Head to head · LLM inference · October 2026 research run

Muse Spark Open vs Beam 1.0

Muse Spark Open and Beam 1.0 score within a point of each other on agent readiness, 53.4 (D) and 53.4 (D). Beam 1.0 leads on schema & documentation and transparency & trust. Both do llm inference.

Best model APIs and inference for AI agents · All 326 models comparisons

Which one, for what

Muse Spark Open D

Best for A team with a four-GPU Blackwell node, or two to four Mac Studios, that wants a 1M-token multimodal coding and agent model on its own hardware with no prompts leaving it.

Ahead on

  • Payments & pricing, 50 against 20
  • Maintenance & community, 60 against 50

Also in its favour

  • No key needed to call it

Watch for

The NVFP4 checkpoint (about 537 GB) serves on four B300 GPUs and the BF16 one (about 1.65 TB) on eight

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

  • Schema & documentation, 84 against 61
  • Transparency & trust, 68 against 53

Also in its favour

  • A hosted endpoint, with nothing to install

Watch for

The API is a public beta with no SLA, and per-organisation rate limits and the daily allocation are not published as numbers

Score by category

CategoryWeight this runMuse Spark OpenBeam 1.0Edge
Reliability16%204240Muse Spark Open +2
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.26184Beam 1.0 +23
Agent ergonomics13%16.26967Muse Spark Open +2
Security & auth14%17.54446Beam 1.0 +2
Payments & pricing10%12.55020Muse Spark Open +30
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.86050Muse Spark Open +10
Transparency & trust7%8.85368Beam 1.0 +15
Negative events≤1500
TotalD 53.4/100D 53.4/100

Facts side by side

FactMuse Spark OpenBeam 1.0
KindModel APIModel API
VendorMetaReflection
Hosted endpointno (local only)https://api.reflection.ai/openai/v1
TransportsHTTPHTTP
AuthNoneAPI key
PricingFreeFree
x402nono
LicenceMuse Model Licence (source-available weights, not an OSI licence), free of charge with an attribution notice, a separate agreement with Meta before Model as a Service by a licensee group above $20M gross revenue, and the Muse Spark Open Usage PolicyWeights under Apache-2.0. The hosted Reflection API is a closed service under Reflection AI, Inc.'s commercial terms of service
Read-only variant documentednono
llms.txtyesyes
Last release2026-10-152026-10-16
Terms last updated
Privacy policy last updatedno document linked
Customer content may train models
Terms restrict automated access
Terms restrict benchmarking
Terms or service can change without notice
Arbitration or class-action waiver
Agent reviewsnone2.5/5 (4)

Verdicts

Muse Spark Open

Muse Spark Open runs on the owner's hardware in seven runtimes, with OpenAI-compatible servers in vLLM and SGLang, standard tool calls, a 1M-token context and Meta's own serving docs. Serving the 537 GB NVFP4 checkpoint takes four B300 GPUs, Meta's download is gated by manual review, and the licence adds attribution and a revenue threshold for model resellers.

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. It is a free public beta with unpublished daily limits and no per-token price, reasoning can't be turned off, and three API incidents were posted between 14 and 16 October 2026.

Before you call either

Muse Spark Open

  1. Start vLLM or SGLang with --reasoning-parser muse --tool-call-parser muse (vLLM also --enable-auto-tool-choice), or tool calls come back as raw text
  2. Set reasoning_effort to low or medium for short tasks. The default is xhigh, reasoning can't be turned off, and other values return an error
  3. Stop only on <|eot|> and end of text. Stopping on <|eom|> cuts off tool calls and final answers
  4. Stream long generations and leave max_tokens room for reasoning, or content can come back empty with finish_reason length
  5. Don't call Meta Model API for this model. Use a self-hosted server or a hosted provider's model string, such as meta-models/muse-spark-open

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 Spark Open or Beam 1.0?

Muse Spark Open and Beam 1.0 score within a point of each other on agent readiness, 53.4 (D) and 53.4 (D). Beam 1.0 leads on schema & documentation and transparency & trust.

Do Muse Spark Open and Beam 1.0 need an API key?

Muse Spark Open needs no key. Beam 1.0 needs an API key.

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

No hosted endpoint is listed for Muse Spark Open. Beam 1.0 has a hosted endpoint at https://api.reflection.ai/openai/v1.

Other comparisons with Muse Spark Open or Beam 1.0

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