Phi-4 14B vs Qwen3-8B
Phi-4 14B (2024) and Qwen3-8B (2025) are compact production models from Microsoft Research and Alibaba. Phi-4 14B ships a 16k-token context window, while Qwen3-8B ships a 128k-token context window. On Google-Proof Q&A, Qwen3-8B leads by 2.8 pts. On pricing, Qwen3-8B costs $0.04/1M input tokens versus $0.07/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Qwen3-8B is ~86% cheaper at $0.04/1M; pay for Phi-4 14B only for provider fit.
Decision scorecard
Local evidence first| Signal | Phi-4 14B | Qwen3-8B |
|---|---|---|
| Best for | provider-routed production | provider-routed production |
| Decision fit | Classification and JSON / Tool use | RAG, Long context, and Classification |
| Context window | 16k | 128k |
| Cheapest output | $0.14/1M tokens | $0.14/1M tokens |
| Provider routes | 3 tracked | 3 tracked |
| Shared benchmarks | 1 rows | Google-Proof Q&A leader |
Decision tradeoffs
- Local decision data tags Phi-4 14B for Classification and JSON / Tool use.
- Qwen3-8B leads the largest shared benchmark signal on Google-Proof Q&A by 2.8 points.
- Qwen3-8B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Qwen3-8B has the lower cheapest tracked output price at $0.14/1M tokens.
- 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 route or tier on this page.
Phi-4 14B
$87.00
Cheapest tracked route/tier: OpenRouter
Qwen3-8B
$62.50
Cheapest tracked route/tier: Novita AI
Estimated monthly gap: $24.50. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Fireworks AI and OpenRouter; start route-level A/B tests there.
- Qwen3-8B is $0.00/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Provider overlap exists on OpenRouter and Fireworks AI; start route-level A/B tests there.
- Phi-4 14B is $0.00/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-12-13 | 2025-08-15 |
| Context window | 16k | 128k |
| Parameters | 14B | 8B |
| Architecture | decoder only | decoder only |
| License | Open Source | Apache 2.0 |
| Knowledge cutoff | 2024-06 | - |
Pricing and availability
| Pricing attribute | Phi-4 14B | Qwen3-8B |
|---|---|---|
| Input price | $0.07/1M tokens | $0.04/1M tokens |
| Output price | $0.14/1M tokens | $0.14/1M tokens |
| Providers |
Capabilities
| Capability | Phi-4 14B | Qwen3-8B |
|---|---|---|
| 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 |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | Phi-4 14B | Qwen3-8B |
|---|---|---|
| Google-Proof Q&A | 56.1 | 58.9 |
Deep dive
On shared benchmark coverage, Google-Proof Q&A has Phi-4 14B at 56.1 and Qwen3-8B at 58.9, with Qwen3-8B ahead by 2.8 points. The largest visible gap is 2.8 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 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 on the cheapest tracked provider, while Qwen3-8B lists $0.04/1M input and $0.14/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Qwen3-8B lower by about $0.02 per million blended tokens. Availability is 3 providers versus 3, so concentration risk also matters.
Choose Phi-4 14B when provider fit are central to the workload. Choose Qwen3-8B when long-context analysis, larger context windows, and lower input-token cost are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Which has a larger context window, Phi-4 14B or Qwen3-8B?
Qwen3-8B supports 128k 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 Qwen3-8B?
Qwen3-8B is cheaper on tracked token pricing. Phi-4 14B costs $0.07/1M input and $0.14/1M output tokens. Qwen3-8B costs $0.04/1M input and $0.14/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Phi-4 14B or Qwen3-8B open source?
Phi-4 14B is listed under Open Source. 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 14B or Qwen3-8B?
Both Phi-4 14B and Qwen3-8B 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 Qwen3-8B?
Phi-4 14B is available on OpenRouter, Fireworks AI, and Microsoft Foundry. Qwen3-8B is available on Fireworks AI, OpenRouter, and Novita AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Phi-4 14B over Qwen3-8B?
Qwen3-8B is ~86% cheaper at $0.04/1M; pay for Phi-4 14B only for provider fit. If your workload also depends on provider fit, start with Phi-4 14B; if it depends on long-context analysis, run the same evaluation with Qwen3-8B.
Continue comparing
Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.