Phi-4 14B vs Qwen2-VL-72B-Instruct
Phi-4 14B (2024) and Qwen2-VL-72B-Instruct (2025) are compact production models from Microsoft Research and Alibaba. Phi-4 14B ships a not-yet-sourced context window, while Qwen2-VL-72B-Instruct ships a 32K-token context window. On pricing, Phi-4 14B costs $0.07/1M input tokens versus $0.9/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
Phi-4 14B is ~1285% cheaper at $0.07/1M; pay for Qwen2-VL-72B-Instruct only for vision-heavy evaluation.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-12-13 | 2025-01-01 |
| Context window | — | 32K |
| Parameters | 14B | 72B |
| Architecture | decoder only | decoder only |
| License | Open Source | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Phi-4 14B | Qwen2-VL-72B-Instruct |
|---|---|---|
| Input price | $0.07/1M tokens | $0.9/1M tokens |
| Output price | $0.14/1M tokens | $0.9/1M tokens |
| Providers |
Capabilities
| Capability | Phi-4 14B | Qwen2-VL-72B-Instruct |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | No |
| Code execution | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on vision: Qwen2-VL-72B-Instruct, multimodal input: Qwen2-VL-72B-Instruct, and structured outputs: Phi-4 14B. 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 14B lists $0.07/1M input and $0.14/1M output tokens, while Qwen2-VL-72B-Instruct lists $0.9/1M input and $0.9/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Phi-4 14B lower by about $0.81 per million blended tokens. Availability is 2 providers versus 1, 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 Qwen2-VL-72B-Instruct when vision-heavy evaluation 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. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.
FAQ
Which is cheaper, Phi-4 14B or Qwen2-VL-72B-Instruct?
Phi-4 14B is cheaper on tracked token pricing. Phi-4 14B costs $0.07/1M input and $0.14/1M output tokens. Qwen2-VL-72B-Instruct costs $0.9/1M input and $0.9/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Phi-4 14B or Qwen2-VL-72B-Instruct open source?
Phi-4 14B is listed under Open Source. Qwen2-VL-72B-Instruct 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 vision, Phi-4 14B or Qwen2-VL-72B-Instruct?
Qwen2-VL-72B-Instruct has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for multimodal input, Phi-4 14B or Qwen2-VL-72B-Instruct?
Qwen2-VL-72B-Instruct has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for structured outputs, Phi-4 14B or Qwen2-VL-72B-Instruct?
Phi-4 14B 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 14B and Qwen2-VL-72B-Instruct?
Phi-4 14B is available on OpenRouter and Fireworks AI. Qwen2-VL-72B-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.
Continue comparing
Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.