Llama 3 Taiwan 70B Instruct vs StepFun Step-2
Llama 3 Taiwan 70B Instruct (2024) and StepFun Step-2 (2025) are compact production models from AI at Meta and StepFun. Llama 3 Taiwan 70B Instruct ships a 8K-token context window, while StepFun Step-2 ships a 128k-token context window. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
StepFun Step-2 fits 16x more tokens; pick it for long-context work and Llama 3 Taiwan 70B Instruct for tighter calls.
Specs
| Released | 2024-07-01 | 2025-10-15 |
| Context window | 8K | 128k |
| Parameters | 70B | — |
| Architecture | decoder only | - |
| License | 1 | Proprietary |
| Knowledge cutoff | - | - |
Pricing and availability
| Llama 3 Taiwan 70B Instruct | StepFun Step-2 | |
|---|---|---|
| Input price | - | - |
| Output price | - | - |
| Providers | - |
Pricing not yet sourced for either model.
Capabilities
| Llama 3 Taiwan 70B Instruct | StepFun Step-2 | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint is close: both models cover the core production surface. That makes context budget, benchmark fit, and provider maturity more important than a simple checklist. If your application depends on one integration detail, verify it against the provider route you plan to use, not just the base model listing.
Pricing coverage is uneven: Llama 3 Taiwan 70B Instruct has no token price sourced yet and StepFun Step-2 has no token price sourced yet. Provider availability is 1 tracked routes versus 0. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Llama 3 Taiwan 70B Instruct when provider fit and broader provider choice are central to the workload. Choose StepFun Step-2 when long-context analysis and larger context windows are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.
FAQ
Which has a larger context window, Llama 3 Taiwan 70B Instruct or StepFun Step-2?
StepFun Step-2 supports 128k tokens, while Llama 3 Taiwan 70B Instruct supports 8K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Llama 3 Taiwan 70B Instruct or StepFun Step-2 open source?
Llama 3 Taiwan 70B Instruct is listed under 1. StepFun Step-2 is listed under Proprietary. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Where can I run Llama 3 Taiwan 70B Instruct and StepFun Step-2?
Llama 3 Taiwan 70B Instruct is available on NVIDIA NIM. StepFun Step-2 is available on the tracked providers still being sourced. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3 Taiwan 70B Instruct over StepFun Step-2?
StepFun Step-2 fits 16x more tokens; pick it for long-context work and Llama 3 Taiwan 70B Instruct for tighter calls. If your workload also depends on provider fit, start with Llama 3 Taiwan 70B Instruct; if it depends on long-context analysis, run the same evaluation with StepFun Step-2.
Continue comparing
Last reviewed: 2026-04-18. Data sourced from public model cards and provider documentation.