Olmo 3 (AI2) vs StepFun Step 3.5 Flash
Which one should you pick? Here's the full breakdown.
Olmo 3 (AI2)
Allen Institute for AI's fully-open frontier reasoning models -- Olmo 3 family (2025-11-20) includes 7B and 32B sizes, four variants (Base, Think, Instruct, RLZero). Apache 2.0 with fully open data + checkpoints + training logs. Olmo 3-Think 32B matches Qwen3-32B-Thinking at 6x fewer training tokens
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 | Olmo 3 (AI2) | StepFun Step 3.5 Flash |
|---|---|---|
| Ease of Use | 6.0 | 6.0 |
| Output Quality | 8.0 | 8.0 |
| Value | 9.5 | 9.0 |
| Features | 8.0 | 8.0 |
| Overall | 7.9 | 7.8 |
Pricing Comparison
| Feature | Olmo 3 (AI2) | StepFun Step 3.5 Flash |
|---|---|---|
| Free Tier | Yes | Yes |
| Starting Price | $0 | $0 |
Which Should You Pick?
Pick Olmo 3 (AI2) if...
AI researchers doing reproducibility work, training-data studies, instruction-tuning research, or RLHF-free (RLZero) experimentation. Also valuable for academic institutions and non-profits that want to use an open-weight model whose provenance is fully auditable. Good as a teaching / learning model where inspecting checkpoints matters.
Visit Olmo 3 (AI2)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 FlashOur Verdict
Olmo 3 (AI2) and StepFun Step 3.5 Flash are extremely close overall. Your choice comes down to specific needs -- Olmo 3 (AI2) is better for ai researchers doing reproducibility work, training-data studies, instruction-tuning research, or rlhf-free (rlzero) experimentation, 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.