Phi-4 14B vs Qwen1.5-110B
Phi-4 14B (2024) and Qwen1.5-110B (2024) are general-purpose language models from Microsoft Research and Alibaba. Phi-4 14B ships a not-yet-sourced context window, while Qwen1.5-110B ships a not-yet-sourced context window. On Massive Multitask Language Understanding, Phi-4 14B leads by 6.6 pts. On pricing, Phi-4 14B costs $0.07/1M input tokens versus $1.5/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Phi-4 14B is ~2208% cheaper at $0.07/1M; pay for Qwen1.5-110B only for provider fit.
Decision scorecard
Local evidence first| Signal | Phi-4 14B | Qwen1.5-110B |
|---|---|---|
| Decision fit | Classification and JSON / Tool use | Coding, Classification, and JSON / Tool use |
| Context window | — | — |
| Cheapest output | $0.14/1M tokens | $2.5/1M tokens |
| Provider routes | 3 tracked | 2 tracked |
| Shared benchmarks | Massive Multitask Language Understanding leader | 1 rows |
Decision tradeoffs
- Phi-4 14B leads the largest shared benchmark signal on Massive Multitask Language Understanding by 6.6 points.
- Phi-4 14B has the lower cheapest tracked output price at $0.14/1M tokens.
- Phi-4 14B has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Phi-4 14B for Classification and JSON / Tool use.
- 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 14B
$87.00
Cheapest tracked route: OpenRouter
Qwen1.5-110B
$1,825
Cheapest tracked route: Microsoft Foundry
Estimated monthly gap: $1,738. Batch, cache, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Microsoft Foundry; start route-level A/B tests there.
- Qwen1.5-110B is $2.36/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Provider overlap exists on Microsoft Foundry; start route-level A/B tests there.
- Phi-4 14B is $2.36/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-12-13 | 2024-04-25 |
| Context window | — | — |
| Parameters | 14B | 110B |
| Architecture | decoder only | decoder only |
| License | Open Source | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Phi-4 14B | Qwen1.5-110B |
|---|---|---|
| Input price | $0.07/1M tokens | $1.5/1M tokens |
| Output price | $0.14/1M tokens | $2.5/1M tokens |
| Providers |
Capabilities
| Capability | Phi-4 14B | Qwen1.5-110B |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | Yes |
| Code execution | No | No |
Benchmarks
| Benchmark | Phi-4 14B | Qwen1.5-110B |
|---|---|---|
| Massive Multitask Language Understanding | 84.8 | 78.2 |
Deep dive
On shared benchmark coverage, Massive Multitask Language Understanding has Phi-4 14B at 84.8 and Qwen1.5-110B at 78.2, with Phi-4 14B ahead by 6.6 points. The largest visible gap is 6.6 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 is close: both models cover structured outputs. 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 14B lists $0.07/1M input and $0.14/1M 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 14B lower by about $1.71 per million blended tokens. Availability is 3 providers versus 2, so concentration risk also matters.
Choose Phi-4 14B 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 14B or Qwen1.5-110B?
Phi-4 14B is cheaper on tracked token pricing. Phi-4 14B costs $0.07/1M input and $0.14/1M 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 14B or Qwen1.5-110B open source?
Phi-4 14B is listed under Open Source. 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 14B or Qwen1.5-110B?
Both Phi-4 14B and Qwen1.5-110B expose structured outputs. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Where can I run Phi-4 14B and Qwen1.5-110B?
Phi-4 14B is available on OpenRouter, Fireworks AI, and Microsoft Foundry. 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 14B over Qwen1.5-110B?
Phi-4 14B is ~2208% cheaper at $0.07/1M; pay for Qwen1.5-110B only for provider fit. If your workload also depends on provider fit, start with Phi-4 14B; 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.