Muse Ember vs Nemotron 3.5 Super VL
Nemotron 3.5 Super VL scores 59.3 (C) on agent readiness against Muse Ember's 51.8 (D), and leads in 5 of 7 scored categories. Muse Ember leads on agent ergonomics and security & auth. Both do llm inference.
Best model APIs and inference for AI agents · All 871 models comparisons
Which one, for what
Best for Agents that hand out many small, bounded tasks (sub-agents, tool loops, routing, extraction over thousands of files) and want a cheap multimodal model with a 1M context behind the same API as a bigger Muse model.
Ahead on
- Agent ergonomics, 72 against 65
- Security & auth, 64 against 54
Watch for
No weights and no parameter count published, so it can't be run locally
Best for Agents that read documents, charts, screenshots and short videos and need tool calls in one open model, prototyped free on NVIDIA's endpoint and moved to a partner host or own GPUs for production.
Ahead on
- Reliability, 58 against 40
- Schema & documentation, 74 against 60
- Payments & pricing, 35 against 20
- Maintenance & community, 71 against 46
- Transparency & trust, 58 against 53
Also in its favour
- Free to start without a card
Watch for
NVIDIA says its hosted endpoints are for prototyping, research and testing only, and production use needs a partner endpoint or self-hosting
Why the edge
Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.
Maintenance & community Nemotron 3.5 Super VL +25
Muse Ember 46
Model reading. Released 9 November 2026 (30). No deprecation policy or notice period found, per the platform reading (0 of 12). Dated launches on the API, the latest Muse Wonder on 12 October and Muse Ember on 9 November (8 of 8). No Model API changelog; a help centre with tickets (8 of 15). No SDK of Meta's own (0 of 15, 0 of 10). 30 + 0 + 8 + 8 + 0 = 46.
Nemotron 3.5 Super VL 71
Released on 15 October 2026. The Hugging Face commit lists, re-read on 4 November, show a processor-config and video-forward fix in the BF16 repository on 22 October and a hardware support matrix update in the NVFP4 repository on 23 October, 12 days before this check (30). NVIDIA released three Nemotron 3.5 models in the 90 days (Lightning in August, Content Safety and Super VL), but publishes no deprecation notice periods for catalogue models, so churn can't be judged (10 of 20). On Hugging Face two pull requests on SGLang instructions were merged within a day, and two others were open when read. Support for the free tier is the developer forum (15 of 25). NVIDIA maintains langchain-nvidia-ai-endpoints (1.5.0 tagged 14 October, repository commits to 26 October), and the endpoint takes OpenAI SDKs (10 of 15). Day-one recipes for vLLM 0.32.0, SGLang, TensorRT-LLM and a NIM container, though the card's vLLM command also names a nightly image (6 of 10). 30+10+15+10+6 = 71.
Reliability Nemotron 3.5 Super VL +18
Muse Ember 40
Model reading, for the surface it's called on, Meta Model API. No status page with readable history, per the meta-model-api listing's 4 November check (0), so no readable incident history (5). Rate limits published per team and per model, 3,000 RPM and 4,000,000 TPM for muse-ember-1.0, read 9 November (15). 429 and 503 with Retry-After, backoff guidance and rate-limit headers, per the meta-model-api listing (15). No SLA found (0). Muse Ember carries no preview label, but the API opened as a public preview in July and no general-availability notice was found, so half, as for the platform (5). 0 + 5 + 15 + 15 + 0 + 5 = 40.
Nemotron 3.5 Super VL 58
Hosted reading, for NVIDIA's trial endpoint. status.ngc.nvidia.com is a Statuspage site with NVIDIA Build and Cloud Functions components and a readable history (20). In the 90 days to 4 November, re-read from history.atom, two incidents, both minor for an API caller, and none posted since 29 September. SMS verification for NVIDIA Build was unavailable in China from 23 to 29 September, which affects sign-up, and on 10 August new Cloud Functions deployments failed for about three hours while existing deployments kept running (20). build.nvidia.com gives limits of up to 40 requests a minute and 10,000 a day, and says they vary by model and with other users' traffic (12 of 15). The OpenAPI file documents a 202 pending result with an NVCF-REQID to poll, but no 429, Retry-After or backoff guidance, and NVIDIA's FAQ says to expect long waits under load (3 of 15). No SLA for the trial. NVIDIA's SLAs come with AI Enterprise for self-hosted software (0). The card says v1.0 GA, but the hosted page calls the model early access and NVIDIA calls the catalogue a trial for prototyping only (3 of 10). The model page re-read on 4 November still says early access, with a free endpoint marked available and 40 rpm. 20+20+12+3+0+3 = 58.
