Phi-4 Mini vs Qwen2-1.5B
Phi-4 Mini (2024) and Qwen2-1.5B (2024) are general-purpose language models from Microsoft Research and Alibaba. Phi-4 Mini ships a not-yet-sourced context window, while Qwen2-1.5B ships a not-yet-sourced context window. On Google-Proof Q&A, Qwen2-1.5B leads by 19.0 pts. On pricing, Phi-4 Mini costs $0.05/1M input tokens versus $0.07/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Phi-4 Mini is ~40% cheaper at $0.05/1M; pay for Qwen2-1.5B only for provider fit.
Decision scorecard
Local evidence first| Signal | Phi-4 Mini | Qwen2-1.5B |
|---|---|---|
| Decision fit | Classification | Coding and Classification |
| Context window | — | — |
| Cheapest output | $0.15/1M tokens | $0.07/1M tokens |
| Provider routes | 3 tracked | 1 tracked |
| Shared benchmarks | 2 rows | Google-Proof Q&A leader |
Decision tradeoffs
- Phi-4 Mini has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Phi-4 Mini for Classification.
- Qwen2-1.5B leads the largest shared benchmark signal on Google-Proof Q&A by 19.0 points.
- Qwen2-1.5B has the lower cheapest tracked output price at $0.07/1M tokens.
- Local decision data tags Qwen2-1.5B for Coding and Classification.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Phi-4 Mini
$77.50
Cheapest tracked route: Novita AI
Qwen2-1.5B
$73.50
Cheapest tracked route: Microsoft Foundry
Estimated monthly gap: $4.00. Batch, cache, and negotiated pricing are excluded from this local estimate.
Switch friction
- No overlapping tracked provider route is sourced for Phi-4 Mini and Qwen2-1.5B; plan for SDK, billing, or endpoint changes.
- Qwen2-1.5B is $0.08/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- No overlapping tracked provider route is sourced for Qwen2-1.5B and Phi-4 Mini; plan for SDK, billing, or endpoint changes.
- Phi-4 Mini is $0.08/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-12-13 | 2024-06-05 |
| Context window | — | — |
| Parameters | 3.8B | 1.54B |
| Architecture | - | decoder only |
| License | Microsoft Research | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Phi-4 Mini | Qwen2-1.5B |
|---|---|---|
| Input price | $0.05/1M tokens | $0.07/1M tokens |
| Output price | $0.15/1M tokens | $0.07/1M tokens |
| Providers |
Capabilities
| Capability | Phi-4 Mini | Qwen2-1.5B |
|---|---|---|
| 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 |
Benchmarks
| Benchmark | Phi-4 Mini | Qwen2-1.5B |
|---|---|---|
| Google-Proof Q&A | 25.2 | 44.2 |
| Massive Multitask Language Understanding | 67.3 | 69.7 |
Deep dive
On shared benchmark coverage, Google-Proof Q&A has Phi-4 Mini at 25.2 and Qwen2-1.5B at 44.2, with Qwen2-1.5B ahead by 19.0 points; Massive Multitask Language Understanding has Phi-4 Mini at 67.3 and Qwen2-1.5B at 69.7, with Qwen2-1.5B ahead by 2.4 points. The largest visible gap is 19.0 points on Google-Proof Q&A, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
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, Phi-4 Mini lists $0.05/1M input and $0.15/1M output tokens, while Qwen2-1.5B lists $0.07/1M input and $0.07/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Qwen2-1.5B lower by about $0.01 per million blended tokens. Availability is 3 providers versus 1, so concentration risk also matters.
Choose Phi-4 Mini when provider fit, lower input-token cost, and broader provider choice are central to the workload. Choose Qwen2-1.5B when provider fit are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Which is cheaper, Phi-4 Mini or Qwen2-1.5B?
Phi-4 Mini is cheaper on tracked token pricing. Phi-4 Mini costs $0.05/1M input and $0.15/1M output tokens. Qwen2-1.5B costs $0.07/1M input and $0.07/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Phi-4 Mini or Qwen2-1.5B open source?
Phi-4 Mini is listed under Microsoft Research. Qwen2-1.5B is listed under Apache 2.0. 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 Phi-4 Mini and Qwen2-1.5B?
Phi-4 Mini is available on Fireworks AI, NVIDIA NIM, and Novita AI. Qwen2-1.5B is available on Microsoft Foundry. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Phi-4 Mini over Qwen2-1.5B?
Phi-4 Mini is ~40% cheaper at $0.05/1M; pay for Qwen2-1.5B only for provider fit. If your workload also depends on provider fit, start with Phi-4 Mini; if it depends on provider fit, run the same evaluation with Qwen2-1.5B.
Continue comparing
Last reviewed: 2026-05-16. Data sourced from public model cards and provider documentation.