Muse Spark Open vs OpenAI API

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.

OpenAI API

Official OpenAPI document and an llms.txt index. Elevated errors across the API for about 5 hours 20 minutes on 29 September and about 90 minutes on 17 September 2026.

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

OpenAI API

1. Build on the Responses API. Astra calls tools only there
2. Use gpt-6-luna for routing and extraction and gpt-6.1-sol for most work. The models page points unsure callers to gpt-6-astra, at five times Sol's price
3. Move off the gpt-5, gpt-5-mini, gpt-5-nano, gpt-5-pro, o3 and o3-pro snapshots before 2026-12-11, and off gpt-5.1, gpt-5.3-codex and gpt-5.4-nano before 2027-04-01
4. Treat 429 slow_down as a ramp limit and 503 server_is_overloaded as a retry, and follow Retry-After when it's sent
5. Prompts over 272K tokens cost double on input. Trim before you pay for it