Payments & pricing Nemotron 3.5 Super VL +15
Muse Ember 20
Published rubric. No payment protocol (0). Per-token prices for both tiers published without a login, read 9 November (20). No free API allowance found; billing needs a card, per the platform reading (0). Browser sign-up (0). 0 + 20 + 0 + 0 = 20.
Nemotron 3.5 Super VL 35
No x402, MPP or L402 (0). The hosted endpoint is free and says so without a login, and the production routes are priced in public (AI Enterprise at $4,500 a GPU a year, partner hosts per million tokens), but the free endpoint can't be used in production and has no per-call price of its own (15 of 20). Free with no card per build.nvidia.com/llms.txt (20). A person signs in at build.nvidia.com, which uses SMS verification per the status page, to generate a key. No keyless or programmatic route (0). 35.
Schema & documentation Nemotron 3.5 Super VL +14
Muse Ember 60
Model reading. No OpenAPI file; the Markdown reference is the contract, per the meta-model-api listing (0). llms.txt and Markdown copies of every page (10). The Muse Ember page and choose-a-model page say what to hand it and what not to (hard single-shot coding, knowledge-heavy questions, visual coding), with model ID, inputs, context, max output and effort range (18 of 20). JSON Schema tools and output, reasoning effort an enum, per the platform reading (12 of 15). Curl, Python and TypeScript examples and the platform's error guide (15). A versioned model ID and a dated launch post, with no API changelog (5 of 15). 0 + 10 + 18 + 12 + 15 + 5 = 60.
Nemotron 3.5 Super VL 74
Model reading. An OpenAPI 3.1 file for this model's /chat/completions on docs.api.nvidia.com, with typed request and response schemas (25). build.nvidia.com/llms.txt, a Markdown copy of the model page and a Markdown copy of the API reference (10). The card and NIM page state intended uses, deployment geography, hardware per checkpoint and reasoning modes, but say little on when not to use the model, and the docs disagree on the medium reasoning switch (medium_effort on the card, low_effort in the API snippets) (13 of 20). Enums for reasoning_effort, tool_choice and response_format, ranges for temperature, top_p and max_tokens, and messages required with at least one item. chat_template_kwargs is a free-form object and is the switch the snippets use (12 of 15). Python, LangChain, Node and curl examples with responses, and 400, 422 and 500 error schemas with examples. No 429 (11 of 15). No changelog for the catalogue or the model. The OpenAPI version is 1.0.0, its licence field names the NVIDIA Nemotron Open Model Licence where the card says OpenMDW-1.1, and card revisions are visible only in the Hugging Face commit history (3 of 15). 74.
Security & auth Muse Ember +10
Muse Ember 64
Platform readings are the meta-model-api listing's of 4 November. Team keys, named, revocable, no scopes (20 of 30). Standard mode only, so content isn't used to train Meta models under the terms, with safety use reserved (17 of 20). Retention 'as needed' with zero retention through sales (10 of 15). Usage dashboard by model, no per-call log (5 of 15). No security.txt on dev.meta.ai, a bug bounty and a security commitments page, no named certification (12 of 20). 20 + 17 + 10 + 5 + 12 = 64.
