Llama 3.2 11B Instruct vs Phi 3.5 MoE Instruct
Llama 3.2 11B Instruct (2025) and Phi 3.5 MoE Instruct (2024) are compact production models from AI at Meta and Microsoft Research. Llama 3.2 11B Instruct ships a 128k-token context window, while Phi 3.5 MoE Instruct ships a 128k-token context window. On pricing, Llama 3.2 11B Instruct costs $0.20/1M input tokens versus $0.50/1M for the alternative. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Llama 3.2 11B Instruct is ~150% cheaper at $0.20/1M; pay for Phi 3.5 MoE Instruct only for provider fit.
Decision scorecard
Local evidence first| Signal | Llama 3.2 11B Instruct | Phi 3.5 MoE Instruct |
|---|---|---|
| Best for | multimodal apps | general production evaluation |
| Decision fit | RAG, Long context, and Vision | Long context |
| Context window | 128k | 128k |
| Cheapest output | $0.27/1M tokens | $0.50/1M tokens |
| Provider routes | 1 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Llama 3.2 11B Instruct has the lower cheapest tracked output price at $0.27/1M tokens.
- Llama 3.2 11B Instruct uniquely exposes Vision, Multimodal, and Structured outputs in local model data.
- Local decision data tags Llama 3.2 11B Instruct for RAG, Long context, and Vision.
- Local decision data tags Phi 3.5 MoE Instruct 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 11B Instruct
$228
Cheapest tracked route/tier: AWS Bedrock
Phi 3.5 MoE Instruct
$525
Cheapest tracked route/tier: Fireworks AI
Estimated monthly gap: $298. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- No overlapping tracked provider route is sourced for Llama 3.2 11B Instruct and Phi 3.5 MoE Instruct; plan for SDK, billing, or endpoint changes.
- Phi 3.5 MoE Instruct is $0.23/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Vision, Multimodal, and Structured outputs before moving production traffic.
- No overlapping tracked provider route is sourced for Phi 3.5 MoE Instruct and Llama 3.2 11B Instruct; plan for SDK, billing, or endpoint changes.
- Llama 3.2 11B Instruct is $0.23/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Llama 3.2 11B Instruct adds Vision, Multimodal, and Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-09-01 | 2024-08-20 |
| Context window | 128k | 128k |
| Parameters | 11B | 16x3.8B (42B, 6.6B active) |
| Architecture | - | 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 | 2023-10 |
Pricing and availability
| Pricing attribute | Llama 3.2 11B Instruct | Phi 3.5 MoE Instruct |
|---|---|---|
| Input price | $0.20/1M tokens | $0.50/1M tokens |
| Output price | $0.27/1M tokens | $0.50/1M tokens |
| Providers |
Capabilities
| Capability | Llama 3.2 11B Instruct | Phi 3.5 MoE Instruct |
|---|---|---|
| Vision | Yes | No |
| Multimodal | Yes | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | 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 vision: Llama 3.2 11B Instruct, multimodal input: Llama 3.2 11B Instruct, 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.
For cost, Llama 3.2 11B Instruct lists $0.20/1M input and $0.27/1M output tokens on the cheapest tracked provider, while Phi 3.5 MoE Instruct lists $0.50/1M input and $0.50/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3.2 11B Instruct lower by about $0.28 per million blended tokens. Availability is 1 providers versus 1, so concentration risk also matters.
Choose Llama 3.2 11B Instruct when vision-heavy evaluation and lower input-token cost 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.
FAQ
Which has a larger context window, Llama 3.2 11B Instruct or Phi 3.5 MoE Instruct?
Llama 3.2 11B 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 11B Instruct or Phi 3.5 MoE Instruct?
Llama 3.2 11B Instruct is cheaper on tracked token pricing. Llama 3.2 11B Instruct costs $0.20/1M input and $0.27/1M output tokens. Phi 3.5 MoE Instruct costs $0.50/1M input and $0.50/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Llama 3.2 11B Instruct or Phi 3.5 MoE Instruct open source?
Llama 3.2 11B Instruct is listed under Llama 3 Community. 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 vision, Llama 3.2 11B Instruct or Phi 3.5 MoE Instruct?
Llama 3.2 11B Instruct has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for multimodal input, Llama 3.2 11B Instruct or Phi 3.5 MoE Instruct?
Llama 3.2 11B Instruct 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.
Where can I run Llama 3.2 11B Instruct and Phi 3.5 MoE Instruct?
Llama 3.2 11B Instruct is available on AWS Bedrock. Phi 3.5 MoE Instruct is available on Fireworks AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-06-04. Data sourced from public model cards and provider documentation.