Phi-4 Mini vs Qwen2-7B
Phi-4 Mini (2024) and Qwen2-7B (2024) are compact production models from Microsoft Research and Alibaba. Phi-4 Mini ships a not-yet-sourced context window, while Qwen2-7B ships a 128K-token context window. On Google-Proof Q&A, Qwen2-7B leads by 30.2 pts. On pricing, Phi-4 Mini costs $0.05/1M input tokens versus $0.05/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Pick Qwen2-7B for reasoning; Phi-4 Mini is better when provider fit matters more.
Decision scorecard
Local evidence first| Signal | Phi-4 Mini | Qwen2-7B |
|---|---|---|
| Decision fit | Classification | Coding, RAG, and Long context |
| Context window | — | 128K |
| Cheapest output | $0.15/1M tokens | $0.15/1M tokens |
| Provider routes | 3 tracked | 5 tracked |
| Shared benchmarks | 2 rows | Google-Proof Q&A leader |
Decision tradeoffs
- Local decision data tags Phi-4 Mini for Classification.
- Qwen2-7B leads the largest shared benchmark signal on Google-Proof Q&A by 30.2 points.
- Qwen2-7B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Qwen2-7B has broader tracked provider coverage for fallback and procurement flexibility.
- Qwen2-7B uniquely exposes Structured outputs in local model data.
- Local decision data tags Qwen2-7B for Coding, RAG, and Long context.
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
Qwen2-7B
$77.50
Cheapest tracked route: DeepInfra
Estimated monthly gap: $0.00. Batch, cache, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Fireworks AI and NVIDIA NIM; start route-level A/B tests there.
- Cheapest tracked output pricing is tied, so migration risk shifts to quality, latency, and provider packaging.
- Qwen2-7B adds Structured outputs in local capability data.
- Provider overlap exists on Fireworks AI and NVIDIA NIM; start route-level A/B tests there.
- Cheapest tracked output pricing is tied, so migration risk shifts to quality, latency, and provider packaging.
- Check replacement coverage for Structured outputs before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-12-13 | 2024-06-05 |
| Context window | — | 128K |
| Parameters | 3.8B | 7.07B |
| Architecture | - | decoder only |
| License | Microsoft Research | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Phi-4 Mini | Qwen2-7B |
|---|---|---|
| Input price | $0.05/1M tokens | $0.05/1M tokens |
| Output price | $0.15/1M tokens | $0.15/1M tokens |
| Providers |
Capabilities
| Capability | Phi-4 Mini | Qwen2-7B |
|---|---|---|
| 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 | Qwen2-7B |
|---|---|---|
| Google-Proof Q&A | 25.2 | 55.4 |
| Massive Multitask Language Understanding | 67.3 | 80.2 |
Deep dive
On shared benchmark coverage, Google-Proof Q&A has Phi-4 Mini at 25.2 and Qwen2-7B at 55.4, with Qwen2-7B ahead by 30.2 points; Massive Multitask Language Understanding has Phi-4 Mini at 67.3 and Qwen2-7B at 80.2, with Qwen2-7B ahead by 12.9 points. The largest visible gap is 30.2 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 differs most on structured outputs: Qwen2-7B. 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 Qwen2-7B lists $0.05/1M input and $0.15/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Phi-4 Mini lower by about $0 per million blended tokens. Availability is 3 providers versus 5, so concentration risk also matters.
Choose Phi-4 Mini when provider fit are central to the workload. Choose Qwen2-7B when provider fit and broader provider choice 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 Qwen2-7B?
Phi-4 Mini is cheaper on tracked token pricing. Phi-4 Mini costs $0.05/1M input and $0.15/1M output tokens. Qwen2-7B costs $0.05/1M input and $0.15/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Phi-4 Mini or Qwen2-7B open source?
Phi-4 Mini is listed under Microsoft Research. Qwen2-7B 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 Qwen2-7B?
Qwen2-7B 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 Qwen2-7B?
Phi-4 Mini is available on Fireworks AI, NVIDIA NIM, and Novita AI. Qwen2-7B is available on DeepInfra, OctoAI API (Deprecated), Microsoft Foundry, Fireworks AI, and NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Phi-4 Mini over Qwen2-7B?
Pick Qwen2-7B for reasoning; Phi-4 Mini is better when provider fit matters more. If your workload also depends on provider fit, start with Phi-4 Mini; if it depends on provider fit, run the same evaluation with Qwen2-7B.
Continue comparing
Last reviewed: 2026-05-16. Data sourced from public model cards and provider documentation.