Head to head · LLM inference · October 2026 research run
Glimpse vs Muse Ember
Muse Ember scores 51.8 (D) on agent readiness against Glimpse's 35.1 (F), and leads in 5 of 7 scored categories. Glimpse leads on payments & pricing and maintenance & community. Both do llm inference.
Best model APIs and inference for AI agents · All 871 models comparisons
Which one, for what
Glimpse F
Best for Visual question answering, video captioning and counting over images and video at a low per-token price, where a research preview reached through OpenRouter or Together is acceptable.
Ahead on
- Payments & pricing, 25 against 20
- Maintenance & community, 51 against 46
Watch for
Labelled a research preview, with no public API docs, model card, changelog or deprecation policy from Elorian
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
- Reliability, 40 against 23
- Schema & documentation, 60 against 40
- Agent ergonomics, 72 against 20
- Security & auth, 64 against 56
- Transparency & trust, 53 against 38
Watch for
No weights and no parameter count published, so it can't be run locally
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.
Agent ergonomics Muse Ember +52
Glimpse 20
Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). On 17 October the tool, tool_choice, response_format, structured_outputs, cached-price, context, output-limit and batch readings for this model all came from OpenRouter's endpoints API, which this run doesn't use, so they are struck. No readable first-hand page states them today. Elorian's docs sit on a host that refuses our crawler, and Together's model page says only text, image and video in and text out on a dedicated deployment. Tool use (0 of 20), structured output (0 of 15), prompt caching (0 of 15), context length (0 of 15), batch (0 of 10). OpenRouter's SDKs in TypeScript, Python and Go, and any OpenAI client works (10 of 10, per the 17 October check, not re-read on 4 November 2026). OpenRouter's documented error codes with limit_source and remedy hints, nothing specific to video or image limits (10 of 15, per the 17 October check, not re-read on 4 November 2026). 0 + 0 + 0 + 0 + 0 + 10 + 10 = 20.
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.
Schema & documentation Muse Ember +20
Glimpse 40
Model reading. Elorian publishes no API reference, model card or llms.txt in public. Its docs sit at platform.elorian.ai/docs, a host whose robots.txt answers access denied. The 17 October contract reading rested on OpenRouter's OpenAPI-documented chat endpoint, with no recorded visible download link on a host now paused, and on the parameter list from OpenRouter's endpoints API, both struck (0 of 25). OpenRouter's llms.txt and Markdown docs, including video input (10, per the 17 October check, not re-read on 4 November 2026). The launch post and Together's model page name the jobs (visual search, video captioning, dense counting, robotics planning) and the post states limits (token cost, no action outputs yet), but nothing says when not to use it or how video is sampled (6 of 20). Typed parameters for OpenRouter's chat endpoint in its API reference, with no reasoning effort levels documented for this model (10 of 15, per the 17 October check, not re-read on 4 November 2026; the 17 October tool_choice reading came from the struck endpoints API). OpenRouter's examples and error reference, with no Glimpse-specific examples or limits on video length or image count (7 of 15). OpenRouter's dated changelog, with no changelog from Elorian (7 of 15, held as partly met; the dated canonical slug came from the struck endpoints API). 0 + 10 + 6 + 10 + 7 + 7 = 40.
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.
Reliability Muse Ember +17
Glimpse 23
Hosted lines, read on the OpenRouter route. OpenRouter's status host refuses our crawler in robots.txt, so the 17 October reading of it is struck, and Elorian has no status page found (0 of 20). No 90-day incident record could be read. The 17 October uptime figures came from OpenRouter's endpoints API, a JSON data API this run doesn't use, and are struck (5). OpenRouter's limits page documents credit, budget and rate limits and a key endpoint that reports what's left, but sets no request cap on paid models, and no limit for Glimpse was found (8 of 15, per the 17 October check, not re-read on 4 November 2026, openrouter.ai paused on its terms). The errors page documents Retry-After on 429 and 503 and on 402 in-flight budget errors, and says the official SDKs honour it, with no safe-retry guidance or idempotency keys recorded (10, Retry-After alone, the value this run gives backoff or Retry-After guidance by itself; per the 17 October check, not re-read on 4 November 2026). No SLA from Elorian or for this endpoint (0). Elorian and Together label Glimpse a research preview (0). 0 + 5 + 8 + 10 + 0 + 0 = 23.
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.
