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 16k-token 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 $0.90/1M 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.
Phi-4 14B is ~1285% cheaper at $0.07/1M; pay for Qwen2-VL-72B-Instruct only for long-context analysis.
Decision scorecard
Local evidence first| Signal | Phi-4 14B | Qwen2-VL-72B-Instruct |
|---|---|---|
| Best for | provider-routed production | multimodal apps |
| Decision fit | Classification and JSON / Tool use | Vision |
| Context window | 16k | 32k |
| Cheapest output | $0.14/1M tokens | $0.90/1M tokens |
| Provider routes | 3 tracked | 1 tracked |
| Shared benchmarks | 0 shared | 0 shared |
Decision tradeoffs
- 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.
- Phi-4 14B uniquely exposes Structured outputs in local model data.
- Local decision data tags Phi-4 14B for Classification and JSON / Tool use.
- Qwen2-VL-72B-Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Qwen2-VL-72B-Instruct uniquely exposes Vision and Multimodal in local model data.
- Local decision data tags Qwen2-VL-72B-Instruct for Vision.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Phi-4 14B
$87.00
Cheapest tracked route/tier: OpenRouter
Qwen2-VL-72B-Instruct
$945
Cheapest tracked route/tier: Fireworks AI
Estimated monthly gap: $858. 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.
- Qwen2-VL-72B-Instruct is $0.76/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Structured outputs before moving production traffic.
- Qwen2-VL-72B-Instruct adds Vision and Multimodal in local capability data.
- Provider overlap exists on Fireworks AI; start route-level A/B tests there.
- Phi-4 14B is $0.76/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Vision and Multimodal before moving production traffic.
- Phi-4 14B adds Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-12-13 | 2025-01-01 |
| Context window | 16k | 32k |
| Parameters | 14B | 72B |
| Architecture | Decoder Only | Decoder Only |
| License | MITOSI-approved | Apache 2.0OSI-approved |
| Openness | Open source | Open source |
| Commercial use | Commercial use: permitted | Commercial use: permitted |
| Knowledge cutoff | 2024-06 | 2023-06 |
Pricing and availability
| Pricing attribute | Phi-4 14B | Qwen2-VL-72B-Instruct |
|---|---|---|
| Input price | $0.07/1M tokens | $0.90/1M tokens |
| Output price | $0.14/1M tokens | $0.90/1M 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 |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
No shared benchmark scores are currently available 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 on the cheapest tracked provider, while Qwen2-VL-72B-Instruct lists $0.90/1M input and $0.90/1M 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 3 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 long-context analysis and larger context windows 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, Phi-4 14B or Qwen2-VL-72B-Instruct?
Qwen2-VL-72B-Instruct supports 32k tokens, while Phi-4 14B supports 16k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
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 $0.90/1M input and $0.90/1M 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 MIT. 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.
Where can I run Phi-4 14B and Qwen2-VL-72B-Instruct?
Phi-4 14B is available on OpenRouter, Fireworks AI, and Microsoft Foundry. 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: 2026-06-15. Data sourced from public model cards and provider documentation.