Llama 3.2 1B vs Phi 3.5 MoE Instruct
Llama 3.2 1B (2024) and Phi 3.5 MoE Instruct (2024) are compact production models from AI at Meta and Microsoft Research. Llama 3.2 1B ships a 128k-token context window, while Phi 3.5 MoE Instruct ships a 128k-token context window. On pricing, Llama 3.2 1B costs $0.10/1M 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 is ~400% cheaper at $0.10/1M; pay for Phi 3.5 MoE Instruct only for provider fit.
Decision scorecard
Local evidence first| Signal | Llama 3.2 1B | Phi 3.5 MoE Instruct |
|---|---|---|
| Best for | general production evaluation | general production evaluation |
| Decision fit | Coding, Long context, and Classification | Long context |
| Context window | 128k | 128k |
| Cheapest output | $0.10/1M tokens | $0.50/1M tokens |
| Provider routes | 1 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Llama 3.2 1B has the lower cheapest tracked output price at $0.10/1M tokens.
- Local decision data tags Llama 3.2 1B for Coding, Long context, and Classification.
- 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
$105
Cheapest tracked route/tier: Fireworks AI
Phi 3.5 MoE Instruct
$525
Cheapest tracked route/tier: Fireworks AI
Estimated monthly gap: $420. 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 $0.40/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Provider overlap exists on Fireworks AI; start route-level A/B tests there.
- Llama 3.2 1B is $0.40/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
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 | Phi 3.5 MoE Instruct |
|---|---|---|
| Input price | $0.10/1M tokens | $0.50/1M tokens |
| Output price | $0.10/1M tokens | $0.50/1M tokens |
| Providers |
Capabilities
| Capability | Llama 3.2 1B | Phi 3.5 MoE Instruct |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| 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 is close: both models cover the core production surface. That makes context budget, benchmark fit, and provider maturity more important than a simple checklist. If your application depends on one integration detail, verify it against the provider route you plan to use, not just the base model listing.
For cost, Llama 3.2 1B lists $0.10/1M input and $0.10/1M 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 lower by about $0.40 per million blended tokens. Availability is 1 providers versus 1, so concentration risk also matters.
Choose Llama 3.2 1B when provider fit 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. 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 or Phi 3.5 MoE Instruct?
Llama 3.2 1B 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 or Phi 3.5 MoE Instruct?
Llama 3.2 1B is cheaper on tracked token pricing. Llama 3.2 1B costs $0.10/1M input and $0.10/1M 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 or Phi 3.5 MoE Instruct open source?
Llama 3.2 1B 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.
Where can I run Llama 3.2 1B and Phi 3.5 MoE Instruct?
Llama 3.2 1B is available on Fireworks AI. Phi 3.5 MoE Instruct is available on Fireworks AI. 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 3.5 MoE Instruct?
Llama 3.2 1B is ~400% cheaper at $0.10/1M; 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; if it depends on provider fit, run the same evaluation with Phi 3.5 MoE Instruct.
Continue comparing
Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.