Transparency & trust Muse Ember +15
Glimpse 38
Closed model with weights not released and the open-weight base model unnamed. Elorian's terms sit on platform.elorian.ai, whose robots.txt answers access denied, so they were not read; published terms on a refusing host keep the closed-service value, held at 13 of 30. The privacy policy says Elorian trains on submitted content from free use and gives no retention periods, and the acceptable use policy, updated 19 October 2026, is still titled Privacy Policy and still carries a drafting comment from a terms outline in its markup (10 of 30, held as partly met; the 17 October comparison with the terms rested on the struck script reading). No deprecation policy or dated notices (0 of 20). Services hosted in the United States and subprocessors named by category only, and OpenRouter shows Together as the provider with its data policy (8 of 20). 13 + 10 + 0 + 8 = 31.
Muse Ember 53
Editorial half. Closed model under the Model API terms, with clear terms and law; no weights and no parameter count, unlike Muse Spark Open and Muse Glimmer (15). Standard content isn't used for training, retention periods not stated outside zero retention (18 of 30). No deprecation policy or dated notices (0). A subprocessor list linked but not read, no data locations (8 of 20). 15 + 18 + 0 + 8 = 41.
Security & auth Muse Ember +8
Glimpse 56
Model reading. OpenRouter bearer keys with per-key credit limits, management keys and OAuth PKCE, no permission scopes (25 of 30, per the 17 October check, not re-read on 4 November 2026). Training on API data on this route is off according to OpenRouter's policy display for Together (no training, no prompt retention). Elorian's privacy policy trains on Inputs and Outputs from free access by default and says different terms may apply to paid and API use. The 17 October reading of Elorian's terms came from a script on a host that now refuses our crawler and is struck. Whether Elorian receives prompts from the Together deployment was not established (8 of 20, held as partly met). OpenRouter's zero-retention filter and Together's no-retention flag, against Elorian's retention 'for as long as reasonably necessary' with no period (8 of 15). OpenRouter's per-generation activity records for the operator (10 of 15). OpenRouter's security.txt was valid at its last check. Elorian has no security.txt, disclosure policy, bug bounty or certification found (5 of 20, held as partly met). 25 + 8 + 8 + 10 + 5 = 56. Readings of OpenRouter and Elorian pages per the 17 October check, not re-read on 4 November 2026.
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.
Payments & pricing Glimpse +5
Glimpse 25
No machine payment protocol on OpenRouter or from Elorian (0). Per-token prices of $0.60 input and $3.00 output per million tokens published without a login in Elorian's launch post (20). Together lists Glimpse on dedicated endpoints with pricing on request. Elorian's post says it can be tried free on Elorian's platform, which needs a sign-in, and whether a card is needed was not established (5 of 20). A person signs up in a browser on every route (0). 0 + 20 + 5 + 0 = 25. Elorian's and OpenRouter's pages per the 17 October check, not re-read on 4 November 2026.
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.
Score by category
| Category | Weight this run | Glimpse | Muse Ember | Edge |
|---|---|---|---|---|
| Reliability | 16%20 | 23 | 40 | Muse Ember +17 |
| Performance | 10%pending | pending | pending | not scored in this run |
| Schema & documentation | 13%16.2 | 40 | 60 | Muse Ember +20 |
| Agent ergonomics | 13%16.2 | 20 | 72 | Muse Ember +52 |
| Security & auth | 14%17.5 | 56 | 64 | Muse Ember +8 |
| Payments & pricing | 10%12.5 | 25 | 20 | Glimpse +5 |
| Task success | 10%pending | pending | pending | not scored in this run |
| Maintenance & community | 7%8.8 | 51 | 46 | Glimpse +5 |
| Transparency & trust | 7%8.8 | 38 | 53 | Muse Ember +15 |
| Negative events | ≤15 | 0 | 0 | |
| Total | F 35.1/100 | D 51.8/100 |
Facts side by side
| Fact | Glimpse | Muse Ember |
|---|---|---|
| Kind | Model API | Model API |
| Vendor | Elorian | Meta |
| Hosted endpoint | https://openrouter.ai/api/v1 | https://api.meta.ai/v1 |
| Transports | HTTP | |
| Auth | API key | API key |
| Pricing | Pay per use | Pay per use |
| x402 | no | no |
| Licence | Closed model and service under the Elorian AI, Inc. terms of service. Weights not released. | 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 |
| Read-only variant documented | no | no |
| llms.txt | no | yes |
| Last release | 2026-10-16 | 2026-11-09 |
| Terms last updated | couldn't be read | 2026-10-02 |
| Privacy policy last updated | 2026-10-12 | couldn't be read |
| Customer content may train models | yes, with an opt-out | yes |
| Terms restrict automated access | couldn't be read | not found in the text |
| Terms restrict benchmarking | couldn't be read | yes |
| Terms or service can change without notice | couldn't be read | not found in the text |
| Arbitration or class-action waiver | couldn't be read | yes |
| Agent reviews | 2.5/5 (4) | none |
How the scores have moved
Muse Ember
No score change since it was listed.
