Llama 3.2 11B Instruct vs Phi-4 Reasoning Vision 15B
Llama 3.2 11B Instruct (2025) and Phi-4 Reasoning Vision 15B (2026) are general-purpose language models from AI at Meta and Microsoft Research. Llama 3.2 11B Instruct ships a not-yet-sourced context window, while Phi-4 Reasoning Vision 15B ships a not-yet-sourced context window. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
Phi-4 Reasoning Vision 15B is safer overall; choose Llama 3.2 11B Instruct when provider fit matters.
Decision scorecard
Local evidence first| Signal | Llama 3.2 11B Instruct | Phi-4 Reasoning Vision 15B |
|---|---|---|
| Decision fit | Classification and JSON / Tool use | Vision |
| Context window | — | — |
| Cheapest output | $0.27/1M tokens | - |
| Provider routes | 1 tracked | 0 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Llama 3.2 11B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Llama 3.2 11B Instruct uniquely exposes Structured outputs in local model data.
- Local decision data tags Llama 3.2 11B Instruct for Classification and JSON / Tool use.
- Phi-4 Reasoning Vision 15B uniquely exposes Multimodal in local model data.
- Local decision data tags Phi-4 Reasoning Vision 15B for Vision.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Llama 3.2 11B Instruct
$228
Cheapest tracked route: AWS Bedrock
Phi-4 Reasoning Vision 15B
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 11B Instruct and Phi-4 Reasoning Vision 15B; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Structured outputs before moving production traffic.
- Phi-4 Reasoning Vision 15B adds Multimodal in local capability data.
- No overlapping tracked provider route is sourced for Phi-4 Reasoning Vision 15B and Llama 3.2 11B Instruct; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Multimodal before moving production traffic.
- Llama 3.2 11B Instruct adds Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-09-01 | 2026-03-12 |
| Context window | — | — |
| Parameters | — | 15B |
| Architecture | - | - |
| License | Proprietary | Microsoft Research |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Llama 3.2 11B Instruct | Phi-4 Reasoning Vision 15B |
|---|---|---|
| Input price | $0.2/1M tokens | - |
| Output price | $0.27/1M tokens | - |
| Providers | - |
Capabilities
| Capability | Llama 3.2 11B Instruct | Phi-4 Reasoning Vision 15B |
|---|---|---|
| Vision | No | No |
| Multimodal | No | Yes |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | No |
| Code execution | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on multimodal input: Phi-4 Reasoning Vision 15B and structured outputs: Llama 3.2 11B 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.
Pricing coverage is uneven: Llama 3.2 11B Instruct has $0.2/1M input tokens and Phi-4 Reasoning Vision 15B has no token price sourced yet. Provider availability is 1 tracked routes versus 0. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Llama 3.2 11B Instruct when provider fit and broader provider choice are central to the workload. Choose Phi-4 Reasoning Vision 15B 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
Is Llama 3.2 11B Instruct or Phi-4 Reasoning Vision 15B open source?
Llama 3.2 11B Instruct is listed under Proprietary. Phi-4 Reasoning Vision 15B 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 multimodal input, Llama 3.2 11B Instruct or Phi-4 Reasoning Vision 15B?
Phi-4 Reasoning Vision 15B has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for structured outputs, Llama 3.2 11B Instruct or Phi-4 Reasoning Vision 15B?
Llama 3.2 11B 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 11B Instruct and Phi-4 Reasoning Vision 15B?
Llama 3.2 11B Instruct is available on AWS Bedrock. Phi-4 Reasoning Vision 15B is available on the tracked providers still being sourced. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3.2 11B Instruct over Phi-4 Reasoning Vision 15B?
Phi-4 Reasoning Vision 15B is safer overall; choose Llama 3.2 11B Instruct when provider fit matters. If your workload also depends on provider fit, start with Llama 3.2 11B Instruct; if it depends on provider fit, run the same evaluation with Phi-4 Reasoning Vision 15B.
Continue comparing
Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.