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Phi-4 14B vs Qwen3.5-9B

Phi-4 14B (2024) and Qwen3.5-9B (2026) are general-purpose language models from Microsoft Research and Alibaba. Phi-4 14B ships a not-yet-sourced context window, while Qwen3.5-9B ships a 262K-token context window. On Google-Proof Q&A, Qwen3.5-9B leads by 25.6 pts. On pricing, Phi-4 14B costs $0.07/1M input tokens versus $0.1/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.

Phi-4 14B is ~54% cheaper at $0.07/1M; pay for Qwen3.5-9B only for vision-heavy evaluation.

Decision scorecard

Local evidence first
SignalPhi-4 14BQwen3.5-9B
Decision fitClassification and JSON / Tool useRAG, Agents, and Long context
Context window262K
Cheapest output$0.14/1M tokens$0.15/1M tokens
Provider routes3 tracked3 tracked
Shared benchmarks1 rowsGoogle-Proof Q&A leader

Decision tradeoffs

Choose Phi-4 14B when...
  • Phi-4 14B has the lower cheapest tracked output price at $0.14/1M tokens.
  • Local decision data tags Phi-4 14B for Classification and JSON / Tool use.
Choose Qwen3.5-9B when...
  • Qwen3.5-9B leads the largest shared benchmark signal on Google-Proof Q&A by 25.6 points.
  • Qwen3.5-9B has the larger context window for long prompts, retrieval packs, or transcript analysis.
  • Qwen3.5-9B uniquely exposes Vision, Multimodal, and Function calling in local model data.
  • Local decision data tags Qwen3.5-9B for RAG, Agents, and Long context.

Monthly cost at traffic

Estimate token spend from the cheapest tracked input and output prices on this page.

Lower estimate Phi-4 14B

Phi-4 14B

$87.00

Cheapest tracked route: OpenRouter

Qwen3.5-9B

$118

Cheapest tracked route: Together AI

Estimated monthly gap: $30.50. Batch, cache, and negotiated pricing are excluded from this local estimate.

Switch friction

Phi-4 14B -> Qwen3.5-9B
  • Provider overlap exists on OpenRouter; start route-level A/B tests there.
  • Qwen3.5-9B is $0.01/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
  • Qwen3.5-9B adds Vision, Multimodal, and Function calling in local capability data.
Qwen3.5-9B -> Phi-4 14B
  • Provider overlap exists on OpenRouter; start route-level A/B tests there.
  • Phi-4 14B is $0.01/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
  • Check replacement coverage for Vision, Multimodal, and Function calling before moving production traffic.

Specs

Specification
Released2024-12-132026-03-02
Context window262K
Parameters14B9B
Architecturedecoder onlydecoder only
LicenseOpen SourceApache 2.0
Knowledge cutoff--

Pricing and availability

Pricing attributePhi-4 14BQwen3.5-9B
Input price$0.07/1M tokens$0.1/1M tokens
Output price$0.14/1M tokens$0.15/1M tokens
Providers

Capabilities

CapabilityPhi-4 14BQwen3.5-9B
VisionNoYes
MultimodalNoYes
ReasoningNoNo
Function callingNoYes
Tool useNoYes
Structured outputsYesYes
Code executionNoNo

Benchmarks

BenchmarkPhi-4 14BQwen3.5-9B
Google-Proof Q&A56.181.7

Deep dive

On shared benchmark coverage, Google-Proof Q&A has Phi-4 14B at 56.1 and Qwen3.5-9B at 81.7, with Qwen3.5-9B ahead by 25.6 points. The largest visible gap is 25.6 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 vision: Qwen3.5-9B, multimodal input: Qwen3.5-9B, function calling: Qwen3.5-9B, and tool use: Qwen3.5-9B. Both models share structured outputs, 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 Qwen3.5-9B lists $0.1/1M input and $0.15/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Phi-4 14B lower by about $0.03 per million blended tokens. Availability is 3 providers versus 3, so concentration risk also matters.

Choose Phi-4 14B when provider fit and lower input-token cost are central to the workload. Choose Qwen3.5-9B 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.

FAQ

Which is cheaper, Phi-4 14B or Qwen3.5-9B?

Phi-4 14B is cheaper on tracked token pricing. Phi-4 14B costs $0.07/1M input and $0.14/1M output tokens. Qwen3.5-9B costs $0.1/1M input and $0.15/1M output tokens. Provider discounts or batch pricing can still change the final bill.

Is Phi-4 14B or Qwen3.5-9B open source?

Phi-4 14B is listed under Open Source. Qwen3.5-9B 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 Qwen3.5-9B?

Qwen3.5-9B 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 Qwen3.5-9B?

Qwen3.5-9B 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 function calling, Phi-4 14B or Qwen3.5-9B?

Qwen3.5-9B has the clearer documented function calling signal in this comparison. If function calling 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 Qwen3.5-9B?

Phi-4 14B is available on OpenRouter, Fireworks AI, and Microsoft Foundry. Qwen3.5-9B is available on Together AI, OpenRouter, and Alibaba Cloud PAI-EAS. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

Continue comparing

Last reviewed: 2026-05-16. Data sourced from public model cards and provider documentation.