Head to head · LLM inference · October 2026 research run

Muse Spark Open vs SiliconFlow

Muse Spark Open scores 53.4 (D) on agent readiness against SiliconFlow's 46.7 (D), and leads in 6 of 7 scored categories. Both do llm inference.

Best model APIs and inference for AI agents · All 210 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

  • Reliability, 42 against 33
  • Agent ergonomics, 69 against 60
  • Security & auth, 44 against 39
  • Payments & pricing, 50 against 35
  • Maintenance & community, 60 against 47

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

SiliconFlow D

Best for Agents that want recent open-weight chat models, plus image, video and speech, behind one OpenAI-style key at low per-token prices, and can live without a status page or SLA.

Also in its favour

  • A hosted endpoint, with nothing to install

Watch for

No status page, incident history or SLA was found, and the terms disclaim any uptime or availability commitment

Score by category

CategoryWeight this runMuse Spark OpenSiliconFlowEdge
Reliability16%204233Muse Spark Open +9
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.26165SiliconFlow +4
Agent ergonomics13%16.26960Muse Spark Open +9
Security & auth14%17.54439Muse Spark Open +5
Payments & pricing10%12.55035Muse Spark Open +15
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.86047Muse Spark Open +13
Transparency & trust7%8.85351Muse Spark Open +2
Negative events≤1500
TotalD 53.4/100D 46.7/100

Facts side by side

FactMuse Spark OpenSiliconFlow
KindModel APIModel API
VendorMetaSiliconFlow Labs Pte. Ltd.
Hosted endpointno (local only)https://api.siliconflow.com/v1
TransportsHTTPHTTP
AuthNoneAPI key
PricingFreePay per use
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 PolicyProprietary service under the SiliconFlow Terms of Use
Read-only variant documentednono
llms.txtyesyes
Last release2026-10-152026-09-14
Terms last updatedno date given
Privacy policy last updatedno document linkedno date given
Customer content may train modelsnot found in the text
Terms restrict automated accessyes
Terms restrict benchmarkingyes
Terms or service can change without noticeyes
Arbitration or class-action waiveryes
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.

SiliconFlow

Per-token prices for every listed model are public, and one key reaches chat, embeddings, reranking, image, video and speech through OpenAI-style and Anthropic-style routes. No status page, SLA, security page or official SDK was found, release notes stop at 11 June 2026, and several model removals are dated the same day as their notice.

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

SiliconFlow

  1. Set the OpenAI client's base URL to https://api.siliconflow.com/v1, or the Anthropic client's to https://api.siliconflow.com/, and send the key as a Bearer token
  2. Take model ids from the model pages or GET /v1/models, not from the OpenAPI enum or the function calling guide, which list removed models
  3. Check the model field of each response. On 11 June 2026 traffic for GLM-5 and Kimi-K2.5 was routed to successor models
  4. Use response_format of json_object only where the model page says JSON Mode is supported, and keep max_tokens about 10,000 below the context length
  5. Set the Claude Code environment variables by hand. The automated route pipes a script from an Amazon S3 bucket into bash

Questions

Which is better for AI agents, Muse Spark Open or SiliconFlow?

Muse Spark Open scores 53.4 (D) on agent readiness against SiliconFlow's 46.7 (D), and leads in 6 of 7 scored categories.

Do Muse Spark Open and SiliconFlow need an API key?

Muse Spark Open needs no key. SiliconFlow needs an API key.

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

No hosted endpoint is listed for Muse Spark Open. SiliconFlow has a hosted endpoint at https://api.siliconflow.com/v1.

Other comparisons with Muse Spark Open or SiliconFlow

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