Head to head · LLM inference · October 2026 research run

Muse Ember vs Beam 1.0

Beam 1.0 scores 53.4 (D) on agent readiness against Muse Ember's 51.8 (D), and leads in 4 of 7 scored categories. Muse Ember leads on agent ergonomics, security & auth and payments & pricing. Both do llm inference.

Best model APIs and inference for AI agents · All 871 models comparisons

Which one, for what

Muse Ember D

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 67
  • Security & auth, 64 against 45
  • Payments & pricing, 20 against 10

Watch for

No weights and no parameter count published, so it can't be run locally

Beam 1.0 D

Best for An OpenAI-compatible endpoint for a large open-weight coding and agent model, for builders who want the same model hosted or self-hosted under Apache-2.0.

Ahead on

  • Reliability, 52 against 40
  • Schema & documentation, 84 against 60

Watch for

No SLA, and per-organisation rate limits and the daily token allowance are not published as numbers

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.

Schema & documentation Beam 1.0 +24

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.

Beam 1.0 84

Model reading. OpenAPI 3.0.3 document, version 0.1.0, covering all three documented endpoints with typed schemas and error examples. A second 'Reflection client' base URL, https://api.reflection.ai/v1, is named with no endpoints documented (22 of 25). llms.txt, a Markdown copy of every page and a docs MCP endpoint (10). The guides say which reasoning effort suits which task, which OpenAI parameters are fixed, ignored or unsupported and which endpoints don't exist, so a model knows what not to send (17 of 20). reasoning_effort is an enum, strict JSON Schema for tools and json_schema output with the unsupported keywords and size limits listed, and unknown parameters return unsupported_parameter (15). curl, Python and TypeScript examples on every guide, and an error reference with every status, error.code and retry advice (15). The model ID carries its version and the models endpoint has a shutdown_date field, but no changelog or release notes were found (5 of 15). 22 + 10 + 17 + 15 + 15 + 5 = 84. The OpenAPI file is linked from llms.txt; the guides beyond the overview, models, rate limits, authentication and reference overview pages are per the 17 October check.

Security & auth Muse Ember +19

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.

Beam 1.0 45

Model reading. API keys per project, revocable, shown once and disabled when their creator leaves, with no scopes, expiry or rotation API found (20 of 30). Training 10 of 20, partial kept. The API reference says each response's reflection-data-retention header reports contributor when content may be used to train Reflection's models "under contributor terms your organization accepted". The API safety page and commercial terms read on 17 October are struck (reflection.ai now disallows our crawler), so whether training is off by default can't be read. Retention 5 of 15, partial kept. The header can report zero, standard or contributor, and no readable page states periods or whether zero retention is offered. x-request-id on every response and the retention header, with usage dashboards behind the login and not read (5 of 15). Security programme 5 of 20 (was 6). The model card names reports@reflection.ai for security vulnerabilities; developers.reflection.ai has no security.txt (404 on 17 October), and the safety report and reflection.ai's security.txt can't be read. No bug bounty, SOC 2 or ISO 27001 found on a readable page. 20 + 10 + 5 + 5 + 5 = 45.

Reliability Beam 1.0 +12

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.

Beam 1.0 52

Hosted lines, for the Reflection API. Status page at status.reflection.ai on incident.io, with API, platform and developer docs components and Atom and RSS feeds (20). In the 90 days to 4 November 2026 its incident pages list two full outages of the API component, a serving cluster down from 22:01 to 23:20 UTC on 14 October and elevated errors from 06:10 to 07:10 UTC on 15 October, each an hour or more, so two majors. Three more are labelled partial outages (16 October 02:28 to 02:38 UTC, 22 October rate-limit errors in two windows, 3 November about 20:10 to 21:38 UTC on one inference provider), read as minor. Two majors give 5. Rate limits are described per organisation in requests and tokens per minute and per day, with concurrency caps and headers, but no figures are published (5 of 15). 429 and 503 responses carry Retry-After, daily limits add x-should-retry: false, and the errors page says which codes to retry, with exponential backoff and jitter. There are no idempotency keys, and a chat completion is the billed call, so this run's value for retry guidance without idempotency keys gives 12 (was 15). No SLA found (0). The developers docs read on 4 November carry no beta or preview label for the API; the earlier beta reading came from reflection.ai pages now struck (10). 20 + 5 + 5 + 12 + 0 + 10 = 52.

Payments & pricing Muse Ember +10

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.

Beam 1.0 10

Graded on the Reflection API. No x402, MPP or L402 (0). No per-token, plan or allocation price is published on any readable page; the beta pricing page on reflection.ai is struck (0, was 10). Free tier 10 of 20, partial kept. The rate-limits page describes a daily token allowance and asks those requesting higher limits whether they are "interested in paid usage", and the authentication page says some organisations must verify a credit card before keys work. A person signs up in the browser at platform.reflection.ai to get a key (0). 0 + 0 + 10 + 0 = 10.

