Llama 3 Taiwan 70B Instruct vs Llama 3.1 405B Instruct
Llama 3 Taiwan 70B Instruct (2024) and Llama 3.1 405B Instruct (2024) are compact production models from AI at Meta. Llama 3 Taiwan 70B Instruct ships a 8K-token context window, while Llama 3.1 405B Instruct 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.
Llama 3.1 405B Instruct fits 16x more tokens; pick it for long-context work and Llama 3 Taiwan 70B Instruct for tighter calls.
Specs
| Released | 2024-07-01 | 2024-07-23 |
| Context window | 8K | 128K |
| Parameters | 70B | 405B |
| Architecture | decoder only | decoder only |
| License | 1 | Open Source |
| Knowledge cutoff | - | - |
Pricing and availability
| Llama 3 Taiwan 70B Instruct | Llama 3.1 405B Instruct | |
|---|---|---|
| Input price | - | $2.4/1M tokens |
| Output price | - | $2.4/1M tokens |
| Providers |
Capabilities
| Llama 3 Taiwan 70B Instruct | Llama 3.1 405B Instruct | |
|---|---|---|
| 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 differs most on structured outputs: Llama 3.1 405B Instruct. Both models share the core language-model surface, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.
Pricing coverage is uneven: Llama 3 Taiwan 70B Instruct has no token price sourced yet and Llama 3.1 405B Instruct has $2.4/1M input tokens. Provider availability is 1 tracked routes versus 11. 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 are central to the workload. Choose Llama 3.1 405B Instruct when long-context analysis, larger context windows, and broader provider choice 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 Llama 3.1 405B Instruct?
Llama 3.1 405B Instruct 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 Llama 3.1 405B Instruct open source?
Llama 3 Taiwan 70B Instruct is listed under 1. Llama 3.1 405B Instruct is listed under Open Source. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for structured outputs, Llama 3 Taiwan 70B Instruct or Llama 3.1 405B Instruct?
Llama 3.1 405B Instruct has the clearer documented structured outputs signal in this comparison. If structured outputs is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Llama 3 Taiwan 70B Instruct and Llama 3.1 405B Instruct?
Llama 3 Taiwan 70B Instruct is available on NVIDIA NIM. Llama 3.1 405B Instruct is available on OctoAI API, Together AI, Fireworks AI, IBM watsonx, and Scale AI GenAI Platform. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3 Taiwan 70B Instruct over Llama 3.1 405B Instruct?
Llama 3.1 405B Instruct 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 Llama 3.1 405B Instruct.
Continue comparing
Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.