Llama 3.2 1B Instruct vs Phi-4 Mini
Llama 3.2 1B Instruct (2024) and Phi-4 Mini (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-4 Mini ships a not-yet-sourced context window. On MMLU PRO, Phi-4 Mini leads by 32.8 pts. On pricing, Llama 3.2 1B Instruct costs $0.03/1M input tokens versus $0.05/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Llama 3.2 1B Instruct is ~85% cheaper at $0.03/1M; pay for Phi-4 Mini only for provider fit.
Decision scorecard
Local evidence first| Signal | Llama 3.2 1B Instruct | Phi-4 Mini |
|---|---|---|
| Decision fit | Coding, RAG, and Long context | Classification |
| Context window | 128K | — |
| Cheapest output | $0.2/1M tokens | $0.15/1M tokens |
| Provider routes | 5 tracked | 3 tracked |
| Shared benchmarks | 3 rows | MMLU PRO leader |
Decision tradeoffs
- Llama 3.2 1B Instruct leads the largest shared benchmark signal on Google-Proof Q&A by 0.4 points.
- Llama 3.2 1B Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Llama 3.2 1B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Llama 3.2 1B Instruct uniquely exposes Structured outputs in local model data.
- Local decision data tags Llama 3.2 1B Instruct for Coding, RAG, and Long context.
- Phi-4 Mini leads the largest shared benchmark signal on MMLU PRO by 32.8 points.
- Phi-4 Mini has the lower cheapest tracked output price at $0.15/1M tokens.
- Local decision data tags Phi-4 Mini for Classification.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Llama 3.2 1B Instruct
$71.60
Cheapest tracked route: OpenRouter
Phi-4 Mini
$77.50
Cheapest tracked route: Novita AI
Estimated monthly gap: $5.90. Batch, cache, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Fireworks AI and NVIDIA NIM; start route-level A/B tests there.
- Phi-4 Mini is $0.05/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Structured outputs before moving production traffic.
- Provider overlap exists on Fireworks AI and NVIDIA NIM; start route-level A/B tests there.
- Llama 3.2 1B Instruct is $0.05/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Llama 3.2 1B Instruct adds Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-09-25 | 2024-12-13 |
| Context window | 128K | — |
| Parameters | 1.23B | 3.8B |
| Architecture | decoder only | - |
| License | Open Source | Microsoft Research |
| Knowledge cutoff | 2023-12 | - |
Pricing and availability
| Pricing attribute | Llama 3.2 1B Instruct | Phi-4 Mini |
|---|---|---|
| Input price | $0.03/1M tokens | $0.05/1M tokens |
| Output price | $0.2/1M tokens | $0.15/1M tokens |
| Providers |
Capabilities
| Capability | Llama 3.2 1B Instruct | Phi-4 Mini |
|---|---|---|
| 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 |
Benchmarks
| Benchmark | Llama 3.2 1B Instruct | Phi-4 Mini |
|---|---|---|
| MMLU PRO | 20.0 | 52.8 |
| Google-Proof Q&A | 25.6 | 25.2 |
| Massive Multitask Language Understanding | 49.3 | 67.3 |
Deep dive
On shared benchmark coverage, MMLU PRO has Llama 3.2 1B Instruct at 20 and Phi-4 Mini at 52.8, with Phi-4 Mini ahead by 32.8 points; Google-Proof Q&A has Llama 3.2 1B Instruct at 25.6 and Phi-4 Mini at 25.2, with Llama 3.2 1B Instruct ahead by 0.4 points; Massive Multitask Language Understanding has Llama 3.2 1B Instruct at 49.3 and Phi-4 Mini at 67.3, with Phi-4 Mini ahead by 18 points. The largest visible gap is 32.8 points on MMLU PRO, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
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-4 Mini lists $0.05/1M input and $0.15/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3.2 1B Instruct lower by about $0 per million blended tokens. Availability is 5 providers versus 3, 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-4 Mini when provider fit are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Which is cheaper, Llama 3.2 1B Instruct or Phi-4 Mini?
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-4 Mini costs $0.05/1M input and $0.15/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Llama 3.2 1B Instruct or Phi-4 Mini open source?
Llama 3.2 1B Instruct is listed under Open Source. Phi-4 Mini is listed under Microsoft Research. 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-4 Mini?
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-4 Mini?
Llama 3.2 1B Instruct is available on OpenRouter, Fireworks AI, NVIDIA NIM, Bitdeer AI, and AWS Bedrock. Phi-4 Mini is available on Fireworks AI, NVIDIA NIM, and Novita AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3.2 1B Instruct over Phi-4 Mini?
Llama 3.2 1B Instruct is ~85% cheaper at $0.03/1M; pay for Phi-4 Mini 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-4 Mini.
Continue comparing
Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.