Llama 3 70B Instruct vs Phi 3.5 MoE Instruct
Llama 3 70B Instruct (2024) and Phi 3.5 MoE Instruct (2024) are compact production models from AI at Meta and Microsoft Research. Llama 3 70B Instruct ships a 8K-token context window, while Phi 3.5 MoE Instruct ships a 128K-token context window. On pricing, Llama 3 70B Instruct costs $0.4/1M input tokens versus $0.5/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Phi 3.5 MoE Instruct fits 16x more tokens; pick it for long-context work and Llama 3 70B Instruct for tighter calls.
Specs
| Released | 2024-04-18 | 2024-08-20 |
| Context window | 8K | 128K |
| Parameters | 70B | 16x3.8B (42B, 6.6B active) |
| Architecture | decoder only | decoder only |
| License | Open Source | MIT |
| Knowledge cutoff | - | - |
Pricing and availability
| Llama 3 70B Instruct | Phi 3.5 MoE Instruct | |
|---|---|---|
| Input price | $0.4/1M tokens | $0.5/1M tokens |
| Output price | $0.4/1M tokens | $0.5/1M tokens |
| Providers |
Capabilities
| Llama 3 70B Instruct | Phi 3.5 MoE 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 70B 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.
For cost, Llama 3 70B Instruct lists $0.4/1M input and $0.4/1M output tokens, while Phi 3.5 MoE Instruct lists $0.5/1M input and $0.5/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3 70B Instruct lower by about $0.1 per million blended tokens. Availability is 18 providers versus 1, so concentration risk also matters.
Choose Llama 3 70B Instruct when provider fit, lower input-token cost, and broader provider choice 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.
FAQ
Which has a larger context window, Llama 3 70B Instruct or Phi 3.5 MoE Instruct?
Phi 3.5 MoE Instruct supports 128K tokens, while Llama 3 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.
Which is cheaper, Llama 3 70B Instruct or Phi 3.5 MoE Instruct?
Llama 3 70B Instruct is cheaper on tracked token pricing. Llama 3 70B Instruct costs $0.4/1M input and $0.4/1M output tokens. Phi 3.5 MoE Instruct costs $0.5/1M input and $0.5/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Llama 3 70B Instruct or Phi 3.5 MoE Instruct open source?
Llama 3 70B Instruct is listed under Open Source. 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.
Which is better for structured outputs, Llama 3 70B Instruct or Phi 3.5 MoE Instruct?
Llama 3 70B 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 70B Instruct and Phi 3.5 MoE Instruct?
Llama 3 70B Instruct is available on GCP Vertex AI, AWS Bedrock, Microsoft Foundry, NVIDIA NIM, and DeepInfra. 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 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 70B Instruct for tighter calls. If your workload also depends on provider fit, start with Llama 3 70B Instruct; if it depends on long-context analysis, run the same evaluation with Phi 3.5 MoE Instruct.
Continue comparing
Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.