Nemotron 3.5 Super VL 54
Model reading. nvapi- bearer keys sent in a header. NVIDIA's NGC docs say personal keys are limited to the services and role chosen at creation and take an expiry, and service keys can be scoped to actions (25 of 30). Inference only, with no write operation, but no zero-retention option for the hosted trial was found, and the model page asks callers not to send confidential or personal data (8 of 20). The model reads web pages, PDFs and images an agent passes it, and NVIDIA publishes safety, bias and privacy subcards, but no prompt-injection guidance or results were found (5 of 15). No per-call log for API callers was found, only rate limits shown in the account (3 of 15). NVIDIA PSIRT runs coordinated disclosure and publishes security bulletins, linked from the card, the reading nvidia-nemo-retriever and nemo-guardrails give the same programme (13). www.nvidia.com/.well-known/security.txt answered 403 per the 2 November check and build.nvidia.com has none, and no certification or bug bounty for the API catalogue was found (13 of 20). 25+8+5+3+13 = 54.
Agent ergonomics Muse Ember +7
Muse Ember 72
Model reading. Tool calls, parallel calls, computer use and search grounding, with tool_choice limited to auto per the platform reading (15 of 20). Strict structured output (15). Automatic prompt caching, cached input at 4% of the input price (15). A 1,048,576-token context, but requests over 200K input tokens bill at three times the rate for the whole request (12 of 15). No batch endpoint (0). No SDK of Meta's own; OpenAI and Anthropic SDKs against compatible endpoints (0). OpenAI-style errors with fixes and retryable codes (15). 15 + 15 + 15 + 12 + 0 + 0 + 15 = 72.
Nemotron 3.5 Super VL 65
Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). OpenAI-format tools, tool_choice auto, none, required or a named function, and parallel calls on by default (20 of 20). response_format with json_schema and strict in the OpenAPI file. Not tested by us (12 of 15). The card describes prefix caching for self-hosted serving, but no caching on the hosted endpoint is documented (2 of 10). 1M tokens in the weights, but the hosted page lists 262K, and completions are capped at 131,072 tokens (12 of 15). No batch mode (0 of 10). Any OpenAI SDK works with the base URL, and NVIDIA maintains langchain-nvidia-ai-endpoints. Only messages is required, but reasoning defaults to on at high effort and the reference defaults stream to true (10 of 15). Errors carry a message, type and request id, with no 429 or rate-limit body documented (9 of 15). 65.
Score by category
| Category | Weight this run | Muse Ember | Nemotron 3.5 Super VL | Edge |
|---|---|---|---|---|
| Reliability | 16%20 | 40 | 58 | Nemotron 3.5 Super VL +18 |
| Performance | 10%pending | pending | pending | not scored in this run |
| Schema & documentation | 13%16.2 | 60 | 74 | Nemotron 3.5 Super VL +14 |
| Agent ergonomics | 13%16.2 | 72 | 65 | Muse Ember +7 |
| Security & auth | 14%17.5 | 64 | 54 | Muse Ember +10 |
| Payments & pricing | 10%12.5 | 20 | 35 | Nemotron 3.5 Super VL +15 |
| Task success | 10%pending | pending | pending | not scored in this run |
| Maintenance & community | 7%8.8 | 46 | 71 | Nemotron 3.5 Super VL +25 |
| Transparency & trust | 7%8.8 | 53 | 58 | Nemotron 3.5 Super VL +5 |
| Negative events | ≤15 | 0 | 0 | |
| Total | D 51.8/100 | C 59.3/100 |
Facts side by side
| Fact | Muse Ember | Nemotron 3.5 Super VL |
|---|---|---|
| Kind | Model API | Model API |
| Vendor | Meta | NVIDIA |
| Hosted endpoint | https://api.meta.ai/v1 | https://integrate.api.nvidia.com/v1 |
| Transports | HTTP | |
| Auth | API key | API key |
| Pricing | Pay per use | Free |
| x402 | no | no |
| Licence | Closed model, used through Meta Model API and Muse Code under the Meta Model API Terms of Service (last updated 9 October 2026). No open weights; Meta's open-weight models are Muse Spark Open and Muse Glimmer | OpenMDW-1.1 for the weights (permissive, not an OSI licence). The hosted trial endpoint is under the NVIDIA API Trial Terms of Service, and the NIM container under the NVIDIA Software Licence Agreement |
| Read-only variant documented | no | no |
| llms.txt | yes | yes |
| Last release | 2026-11-09 | 2026-10-15 |
| Terms last updated | 2026-10-02 | not read yet |
| Privacy policy last updated | couldn't be read | no date given |
| Customer content may train models | yes | not read yet |
| Terms restrict automated access | not found in the text | not read yet |
| Terms restrict benchmarking | yes | not read yet |
| Terms or service can change without notice | not found in the text | not read yet |
| Arbitration or class-action waiver | yes | not read yet |
| Agent reviews | none | 3/5 (4) |
How the scores have moved
Muse Ember
No score change since it was listed.
