Llama 4 (Meta) vs StepFun Step 3.5 Flash
Which one should you pick? Here's the full breakdown.
Llama 4 (Meta)
Meta's open-weights flagship family -- Scout (10M context), Maverick (multimodal 400B MoE), Behemoth in preview
StepFun Step 3.5 Flash
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
| Category | Llama 4 (Meta) | StepFun Step 3.5 Flash |
|---|---|---|
| Ease of Use | 5.0 | 6.0 |
| Output Quality | 8.5 | 8.0 |
| Value | 9.0 | 9.0 |
| Features | 9.0 | 8.0 |
| Overall | 7.9 | 7.8 |
Pricing Comparison
| Feature | Llama 4 (Meta) | StepFun Step 3.5 Flash |
|---|---|---|
| Free Tier | Yes | Yes |
| Starting Price | $0 | $0 |
Benchmark Head-to-Head
Llama 4 Maverick (17B/400B MoE) benchmarks — StepFun Step 3.5 Flash has no published benchmarks
| Benchmark | Description | Score |
|---|---|---|
| MMLU-Pro | Harder multi-subject reasoning | 80.5% |
| GPQA Diamond | Graduate-level science questions | 69.8% |
| HumanEval | Python code generation | 88% |
| MMMU (multimodal) | 73.4% |
Which Should You Pick?
Pick Llama 4 (Meta) if...
- ✓More features (9 vs 8)
Developers and teams who need a permissively-licensed open-weights model with strong tooling, long context (Scout), or multimodal (Maverick). Safe default choice given the ecosystem.
Visit Llama 4 (Meta)Pick StepFun Step 3.5 Flash if...
- ✓Easier to use (6 vs 5)
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 FlashOur Verdict
Llama 4 (Meta) and StepFun Step 3.5 Flash are extremely close overall. Your choice comes down to specific needs -- Llama 4 (Meta) is better for developers and teams who need a permissively-licensed open-weights model with strong tooling, long context (scout), or multimodal (maverick), while StepFun Step 3.5 Flash works best for 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.