Phi-4 Mini vs Qwen1.5-110B
Phi-4 Mini (2024) and Qwen1.5-110B (2024) are general-purpose language models from Microsoft Research and Alibaba. Phi-4 Mini ships a not-yet-sourced context window, while Qwen1.5-110B ships a not-yet-sourced context window. On Massive Multitask Language Understanding, Qwen1.5-110B leads by 10.9 pts. On pricing, Phi-4 Mini costs $0.05/1M input tokens versus $1.5/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Phi-4 Mini is ~2900% cheaper at $0.05/1M; pay for Qwen1.5-110B only for provider fit.
Decision scorecard
Local evidence first| Signal | Phi-4 Mini | Qwen1.5-110B |
|---|---|---|
| Decision fit | Classification | Coding, Classification, and JSON / Tool use |
| Context window | — | — |
| Cheapest output | $0.15/1M tokens | $2.5/1M tokens |
| Provider routes | 3 tracked | 2 tracked |
| Shared benchmarks | 1 rows | Massive Multitask Language Understanding leader |
Decision tradeoffs
- Phi-4 Mini has the lower cheapest tracked output price at $0.15/1M tokens.
- Phi-4 Mini has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Phi-4 Mini for Classification.
- Qwen1.5-110B leads the largest shared benchmark signal on Massive Multitask Language Understanding by 10.9 points.
- Qwen1.5-110B uniquely exposes Structured outputs in local model data.
- Local decision data tags Qwen1.5-110B for Coding, Classification, and JSON / Tool use.
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
Qwen1.5-110B
$1,825
Cheapest tracked route: Microsoft Foundry
Estimated monthly gap: $1,748. 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 Qwen1.5-110B; plan for SDK, billing, or endpoint changes.
- Qwen1.5-110B is $2.35/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Qwen1.5-110B adds Structured outputs in local capability data.
- No overlapping tracked provider route is sourced for Qwen1.5-110B and Phi-4 Mini; plan for SDK, billing, or endpoint changes.
- Phi-4 Mini is $2.35/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Structured outputs before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-12-13 | 2024-04-25 |
| Context window | — | — |
| Parameters | 3.8B | 110B |
| Architecture | - | decoder only |
| License | Microsoft Research | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Phi-4 Mini | Qwen1.5-110B |
|---|---|---|
| Input price | $0.05/1M tokens | $1.5/1M tokens |
| Output price | $0.15/1M tokens | $2.5/1M tokens |
| Providers |
Capabilities
| Capability | Phi-4 Mini | Qwen1.5-110B |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | Yes |
| Code execution | No | No |
Benchmarks
| Benchmark | Phi-4 Mini | Qwen1.5-110B |
|---|---|---|
| Massive Multitask Language Understanding | 67.3 | 78.2 |
Deep dive
On shared benchmark coverage, Massive Multitask Language Understanding has Phi-4 Mini at 67.3 and Qwen1.5-110B at 78.2, with Qwen1.5-110B ahead by 10.9 points. The largest visible gap is 10.9 points on Massive Multitask Language Understanding, 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 differs most on structured outputs: Qwen1.5-110B. 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, Phi-4 Mini lists $0.05/1M input and $0.15/1M output tokens, while Qwen1.5-110B lists $1.5/1M input and $2.5/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Phi-4 Mini lower by about $1.72 per million blended tokens. Availability is 3 providers versus 2, 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 Qwen1.5-110B 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 Qwen1.5-110B?
Phi-4 Mini is cheaper on tracked token pricing. Phi-4 Mini costs $0.05/1M input and $0.15/1M output tokens. Qwen1.5-110B costs $1.5/1M input and $2.5/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Phi-4 Mini or Qwen1.5-110B open source?
Phi-4 Mini is listed under Microsoft Research. Qwen1.5-110B 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.
Which is better for structured outputs, Phi-4 Mini or Qwen1.5-110B?
Qwen1.5-110B 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 Phi-4 Mini and Qwen1.5-110B?
Phi-4 Mini is available on Fireworks AI, NVIDIA NIM, and Novita AI. Qwen1.5-110B is available on Microsoft Foundry and Together AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Phi-4 Mini over Qwen1.5-110B?
Phi-4 Mini is ~2900% cheaper at $0.05/1M; pay for Qwen1.5-110B 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 Qwen1.5-110B.
Continue comparing
Last reviewed: 2026-05-16. Data sourced from public model cards and provider documentation.