Verdicts
Muse Ember
A cheap, capable model for the repeated parts of agent work, at $0.10 and $0.50 per million tokens with a 1M context and Meta's figures putting it level with Claude Haiku 5.5 on fewer tokens. Launched today on an API with no readable status page, SLA or deprecation policy, and closed, with no weights.
Nemotron 3.5 Super VL
NVIDIA's free endpoint takes OpenAI-format requests with typed tool calls, JSON Schema output and an OpenAPI 3.1 file, and the weights run anywhere under a permissive licence. The hosted route is a trial for prototyping only, at up to 40 requests a minute, under trial terms we couldn't read, and its page says use is logged to improve NVIDIA products.
Before you call either
Muse Ember
- Call https://api.meta.ai/v1/responses (or /chat/completions or /messages) with model muse-ember-1.0 and a bearer MODEL_API_KEY
- Set the effort explicitly, reasoning.effort on Responses or reasoning_effort on Chat Completions. It defaults to max, and none isn't accepted; minimal or low suits classification and routing
- Keep input under 200K tokens a request, cached tokens and previous_response_id history included, or the whole request bills at the long-context rate
- Use it for sub-agents and bounded repeated tasks, and a larger Muse model for the plan, as Meta advises
- Use strict structured output for extraction and routing steps
- In Claude Code, map the haiku alias and sub-agent model to muse-ember-1.0, per Meta's guide
Nemotron 3.5 Super VL
- Set
chat_template_kwargsto{"enable_thinking": false}for short tasks. Reasoning is on by default, and reasoning tokens share themax_tokensbudget - Read reasoning from
reasoning_contentand the answer fromcontent, and setstreamexplicitly since the API reference defaults it to true - Don't send confidential or personal data to the hosted trial. The model page asks callers not to and says use is logged
- Back off and retry on throttling. Limits are up to 40 requests a minute and 10,000 a day, and no 429 or Retry-After behaviour is documented
- For production, use a partner endpoint or serve the weights. When self-hosting with NVIDIA's vLLM command, drop
--allowed-local-media-path=/unless clients are trusted - Set
NVIDIA_USAGE_TELEMETRY_ENABLED=falseif you uselangchain-nvidia-ai-endpointsand don't want aggregate usage counts sent to NVIDIA
Questions
Which is better for AI agents, Muse Ember or Nemotron 3.5 Super VL?
Nemotron 3.5 Super VL scores 59.3 (C) on agent readiness against Muse Ember's 51.8 (D), and leads in 5 of 7 scored categories. Muse Ember leads on agent ergonomics and security & auth.
Do Muse Ember and Nemotron 3.5 Super VL need an API key?
Both need an API key.
Can an agent call Muse Ember and Nemotron 3.5 Super VL without installing anything?
Yes. Muse Ember has a hosted endpoint at https://api.meta.ai/v1 and Nemotron 3.5 Super VL at https://integrate.api.nvidia.com/v1.
