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GPT-5.4 Nano vs Phi-4 Mini Flash Reasoning

GPT-5.4 Nano (2026) and Phi-4 Mini Flash Reasoning (2025) are frontier reasoning models from OpenAI and Microsoft Research. GPT-5.4 Nano ships a 400K-token context window, while Phi-4 Mini Flash Reasoning ships a 128K-token context window. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing. The goal is to make the tradeoff clear before deeper testing.

GPT-5.4 Nano is safer overall; choose Phi-4 Mini Flash Reasoning when reasoning depth matters.

Decision scorecard

Local evidence first
SignalGPT-5.4 NanoPhi-4 Mini Flash Reasoning
Decision fitCoding, RAG, and AgentsLong context
Context window400K128K
Cheapest output$1.25/1M tokens-
Provider routes2 tracked1 tracked
Shared benchmarks0 rows0 rows

Decision tradeoffs

Choose GPT-5.4 Nano when...
  • GPT-5.4 Nano has the larger context window for long prompts, retrieval packs, or transcript analysis.
  • GPT-5.4 Nano has broader tracked provider coverage for fallback and procurement flexibility.
  • GPT-5.4 Nano uniquely exposes Multimodal, Function calling, and Tool use in local model data.
  • Local decision data tags GPT-5.4 Nano for Coding, RAG, and Agents.
Choose Phi-4 Mini Flash Reasoning when...
  • Phi-4 Mini Flash Reasoning uniquely exposes Reasoning in local model data.
  • Local decision data tags Phi-4 Mini Flash Reasoning for Long context.

Monthly cost at traffic

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

GPT-5.4 Nano

$473

Cheapest tracked route: OpenAI API

Phi-4 Mini Flash Reasoning

Unavailable

No complete token price in local provider data

Cost delta unavailable until both models have sourced input and output token prices.

Switch friction

GPT-5.4 Nano -> Phi-4 Mini Flash Reasoning
  • No overlapping tracked provider route is sourced for GPT-5.4 Nano and Phi-4 Mini Flash Reasoning; plan for SDK, billing, or endpoint changes.
  • Check replacement coverage for Multimodal, Function calling, and Tool use before moving production traffic.
  • Phi-4 Mini Flash Reasoning adds Reasoning in local capability data.
Phi-4 Mini Flash Reasoning -> GPT-5.4 Nano
  • No overlapping tracked provider route is sourced for Phi-4 Mini Flash Reasoning and GPT-5.4 Nano; plan for SDK, billing, or endpoint changes.
  • Check replacement coverage for Reasoning before moving production traffic.
  • GPT-5.4 Nano adds Multimodal, Function calling, and Tool use in local capability data.

Specs

Specification
Released2026-03-052025-12-01
Context window400K128K
Parameters
Architecturedecoder onlydecoder only
LicenseProprietary1
Knowledge cutoff2025-08-

Pricing and availability

Pricing attributeGPT-5.4 NanoPhi-4 Mini Flash Reasoning
Input price$0.2/1M tokens-
Output price$1.25/1M tokens-
Providers

Capabilities

CapabilityGPT-5.4 NanoPhi-4 Mini Flash Reasoning
VisionNoNo
MultimodalYesNo
ReasoningNoYes
Function callingYesNo
Tool useYesNo
Structured outputsYesNo
Code executionYesNo

Benchmarks

No shared benchmark rows are currently sourced for this pair.

Deep dive

The capability footprint differs most on multimodal input: GPT-5.4 Nano, reasoning mode: Phi-4 Mini Flash Reasoning, function calling: GPT-5.4 Nano, tool use: GPT-5.4 Nano, structured outputs: GPT-5.4 Nano, and code execution: GPT-5.4 Nano. 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.

Pricing coverage is uneven: GPT-5.4 Nano has $0.2/1M input tokens and Phi-4 Mini Flash Reasoning has no token price sourced yet. Provider availability is 2 tracked routes versus 1. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.

Choose GPT-5.4 Nano when coding workflow support, larger context windows, and broader provider choice are central to the workload. Choose Phi-4 Mini Flash Reasoning when reasoning depth 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 has a larger context window, GPT-5.4 Nano or Phi-4 Mini Flash Reasoning?

GPT-5.4 Nano supports 400K tokens, while Phi-4 Mini Flash Reasoning supports 128K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.

Is GPT-5.4 Nano or Phi-4 Mini Flash Reasoning open source?

GPT-5.4 Nano is listed under Proprietary. Phi-4 Mini Flash Reasoning is listed under 1. 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 multimodal input, GPT-5.4 Nano or Phi-4 Mini Flash Reasoning?

GPT-5.4 Nano 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 reasoning mode, GPT-5.4 Nano or Phi-4 Mini Flash Reasoning?

Phi-4 Mini Flash Reasoning has the clearer documented reasoning mode signal in this comparison. If reasoning mode 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, GPT-5.4 Nano or Phi-4 Mini Flash Reasoning?

GPT-5.4 Nano 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 GPT-5.4 Nano and Phi-4 Mini Flash Reasoning?

GPT-5.4 Nano is available on OpenAI API and OpenRouter. Phi-4 Mini Flash Reasoning is available on NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

Continue comparing

Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.