Llama 3.2 1B vs Phi 3.5 Mini Instruct
Llama 3.2 1B (2024) and Phi 3.5 Mini Instruct (2024) are compact production models from AI at Meta and Microsoft Research. Llama 3.2 1B ships a 128K-token context window, while Phi 3.5 Mini Instruct ships a 128K-token context window. On pricing, Llama 3.2 1B costs $0.1/1M input tokens versus $0.9/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Llama 3.2 1B is ~800% cheaper at $0.1/1M; pay for Phi 3.5 Mini Instruct only for provider fit.
Specs
| Released | 2024-09-25 | 2024-08-20 |
| Context window | 128K | 128K |
| Parameters | 1.23B | 3.8B |
| Architecture | decoder only | decoder only |
| License | Open Source | MIT |
| Knowledge cutoff | 2023-12 | - |
Pricing and availability
| Llama 3.2 1B | Phi 3.5 Mini Instruct | |
|---|---|---|
| Input price | $0.1/1M tokens | $0.9/1M tokens |
| Output price | $0.1/1M tokens | $0.9/1M tokens |
| Providers |
Capabilities
| Llama 3.2 1B | Phi 3.5 Mini 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 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.
For cost, Llama 3.2 1B lists $0.1/1M input and $0.1/1M output tokens, while Phi 3.5 Mini Instruct lists $0.9/1M input and $0.9/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3.2 1B lower by about $0.8 per million blended tokens. Availability is 1 providers versus 2, so concentration risk also matters.
Choose Llama 3.2 1B when provider fit and lower input-token cost are central to the workload. Choose Phi 3.5 Mini Instruct when provider fit and broader provider choice 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 or Phi 3.5 Mini Instruct?
Llama 3.2 1B supports 128K tokens, while Phi 3.5 Mini 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 or Phi 3.5 Mini Instruct?
Llama 3.2 1B is cheaper on tracked token pricing. Llama 3.2 1B costs $0.1/1M input and $0.1/1M output tokens. Phi 3.5 Mini Instruct costs $0.9/1M input and $0.9/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Llama 3.2 1B or Phi 3.5 Mini Instruct open source?
Llama 3.2 1B is listed under Open Source. Phi 3.5 Mini 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 3.2 1B and Phi 3.5 Mini Instruct?
Llama 3.2 1B is available on Fireworks AI. Phi 3.5 Mini Instruct is available on Fireworks AI and NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3.2 1B over Phi 3.5 Mini Instruct?
Llama 3.2 1B is ~800% cheaper at $0.1/1M; pay for Phi 3.5 Mini Instruct only for provider fit. If your workload also depends on provider fit, start with Llama 3.2 1B; if it depends on provider fit, run the same evaluation with Phi 3.5 Mini Instruct.
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Last reviewed: 2026-04-15. Data sourced from public model cards and provider documentation.