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 $0.40/1M input tokens versus $0.50/1M for the alternative. 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 70B Instruct for tighter calls.
Decision scorecard
Local evidence first| Signal | Llama 3 70B Instruct | Phi 3.5 MoE Instruct |
|---|---|---|
| Best for | provider-routed production | general production evaluation |
| Decision fit | Coding, Classification, and JSON / Tool use | Long context |
| Context window | 8k | 128k |
| Cheapest output | $0.40/1M tokens | $0.50/1M tokens |
| Provider routes | 18 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Llama 3 70B Instruct has the lower cheapest tracked output price at $0.40/1M tokens.
- Llama 3 70B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Llama 3 70B Instruct uniquely exposes Structured outputs in local model data.
- Local decision data tags Llama 3 70B Instruct for Coding, Classification, and JSON / Tool use.
- 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 70B Instruct
$420
Cheapest tracked route/tier: Hyperbolic AI Inference
Phi 3.5 MoE Instruct
$525
Cheapest tracked route/tier: Fireworks AI
Estimated monthly gap: $105. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Fireworks AI; start route-level A/B tests there.
- Phi 3.5 MoE Instruct is $0.10/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Structured outputs before moving production traffic.
- Provider overlap exists on Fireworks AI; start route-level A/B tests there.
- Llama 3 70B Instruct is $0.10/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Llama 3 70B Instruct adds Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| 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 | 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 70B Instruct | Phi 3.5 MoE Instruct |
|---|---|---|
| Input price | $0.40/1M tokens | $0.50/1M tokens |
| Output price | $0.40/1M tokens | $0.50/1M tokens |
| Providers |
Capabilities
| Capability | Llama 3 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 | Yes | 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 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 $0.40/1M input and $0.40/1M output tokens on the cheapest tracked provider, while Phi 3.5 MoE Instruct lists $0.50/1M input and $0.50/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3 70B Instruct lower by about $0.10 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 $0.40/1M input and $0.40/1M output tokens. Phi 3.5 MoE Instruct costs $0.50/1M input and $0.50/1M 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 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.
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: 2026-05-22. Data sourced from public model cards and provider documentation.