Llama 3.2 1B vs Phi-4 Mini Flash Reasoning
Llama 3.2 1B (2024) and Phi-4 Mini Flash Reasoning (2025) are frontier reasoning models from AI at Meta and Microsoft Research. Llama 3.2 1B ships a 128k-token context window, while Phi-4 Mini Flash Reasoning ships a 128k-token context window. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Phi-4 Mini Flash Reasoning is safer overall; choose Llama 3.2 1B when provider fit matters.
Decision scorecard
Local evidence first| Signal | Llama 3.2 1B | Phi-4 Mini Flash Reasoning |
|---|---|---|
| Best for | general production evaluation | reasoning-heavy apps |
| Decision fit | Coding, Long context, and Classification | Long context |
| Context window | 128k | 128k |
| Cheapest output | $0.10/1M tokens | - |
| Provider routes | 1 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Local decision data tags Llama 3.2 1B for Coding, Long context, and Classification.
- Phi-4 Mini Flash Reasoning uniquely exposes Reasoning in local model data.
- Local decision data tags Phi-4 Mini Flash Reasoning for Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Llama 3.2 1B
$105
Cheapest tracked route/tier: Fireworks AI
Phi-4 Mini Flash Reasoning
Unavailable
No complete token price in local provider data
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Llama 3.2 1B and Phi-4 Mini Flash Reasoning; plan for SDK, billing, or endpoint changes.
- Phi-4 Mini Flash Reasoning adds Reasoning in local capability data.
- No overlapping tracked provider route is sourced for Phi-4 Mini Flash Reasoning and Llama 3.2 1B; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Reasoning before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-09-25 | 2025-12-01 |
| Context window | 128k | 128k |
| Parameters | 1.23B | 3.8B |
| 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 | 2025-02 |
Pricing and availability
| Pricing attribute | Llama 3.2 1B | Phi-4 Mini Flash Reasoning |
|---|---|---|
| Input price | $0.10/1M tokens | - |
| Output price | $0.10/1M tokens | - |
| Providers |
Capabilities
| Capability | Llama 3.2 1B | Phi-4 Mini Flash Reasoning |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | Yes |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | 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 reasoning mode: Phi-4 Mini Flash Reasoning. 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.
Pricing coverage is uneven: Llama 3.2 1B has $0.10/1M input tokens and Phi-4 Mini Flash Reasoning has no token price sourced yet. Provider availability is 1 tracked routes versus 1. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Llama 3.2 1B when provider fit are central to the workload. Choose Phi-4 Mini Flash Reasoning when reasoning depth 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. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.
FAQ
Which has a larger context window, Llama 3.2 1B or Phi-4 Mini Flash Reasoning?
Llama 3.2 1B supports 128k tokens, while Phi-4 Mini Flash Reasoning supports 128k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Llama 3.2 1B or Phi-4 Mini Flash Reasoning open source?
Llama 3.2 1B is listed under Llama 3 Community. Phi-4 Mini Flash Reasoning 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 reasoning mode, Llama 3.2 1B or Phi-4 Mini Flash Reasoning?
Phi-4 Mini Flash Reasoning has the clearer documented reasoning mode signal in this comparison. If reasoning mode 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 and Phi-4 Mini Flash Reasoning?
Llama 3.2 1B is available on Fireworks AI. Phi-4 Mini Flash Reasoning is available on NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
When should I pick Llama 3.2 1B over Phi-4 Mini Flash Reasoning?
Phi-4 Mini Flash Reasoning is safer overall; choose Llama 3.2 1B when provider fit matters. If your workload also depends on provider fit, start with Llama 3.2 1B; if it depends on reasoning depth, run the same evaluation with Phi-4 Mini Flash Reasoning.
Continue comparing
Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.