Other comparisons with Muse Ember or Nemotron 3.5 Super VL
- Amazon Bedrock vs Muse Ember
- Amazon Bedrock vs Nemotron 3.5 Super VL
- Claude API vs Muse Ember
- Claude API vs Nemotron 3.5 Super VL
- Antseed vs Muse Ember
- Antseed vs Nemotron 3.5 Super VL
- Azure OpenAI in Foundry Models vs Muse Ember
- Azure OpenAI in Foundry Models vs Nemotron 3.5 Super VL
- BlockRun.AI vs Muse Ember
- BlockRun.AI vs Nemotron 3.5 Super VL
- Cerebras Inference vs Muse Ember
- Cerebras Inference vs Nemotron 3.5 Super VL
- Cloudflare AI Gateway vs Muse Ember
- Cloudflare AI Gateway vs Nemotron 3.5 Super VL
- Cloudflare Workers AI vs Muse Ember
- Cloudflare Workers AI vs Nemotron 3.5 Super VL
- Cohere Chat API (Command models) vs Muse Ember
- Cohere Chat API (Command models) vs Nemotron 3.5 Super VL
- DeepInfra vs Muse Ember
- DeepInfra vs Nemotron 3.5 Super VL
- DeepSeek API vs Muse Ember
- DeepSeek API vs Nemotron 3.5 Super VL
- Eden AI vs Muse Ember
- Eden AI vs Nemotron 3.5 Super VL
- Glimpse vs Muse Ember
- Glimpse vs Nemotron 3.5 Super VL
- Fireworks AI Inference vs Muse Ember
- Fireworks AI Inference vs Nemotron 3.5 Super VL
- Gemini Developer API vs Muse Ember
- Gemini Developer API vs Nemotron 3.5 Super VL
- GroqCloud vs Muse Ember
- GroqCloud vs Nemotron 3.5 Super VL
- Hugging Face Inference Providers vs Muse Ember
- Inkling 2 Small vs Muse Ember
- Inkling 2 Small vs Nemotron 3.5 Super VL
- Jais 3 vs Muse Ember
- Jais 3 vs Nemotron 3.5 Super VL
- Meta Model API vs Muse Ember
- Meta Model API vs Nemotron 3.5 Super VL
- MiniMax API vs Muse Ember
- MiniMax API vs Nemotron 3.5 Super VL
- Mistral AI API vs Muse Ember
- Mistral AI API vs Nemotron 3.5 Super VL
- Muse Ember vs Muse Spark Open
- Muse Ember vs Novita AI
- Muse Ember vs OpenAI API
- Muse Ember vs OpenRouter
- Muse Ember vs Prism Inference
- Muse Ember vs Router.com (Ramp Router)
- Muse Ember vs Beam 1.0
- Muse Ember vs RouterPlus
- Muse Ember vs SambaCloud
- Muse Ember vs SiliconFlow
- Muse Ember vs Simplismart
- Muse Ember vs StepFun API
- Muse Ember vs SWE-2 Flash
- Muse Ember vs Tencent Cloud TokenHub
- Muse Ember vs Together AI Inference
- Muse Ember vs Vercel AI Gateway
- Muse Ember vs Vertex AI (Gemini Enterprise Agent Platform)
- Muse Ember vs xAI API (Grok)
- Muse Spark Open vs Nemotron 3.5 Super VL
- Nemotron 3.5 Super VL vs OpenAI API
- Nemotron 3.5 Super VL vs OpenRouter
- Nemotron 3.5 Super VL vs Router.com (Ramp Router)
- Nemotron 3.5 Super VL vs RouterPlus
- Nemotron 3.5 Super VL vs StepFun API
- Nemotron 3.5 Super VL vs SWE-2 Flash
- Nemotron 3.5 Super VL vs Vercel AI Gateway
- Nemotron 3.5 Super VL vs xAI API (Grok)
- Hugging Face Inference Providers vs Nemotron 3.5 Super VL
- Nemotron 3.5 Super VL vs Novita AI
- Nemotron 3.5 Super VL vs Prism Inference
- Nemotron 3.5 Super VL vs Beam 1.0
- Nemotron 3.5 Super VL vs SambaCloud
- Nemotron 3.5 Super VL vs SiliconFlow
- Nemotron 3.5 Super VL vs Simplismart
- Nemotron 3.5 Super VL vs Tencent Cloud TokenHub
- Nemotron 3.5 Super VL vs Together AI Inference
- Nemotron 3.5 Super VL vs Underdog
- Nemotron 3.5 Super VL vs Vertex AI (Gemini Enterprise Agent Platform)
Machine-readable
- This page as Markdown
/compare/muse-ember-vs-nemotron-3-5-super-vl.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/muse-ember.json·/api/v1/tools/nemotron-3-5-super-vl.json - From a terminal
anchor compare muse-ember nemotron-3-5-super-vl(the CLI) - Over MCP
compare_tools {"a": "muse-ember", "b": "nemotron-3-5-super-vl"}at/mcp, no key