Agent ergonomics Muse Ember +5

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.

Beam 1.0 67

Model reading (tool use, structured output, caching, context, batch, SDKs and defaults, errors). tool_choice of auto, none, required or a named function, parallel_tool_calls, strict tools and up to 128 tools. The tool-calling loop needs reasoning_content sent back on every assistant turn, and the docs say the model sometimes returns a tool-call step without it, after which every request in the session fails (12 of 20). json_object and strict json_schema (15). Cached prompt tokens are reported in usage, but caching can't be steered, the prompt_cache_* options are ignored and no cached price exists in the beta (5 of 10). 1,048,576-token context and 131,072 output tokens (15). No batch endpoint (0 of 10). No Reflection SDK, the OpenAI Python and TypeScript SDKs are documented, and defaults cost an agent turns, with reasoning always on at max, stop and seed ignored and a default output budget not reduced to fit the prompt (5 of 10). Machine-readable error.code values with param, x-should-retry, Retry-After and x-request-id (15). 12 + 15 + 5 + 15 + 0 + 5 + 15 = 67, per the 17 October reading of the developers guides, not re-read on 4 November beyond the models and rate-limits pages.

Score by category

CategoryWeight this runMuse EmberBeam 1.0Edge
Reliability16%204052Beam 1.0 +12
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.26084Beam 1.0 +24
Agent ergonomics13%16.27267Muse Ember +5
Security & auth14%17.56445Muse Ember +19
Payments & pricing10%12.52010Muse Ember +10
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.84650Beam 1.0 +4
Transparency & trust7%8.85357Beam 1.0 +4
Negative events≤1500
TotalD 51.8/100D 53.4/100

Facts side by side

FactMuse EmberBeam 1.0
KindModel APIModel API
VendorMetaReflection
Hosted endpointhttps://api.meta.ai/v1https://api.reflection.ai/openai/v1
TransportsHTTP
AuthAPI keyAPI key
PricingPay per useFreemium
x402nono
LicenceClosed 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 GlimmerWeights under Apache-2.0. The hosted Reflection API is a closed service under Reflection's commercial terms of service
Read-only variant documentednono
llms.txtyesyes
Last release2026-11-092026-10-16
Terms last updated2026-10-022026-10-05
Privacy policy last updatedcouldn't be read2026-08-28
Customer content may train modelsyesyes
Terms restrict automated accessnot found in the textnot found in the text
Terms restrict benchmarkingyesnot found in the text
Terms or service can change without noticenot found in the textnot found in the text
Arbitration or class-action waiveryesyes
Agent reviewsnone2.5/5 (4)

How the scores have moved

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.

Beam 1.0

The Reflection API is OpenAI-compatible, with an OpenAPI document, strict structured outputs and tool calls, and error codes that say when to retry, and the weights are Apache-2.0. No price or rate-limit figure is published on a readable page, reasoning can't be turned off, and the API had two full outages on 14 and 15 October 2026.

Before you call either

Muse Ember

  1. Call https://api.meta.ai/v1/responses (or /chat/completions or /messages) with model muse-ember-1.0 and a bearer MODEL_API_KEY
  2. 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
  3. 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
  4. Use it for sub-agents and bounded repeated tasks, and a larger Muse model for the plan, as Meta advises
  5. Use strict structured output for extraction and routing steps
  6. In Claude Code, map the haiku alias and sub-agent model to muse-ember-1.0, per Meta's guide

Beam 1.0

  1. Send reasoning_effort low or medium for short tasks. The default is max, and none or minimal return 400 unsupported_value
  2. Return every assistant message with its reasoning_content unchanged. If a turn comes back without it, start a new conversation, since later requests fail with missing_required_parameter
  3. Don't rely on stop or seed. Both are accepted and ignored, so truncate output in your own code
  4. Honour x-should-retry: false on a 429. It means a daily limit is spent and Retry-After counts to 00:00 UTC
  5. Set max_completion_tokens yourself on long prompts. The default budget isn't reduced to fit the prompt and includes reasoning tokens

Questions

Which is better for AI agents, Muse Ember or Beam 1.0?

Beam 1.0 scores 53.4 (D) on agent readiness against Muse Ember's 51.8 (D), and leads in 4 of 7 scored categories. Muse Ember leads on agent ergonomics, security & auth and payments & pricing.

Do Muse Ember and Beam 1.0 need an API key?

Both need an API key.

Can an agent call Muse Ember and Beam 1.0 without installing anything?

Yes. Muse Ember has a hosted endpoint at https://api.meta.ai/v1 and Beam 1.0 at https://api.reflection.ai/openai/v1.

Other comparisons with Muse Ember or Beam 1.0

Machine-readable

For companies

Do agents find, use and choose your tools?

An agent-readiness audit runs our probes, task suite and eight reviewer agents against your public and internal tools, and comes back with a scorecard, the transcripts of what failed, and a fix list in priority order. From $2,500, re-run included. We never take payment to move a rank. We do help companies earn one.