Muse Spark + Muse Wonder (Meta)
Free tier available
- Muse app (Free)$0
- Muse Power$20/mo
- Muse Maximum$100/mo
Our pickMuse Spark + Muse Wonder (Meta)

StepFun Step 3.7 Flash
Tier-list head-to-head. Muse Spark + Muse Wonder (Meta) takes the A-tier slot — here's the breakdown.
Spec sheet
| Tier | A-tierwin | B-tier |
| Overall score | 8.8 / 10win | 7.8 / 10 |
| Free tier | Yes | Yes |
| Starting price | $0 | $0 |
| Best for | Developers who want a cheap, capable agentic model (Muse Spark 1. | Teams building agent systems on Chinese open-weight foundations who want something other than DeepSeek or Q… |
| Last reviewed | 2026-10-17 | 2026-06-10 |
Head-to-head
Rated 1-10 on the same rubric across all 130 tools we cover.
What you'll pay
Look past the headline number -- entry-tier limits drive most cost surprises.
Free tier available
Free tier available
Muse Spark Open (vendor model card, xhigh effort; Muse Wonder's table is image-only, numbers not recorded) benchmarks — StepFun Step 3.7 Flash has no published benchmarks
| Benchmark | Description | Score |
|---|---|---|
| Artificial Analysis Intelligence Index | 39 | |
| MCP-Atlas | 89.2% | |
| OSWorld Verified | 80.4% | |
| Toolathlon Verified | 72.8% | |
| JobBench | 61.8% | |
| HLE-Diamond (no tools) | 26.1% |
The decision
Use-case anchors and category strengths, side by side.
Developers who want a cheap, capable agentic model (Muse Spark 1.3 on the Meta Model API), teams that want a frontier open-weight model they can self-host (Muse Spark Open), local-agent builders on one consumer GPU (Muse Glimmer), and US consumers who want an agent that actually does errands through the Muse app.
Visit Muse Spark + Muse Wonder (Meta)Teams building agent systems on Chinese open-weight foundations who want something other than DeepSeek or Qwen, especially if agentic tool-use is the primary workload. Also good for Chinese-market products where StepFun's domestic tuning advantages matter. And for anyone looking to add diversity to their open-weight evaluation matrix beyond the top-3 Chinese labs.
Visit StepFun Step 3.7 FlashBottom line
Muse Spark + Muse Wonder (Meta) is the clear winner: 8.8/10 (A-tier) versus 7.8/10 (B-tier). StepFun Step 3.7 Flash isn't a bad tool, but on every category that drives the overall score, Muse Spark + Muse Wonder (Meta) comes out ahead. The tier gap is repeatable -- not methodology noise -- and the day-to-day experience reflects it.
Pricing-wise, both tools have a free tier (Muse Spark + Muse Wonder (Meta) starts $0, StepFun Step 3.7 Flash starts $0), so you can test either without committing. Compare what each free tier actually unlocks -- usage caps, model access, and feature gates differ a lot more than the headline price suggests, especially as both vendors have tightened limits in 2026.
By use case: pick Muse Spark + Muse Wonder (Meta) when developers who want a cheap, capable agentic model (muse spark 1. Pick StepFun Step 3.7 Flash when teams building agent systems on chinese open-weight foundations who want something other than deepseek or qwen, especially if agentic tool-use is the primary workload. The two tools aren't fighting for the same person -- they're aiming at adjacent jobs that occasionally overlap. If you're squarely in Muse Spark + Muse Wonder (Meta)'s lane, the tier-list ranking and the use-case fit point the same direction; if you're in StepFun Step 3.7 Flash's lane, the score gap matters less than the fit.
Bottom line: Muse Spark + Muse Wonder (Meta) is the better tool for most people right now. Pick StepFun Step 3.7 Flash only when teams building agent systems on chinese open-weight foundations who want something other than deepseek or qwen, especially if agentic tool-use is the primary workload -- that's its lane, and inside that lane it still earns its place.
Keep digging
Full Muse Spark + Muse Wonder (Meta) review
Tier A · 8.8/10
Full StepFun Step 3.7 Flash review
Tier B · 7.8/10
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Built from our daily AI-tool sweep, last touched October 17, 2026. Honest tier-list reviews — no affiliate-link pieces disguised as advice. See the rubric or how we review.