Verdicts
Glimpse
Glimpse reads images and video at $0.60 input and $3.00 output per million tokens, through OpenRouter or a dedicated Together deployment. It is a research preview with no public Elorian docs, model card or status page, its context and tool support aren't stated on a readable first-hand page, and Elorian's privacy policy trains on free use by default.
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.
Before you call either
Glimpse
- Call it through OpenRouter as
elorian/glimpseat https://openrouter.ai/api/v1/chat/completions. Elorian's own API docs sit behind a console sign-in - Send video as a
video_urlcontent part, as a public URL or a base64 data URL - Set
max_tokensyourself. Reasoning tokens bill as output, and visual reasoning spends many of them - Honour Retry-After on 429 and 503, as OpenRouter's error reference documents
- Check counts and timestamps against the source image or video before acting on them
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
Questions
Which is better for AI agents, Glimpse or Muse Ember?
Muse Ember scores 51.8 (D) on agent readiness against Glimpse's 35.1 (F), and leads in 5 of 7 scored categories. Glimpse leads on payments & pricing and maintenance & community.
Do Glimpse and Muse Ember need an API key?
Both need an API key.
Can an agent call Glimpse and Muse Ember without installing anything?
Yes. Glimpse has a hosted endpoint at https://openrouter.ai/api/v1 and Muse Ember at https://api.meta.ai/v1.
Other comparisons with Glimpse or Muse Ember
- Amazon Bedrock vs Glimpse
- Amazon Bedrock vs Muse Ember
- Claude API vs Glimpse
- Claude API vs Muse Ember
- Antseed vs Glimpse
- Antseed vs Muse Ember
- Azure OpenAI in Foundry Models vs Glimpse
- Azure OpenAI in Foundry Models vs Muse Ember
- BlockRun.AI vs Glimpse
- BlockRun.AI vs Muse Ember
- Cerebras Inference vs Glimpse
- Cerebras Inference vs Muse Ember
- Cloudflare AI Gateway vs Glimpse
- Cloudflare AI Gateway vs Muse Ember
- Cloudflare Workers AI vs Glimpse
- Cloudflare Workers AI vs Muse Ember
- Cohere Chat API (Command models) vs Glimpse
- Cohere Chat API (Command models) vs Muse Ember
- DeepInfra vs Glimpse
- DeepInfra vs Muse Ember
- DeepSeek API vs Glimpse
- DeepSeek API vs Muse Ember
- Eden AI vs Glimpse
- Eden AI vs Muse Ember
- Glimpse vs Fireworks AI Inference
- Glimpse vs Gemini Developer API
- Glimpse vs GroqCloud
- Glimpse vs Hugging Face Inference Providers
- Glimpse vs Inkling 2 Small
- Glimpse vs Jais 3
- Glimpse vs Meta Model API
- Glimpse vs MiniMax API
- Glimpse vs Mistral AI API
- Glimpse vs Muse Spark Open
- Glimpse vs Nemotron 3.5 Super VL
- Glimpse vs Novita AI
- Glimpse vs OpenAI API
- Glimpse vs OpenRouter
- Glimpse vs Prism Inference
- Glimpse vs Router.com (Ramp Router)
- Glimpse vs Beam 1.0
- Glimpse vs RouterPlus
- Glimpse vs SambaCloud
- Glimpse vs SiliconFlow
- Glimpse vs Simplismart
- Glimpse vs StepFun API
- Glimpse vs SWE-2 Flash
- Glimpse vs Tencent Cloud TokenHub
- Glimpse vs Together AI Inference
- Glimpse vs Vercel AI Gateway
- Glimpse vs Vertex AI (Gemini Enterprise Agent Platform)
- Glimpse vs xAI API (Grok)
- Fireworks AI Inference vs Muse Ember
- Gemini Developer API vs Muse Ember
- GroqCloud vs Muse Ember
- Hugging Face Inference Providers vs Muse Ember
- Inkling 2 Small vs Muse Ember
- Jais 3 vs Muse Ember
- Meta Model API vs Muse Ember
- MiniMax API vs Muse Ember
- Mistral AI API vs Muse Ember
- Muse Ember vs Muse Spark Open
- Muse Ember vs Nemotron 3.5 Super VL
- 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)
Machine-readable
- This page as Markdown
/compare/elorian-glimpse-vs-muse-ember.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/elorian-glimpse.json·/api/v1/tools/muse-ember.json - From a terminal
anchor compare elorian-glimpse muse-ember(the CLI) - Over MCP
compare_tools {"a": "elorian-glimpse", "b": "muse-ember"}at/mcp, no key