Llama 3.2 1B Instruct vs Phi 3.5 MoE Instruct
Llama 3.2 1B Instruct (2024) and Phi 3.5 MoE Instruct (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 3.5 MoE Instruct ships a 128k-token context window. On pricing, Llama 3.2 1B Instruct costs $0.03/1M 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 1B Instruct is ~1752% cheaper at $0.03/1M; pay for Phi 3.5 MoE Instruct only for provider fit.
Decision scorecard
Local evidence first| Signal | Llama 3.2 1B Instruct | Phi 3.5 MoE Instruct |
|---|---|---|
| Best for | provider-routed production | general production evaluation |
| Decision fit | Coding, RAG, and Long context | Long context |
| Context window | 128k | 128k |
| Cheapest output | $0.20/1M tokens | $0.50/1M tokens |
| Provider routes | 7 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Llama 3.2 1B Instruct has the lower cheapest tracked output price at $0.20/1M tokens.
- 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.
- 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 1B Instruct
$71.85
Cheapest tracked route/tier: Cloudflare Workers AI
Phi 3.5 MoE Instruct
$525
Cheapest tracked route/tier: Fireworks AI
Estimated monthly gap: $453. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Fireworks AI; start route-level A/B tests there.
- Phi 3.5 MoE Instruct is $0.30/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Structured outputs before moving production traffic.
- Provider overlap exists on Fireworks AI; start route-level A/B tests there.
- Llama 3.2 1B Instruct is $0.30/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Llama 3.2 1B Instruct adds Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-09-25 | 2024-08-20 |
| Context window | 128k | 128k |
| Parameters | 1.23B | 16x3.8B (42B, 6.6B active) |
| 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 | 2023-10 |
Pricing and availability
| Pricing attribute | Llama 3.2 1B Instruct | Phi 3.5 MoE Instruct |
|---|---|---|
| Input price | $0.03/1M tokens | $0.50/1M tokens |
| Output price | $0.20/1M tokens | $0.50/1M tokens |
| Providers |
Capabilities
| Capability | Llama 3.2 1B Instruct | Phi 3.5 MoE Instruct |
|---|---|---|
| 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 |
| 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 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 $0.20/1M 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 1B Instruct lower by about $0.42 per million blended tokens. Availability is 7 providers versus 1, 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 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. 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 Instruct or Phi 3.5 MoE Instruct?
Llama 3.2 1B 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 1B Instruct or Phi 3.5 MoE Instruct?
Llama 3.2 1B Instruct is cheaper on tracked token pricing. Llama 3.2 1B Instruct costs $0.03/1M input and $0.20/1M 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 1B Instruct or Phi 3.5 MoE Instruct open source?
Llama 3.2 1B 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 structured outputs, Llama 3.2 1B Instruct or Phi 3.5 MoE Instruct?
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 3.5 MoE Instruct?
Llama 3.2 1B Instruct is available on Cloudflare Workers AI, OpenRouter, Fireworks AI, NVIDIA NIM, and Bitdeer AI. Phi 3.5 MoE Instruct is available on Fireworks AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3.2 1B Instruct over Phi 3.5 MoE Instruct?
Llama 3.2 1B Instruct is ~1752% cheaper at $0.03/1M; pay for Phi 3.5 MoE Instruct 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 3.5 MoE Instruct.
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Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.