Llama 3.2 1B Instruct vs Phi 3.5 MoE Instruct
Llama 3.2 1B Instruct (2024) and Phi 3.5 MoE Instruct (2024) are compact production models from AI at Meta and Microsoft Research. Llama 3.2 1B Instruct ships a 128K-token context window, while Phi 3.5 MoE Instruct ships a 128K-token context window. On pricing, Llama 3.2 1B Instruct costs $0.03/1M input tokens versus $0.5/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Llama 3.2 1B Instruct is ~1752% cheaper at $0.03/1M; pay for Phi 3.5 MoE Instruct only for provider fit.
Specs
| Released | 2024-09-25 | 2024-08-20 |
| Context window | 128K | 128K |
| Parameters | 1.23B | 16x3.8B (42B, 6.6B active) |
| Architecture | decoder only | decoder only |
| License | Open Source | MIT |
| Knowledge cutoff | 2023-12 | - |
Pricing and availability
| Llama 3.2 1B Instruct | Phi 3.5 MoE Instruct | |
|---|---|---|
| Input price | $0.03/1M tokens | $0.5/1M tokens |
| Output price | $0.2/1M tokens | $0.5/1M tokens |
| Providers |
Capabilities
| Llama 3.2 1B 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 3.2 1B 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 3.2 1B Instruct lists $0.03/1M input and $0.2/1M 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 3.2 1B Instruct lower by about $0.42 per million blended tokens. Availability is 5 providers versus 1, so concentration risk also matters.
Choose Llama 3.2 1B Instruct when provider fit, lower input-token cost, and broader provider choice are central to the workload. Choose Phi 3.5 MoE Instruct when provider fit 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 3.2 1B Instruct or Phi 3.5 MoE Instruct?
Llama 3.2 1B 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 3.2 1B Instruct or Phi 3.5 MoE Instruct?
Llama 3.2 1B Instruct is cheaper on tracked token pricing. Llama 3.2 1B Instruct costs $0.03/1M input and $0.2/1M 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 3.2 1B Instruct or Phi 3.5 MoE Instruct open source?
Llama 3.2 1B 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 3.2 1B Instruct or Phi 3.5 MoE Instruct?
Llama 3.2 1B 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 3.2 1B Instruct and Phi 3.5 MoE Instruct?
Llama 3.2 1B Instruct is available on OpenRouter, Fireworks AI, NVIDIA NIM, Bitdeer AI, and AWS Bedrock. 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 3.2 1B Instruct over Phi 3.5 MoE Instruct?
Llama 3.2 1B Instruct is ~1752% cheaper at $0.03/1M; pay for Phi 3.5 MoE Instruct only for provider fit. If your workload also depends on provider fit, start with Llama 3.2 1B Instruct; if it depends on provider fit, 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.