Meta Opens Muse Spark 1.1 to Developers at $1.25/M — Leads 4 of 12 Agentic Benchmarks
Meta Superintelligence Labs has released Muse Spark 1.1, a multimodal reasoning model built for agentic tasks, alongside the public preview of its first developer-facing API. The Meta Model API is live today in the US, OpenAI SDK-compatible, and starts at $1.25 per million input tokens and $4.25 per million output tokens. New accounts receive $20 in free credits.
That pricing undercuts Claude Sonnet 5 ($2/$10 introductory) by a substantial margin, and sits far below Claude Opus 4.8 and GPT-5.5. Three months after Muse Spark’s initial April launch with no API access, Meta has moved from a consumer-first release to a direct play for developer budgets.
Benchmark Results Across 12 Tests
Meta published a comparison table covering Muse Spark 1.1, the original Muse Spark, Gemini 3.1 Pro, Claude Opus 4.8, and GPT-5.5 across three categories: agents, coding, and multimodal.
Where Muse Spark 1.1 leads:
| Benchmark | Muse Spark 1.1 | Opus 4.8 | GPT-5.5 |
|---|---|---|---|
| MCP Atlas (tool use) | 88.1 | ~79 | ~80 |
| JobBench (professional tool use) | 54.7 | 48.4 | 38.3 |
| HLE with Tools | 62.1 | 57.9 | — |
| Finance Agent v2 | 57.2 | 53.9 | 51.8 |
The JobBench margin is the headline result. At 54.7 against Opus 4.8’s 48.4 and GPT-5.5’s 38.3, it is the widest gap in the entire release table.
Where Muse Spark 1.1 trails:
| Benchmark | Muse Spark 1.1 | Opus 4.8 | GPT-5.5 |
|---|---|---|---|
| Terminal-Bench 2.1 | 80.0 | 82.7 | 83.4 |
| SWE-Bench Pro | 61.5 | 69.2 | — |
| DeepSWE 1.1 | 53.3 | 59.0 | 67.0 |
| OSWorld-Verified (computer use) | 80.8 | 83.4 | — |
| Toolathlon-Verified | 75.6 | 76.2 | — |
Coding remains Muse Spark 1.1’s weakest area. The 7.7-point gap on SWE-Bench Pro against Opus 4.8 is meaningful. On DeepSWE 1.1 — a long-horizon agentic coding test — Muse Spark 1.1 scores 53.3 against GPT-5.5’s 67.0, a 13.7-point deficit. That said, the original Muse Spark scored just 10.0 on the same test, so the internal trajectory is steep.
On VALS-AI’s independent evaluation, Muse Spark 1.1 ranks fourth overall, while being the best-priced model in that tier.
Muse Spark 1.1 leads 4 of the 12 benchmarks in Meta’s table. Opus 4.8 leads 5, GPT-5.5 leads 3.
Architecture and Agentic Design
The model is built to operate both as a main orchestrator and as a subagent. In orchestrator mode, it gathers context, builds a plan, and delegates to parallel subagents. As a subagent, it runs tasks and escalates when appropriate. Meta says it zero-shot generalises to new native tools, MCP servers, and custom skills without targeted fine-tuning.
Muse Spark 1.1 self-manages its 1M-token context window, deciding what to retain, retrieve, and compress during long agentic runs. This is positioned as central to multi-session project work.
The Closed Weights Decision
Muse Spark 1.1 ships without open weights. Like its predecessor, it is a proprietary closed model — a departure from the Llama strategy that made Meta a hero in the open-source AI community. No Apache 2.0 weights are forthcoming, at least at launch. The positioning is as a commercial API product, not a foundation for community fine-tuning.
Muse Image, Meta’s image generation model, is not available through the API at launch.
Why This Matters
Before today, Meta had no public developer API for any frontier-class model. Muse Spark 1.1 changes that, and does it at pricing that forces a comparison against Sonnet 5 — not against the models it beats in specific agent benchmarks. Developers building tool-heavy or finance-adjacent agents have a credible lower-cost option for the first time. For pure coding workloads, Opus 4.8 and GPT-5.5 remain ahead.