Llama 3 Taiwan 70B Instruct vs Phi 3.5 MoE Instruct
Llama 3 Taiwan 70B Instruct (2024) and Phi 3.5 MoE Instruct (2024) are compact production models from AI at Meta and Microsoft Research. Llama 3 Taiwan 70B Instruct ships a 8k-token context window, while Phi 3.5 MoE Instruct ships a 128k-token context window. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Phi 3.5 MoE Instruct fits 16x more tokens; pick it for long-context work and Llama 3 Taiwan 70B Instruct for tighter calls.
Decision scorecard
Local evidence first| Signal | Llama 3 Taiwan 70B Instruct | Phi 3.5 MoE Instruct |
|---|---|---|
| Best for | general production evaluation | general production evaluation |
| Decision fit | General | Long context |
| Context window | 8k | 128k |
| Cheapest output | - | $0.50/1M tokens |
| Provider routes | 1 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Use Llama 3 Taiwan 70B Instruct when your own prompt tests beat the comparison signals; the local data does not show a decisive standalone advantage yet.
- Phi 3.5 MoE Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags Phi 3.5 MoE Instruct for Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Llama 3 Taiwan 70B Instruct
Unavailable
No complete token price in local provider data
Phi 3.5 MoE Instruct
$525
Cheapest tracked route/tier: Fireworks AI
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Llama 3 Taiwan 70B Instruct and Phi 3.5 MoE Instruct; plan for SDK, billing, or endpoint changes.
- No overlapping tracked provider route is sourced for Phi 3.5 MoE Instruct and Llama 3 Taiwan 70B Instruct; plan for SDK, billing, or endpoint changes.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-07-01 | 2024-08-20 |
| Context window | 8k | 128k |
| Parameters | 70B | 16x3.8B (42B, 6.6B active) |
| Architecture | decoder only | decoder only |
| License | Llama 3 Community | MIT(OSI) |
| Openness | Open weights | Open source |
| Commercial use | Commercial use with conditions | Commercial use allowed |
| Knowledge cutoff | 2023-12 | 2023-10 |
Pricing and availability
| Pricing attribute | Llama 3 Taiwan 70B Instruct | Phi 3.5 MoE Instruct |
|---|---|---|
| Input price | - | $0.50/1M tokens |
| Output price | - | $0.50/1M tokens |
| Providers |
Capabilities
| Capability | Llama 3 Taiwan 70B Instruct | Phi 3.5 MoE Instruct |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | No |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
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 Phi 3.5 MoE Instruct has $0.50/1M input tokens. Provider availability is 1 tracked routes versus 1. 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 Phi 3.5 MoE Instruct 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 Phi 3.5 MoE Instruct?
Phi 3.5 MoE 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 Phi 3.5 MoE Instruct open source?
Llama 3 Taiwan 70B Instruct is listed under Llama 3 Community. Phi 3.5 MoE Instruct is listed under MIT. 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 Phi 3.5 MoE Instruct?
Llama 3 Taiwan 70B Instruct is available on NVIDIA NIM. Phi 3.5 MoE Instruct is available on Fireworks AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3 Taiwan 70B Instruct over Phi 3.5 MoE Instruct?
Phi 3.5 MoE 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 Phi 3.5 MoE Instruct.
Continue comparing
Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.