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Llama 3.1 405B Instruct vs Phi 3.5 MoE Instruct

Llama 3.1 405B Instruct (2024) and Phi 3.5 MoE Instruct (2024) are compact production models from AI at Meta and Microsoft Research. Llama 3.1 405B Instruct ships a 128K-token context window, while Phi 3.5 MoE Instruct ships a 128K-token context window. On pricing, Phi 3.5 MoE Instruct costs $0.5/1M input tokens versus $2.4/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.

Phi 3.5 MoE Instruct is ~380% cheaper at $0.5/1M; pay for Llama 3.1 405B Instruct only for provider fit.

Decision scorecard

Local evidence first
SignalLlama 3.1 405B InstructPhi 3.5 MoE Instruct
Decision fitRAG, Long context, and ClassificationLong context
Context window128K128K
Cheapest output$2.4/1M tokens$0.5/1M tokens
Provider routes11 tracked1 tracked
Shared benchmarks0 rows0 rows

Decision tradeoffs

Choose Llama 3.1 405B Instruct when...
  • Llama 3.1 405B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
  • Llama 3.1 405B Instruct uniquely exposes Structured outputs in local model data.
  • Local decision data tags Llama 3.1 405B Instruct for RAG, Long context, and Classification.
Choose Phi 3.5 MoE Instruct when...
  • Phi 3.5 MoE Instruct has the lower cheapest tracked output price at $0.5/1M tokens.
  • 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 prices on this page.

Lower estimate Phi 3.5 MoE Instruct

Llama 3.1 405B Instruct

$2,520

Cheapest tracked route: AWS Bedrock

Phi 3.5 MoE Instruct

$525

Cheapest tracked route: Fireworks AI

Estimated monthly gap: $1,995. Batch, cache, and negotiated pricing are excluded from this local estimate.

Switch friction

Llama 3.1 405B Instruct -> Phi 3.5 MoE Instruct
  • Provider overlap exists on Fireworks AI; start route-level A/B tests there.
  • Phi 3.5 MoE Instruct is $1.9/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
  • Check replacement coverage for Structured outputs before moving production traffic.
Phi 3.5 MoE Instruct -> Llama 3.1 405B Instruct
  • Provider overlap exists on Fireworks AI; start route-level A/B tests there.
  • Llama 3.1 405B Instruct is $1.9/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
  • Llama 3.1 405B Instruct adds Structured outputs in local capability data.

Specs

Specification
Released2024-07-232024-08-20
Context window128K128K
Parameters405B16x3.8B (42B, 6.6B active)
Architecturedecoder onlydecoder only
LicenseOpen SourceMIT
Knowledge cutoff--

Pricing and availability

Pricing attributeLlama 3.1 405B InstructPhi 3.5 MoE Instruct
Input price$2.4/1M tokens$0.5/1M tokens
Output price$2.4/1M tokens$0.5/1M tokens
Providers

Capabilities

CapabilityLlama 3.1 405B InstructPhi 3.5 MoE Instruct
VisionNoNo
MultimodalNoNo
ReasoningNoNo
Function callingNoNo
Tool useNoNo
Structured outputsYesNo
Code executionNoNo

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.

For cost, Llama 3.1 405B Instruct lists $2.4/1M input and $2.4/1M 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 Phi 3.5 MoE Instruct lower by about $1.9 per million blended tokens. Availability is 11 providers versus 1, so concentration risk also matters.

Choose Llama 3.1 405B Instruct when provider fit and broader provider choice are central to the workload. Choose Phi 3.5 MoE Instruct when provider fit and lower input-token cost 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 3.1 405B Instruct or Phi 3.5 MoE Instruct?

Llama 3.1 405B Instruct supports 128K tokens, while Phi 3.5 MoE Instruct supports 128K 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 3.1 405B Instruct or Phi 3.5 MoE Instruct?

Phi 3.5 MoE Instruct is cheaper on tracked token pricing. Llama 3.1 405B Instruct costs $2.4/1M input and $2.4/1M 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 3.1 405B Instruct or Phi 3.5 MoE Instruct open source?

Llama 3.1 405B 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 3.1 405B Instruct or Phi 3.5 MoE 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 3.1 405B Instruct and Phi 3.5 MoE Instruct?

Llama 3.1 405B Instruct is available on OctoAI API (Deprecated), Together AI, Fireworks AI, IBM watsonx, and Scale AI GenAI Platform. 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 3.1 405B Instruct over Phi 3.5 MoE Instruct?

Phi 3.5 MoE Instruct is ~380% cheaper at $0.5/1M; pay for Llama 3.1 405B Instruct only for provider fit. If your workload also depends on provider fit, start with Llama 3.1 405B Instruct; if it depends on provider fit, run the same evaluation with Phi 3.5 MoE Instruct.

Continue comparing

Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.