Phi-4 Mini vs Qwen3-8B
Phi-4 Mini (2024) and Qwen3-8B (2025) are compact production models from Microsoft Research and Alibaba. Phi-4 Mini ships a not-yet-sourced context window, while Qwen3-8B ships a 128K-token context window. On Google-Proof Q&A, Qwen3-8B leads by 33.7 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 Qwen3-8B for reasoning; Phi-4 Mini is better when provider fit matters more.
Decision scorecard
Local evidence first| Signal | Phi-4 Mini | Qwen3-8B |
|---|---|---|
| Decision fit | Classification | RAG, Long context, and Classification |
| Context window | — | 128K |
| Cheapest output | $0.15/1M tokens | $0.4/1M tokens |
| Provider routes | 3 tracked | 2 tracked |
| Shared benchmarks | 1 rows | Google-Proof Q&A 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.
- Qwen3-8B leads the largest shared benchmark signal on Google-Proof Q&A by 33.7 points.
- Qwen3-8B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Qwen3-8B uniquely exposes Structured outputs in local model data.
- Local decision data tags Qwen3-8B for RAG, Long context, and Classification.
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
Qwen3-8B
$140
Cheapest tracked route: OpenRouter
Estimated monthly gap: $62.50. Batch, cache, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Fireworks AI; start route-level A/B tests there.
- Qwen3-8B is $0.25/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Qwen3-8B adds Structured outputs in local capability data.
- Provider overlap exists on Fireworks AI; start route-level A/B tests there.
- Phi-4 Mini is $0.25/1M 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 | 2025-08-15 |
| Context window | — | 128K |
| Parameters | 3.8B | 8B |
| Architecture | - | decoder only |
| License | Microsoft Research | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Phi-4 Mini | Qwen3-8B |
|---|---|---|
| Input price | $0.05/1M tokens | $0.05/1M tokens |
| Output price | $0.15/1M tokens | $0.4/1M tokens |
| Providers |
Capabilities
| Capability | Phi-4 Mini | Qwen3-8B |
|---|---|---|
| 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 | Qwen3-8B |
|---|---|---|
| Google-Proof Q&A | 25.2 | 58.9 |
Deep dive
On shared benchmark coverage, Google-Proof Q&A has Phi-4 Mini at 25.2 and Qwen3-8B at 58.9, with Qwen3-8B ahead by 33.7 points. The largest visible gap is 33.7 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: Qwen3-8B. 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 Qwen3-8B lists $0.05/1M input and $0.4/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Phi-4 Mini lower by about $0.08 per million blended tokens. Availability is 3 providers versus 2, so concentration risk also matters.
Choose Phi-4 Mini when provider fit and broader provider choice are central to the workload. Choose Qwen3-8B when provider fit 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.
FAQ
Which is cheaper, Phi-4 Mini or Qwen3-8B?
Phi-4 Mini is cheaper on tracked token pricing. Phi-4 Mini costs $0.05/1M input and $0.15/1M output tokens. Qwen3-8B costs $0.05/1M input and $0.4/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Phi-4 Mini or Qwen3-8B open source?
Phi-4 Mini is listed under Microsoft Research. Qwen3-8B 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 Qwen3-8B?
Qwen3-8B 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 Qwen3-8B?
Phi-4 Mini is available on Fireworks AI, NVIDIA NIM, and Novita AI. Qwen3-8B is available on Fireworks AI and OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Phi-4 Mini over Qwen3-8B?
Pick Qwen3-8B 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 Qwen3-8B.
Continue comparing
Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.