Muse Spark (Meta) vs StepFun Step 3.5 Flash

Which one should you pick? Here's the full breakdown.

Our Pick

Muse Spark (Meta)

A
8.8/10

Meta's first model from its Superintelligence Lab -- natively multimodal with Contemplating mode for multi-agent reasoning

StepFun Step 3.5 Flash

B
7.8/10

StepFun's (China) agent-focused open-weight model -- Step 3.5 Flash launched 2026-02-01. 196B sparse MoE, ~11B active. Benchmarks slightly ahead of DeepSeek V3.2 at over 3x smaller total size. Step 3 (321B / 38B active, Apache 2.0) and Step3-VL-10B multimodal also in the family

CategoryMuse Spark (Meta)StepFun Step 3.5 Flash
Ease of Use9.06.0
Output Quality8.08.0
Value10.09.0
Features8.08.0
Overall8.87.8

Pricing Comparison

FeatureMuse Spark (Meta)StepFun Step 3.5 Flash
Free TierYesYes
Starting Price$0$0

Benchmark Head-to-Head

Muse Spark benchmarks — StepFun Step 3.5 Flash has no published benchmarks

BenchmarkScore
MMLU89%
GPQA Diamond86%
HumanEval91%
Humanity's Last Exam58%

Which Should You Pick?

Pick Muse Spark (Meta) if...

  • Easier to use (9 vs 6)
  • Better value for money (10/10)

Anyone who wants frontier-level AI for free. If you use Meta's apps (Facebook, Instagram, WhatsApp) already, Muse Spark is the most accessible high-quality LLM with zero cost.

Visit Muse Spark (Meta)

Pick StepFun Step 3.5 Flash if...

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 3.5 Flash

Our Verdict

Muse Spark (Meta) is the clear winner here with 8.8/10 vs 7.8/10. StepFun Step 3.5 Flash isn't bad, but Muse Spark (Meta) outperforms it across the board. Pick StepFun Step 3.5 Flash only if 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.