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Gemma 3n 2B (free) vs Phi-4 Mini Flash Reasoning

Gemma 3n 2B (free) (2025) and Phi-4 Mini Flash Reasoning (2025) are frontier reasoning models from Google DeepMind and Microsoft Research. Gemma 3n 2B (free) ships a 8K-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.

Phi-4 Mini Flash Reasoning fits 16x more tokens; pick it for long-context work and Gemma 3n 2B (free) for tighter calls.

Specs

Specification
Released2025-04-032025-12-01
Context window8K128K
Parameters
Architecturedecoder onlydecoder only
LicenseOpen Source1
Knowledge cutoff--

Pricing and availability

Pricing attributeGemma 3n 2B (free)Phi-4 Mini Flash Reasoning
Input price--
Output price--
Providers

Pricing not yet sourced for either model.

Capabilities

CapabilityGemma 3n 2B (free)Phi-4 Mini Flash Reasoning
VisionNoNo
MultimodalNoNo
ReasoningNoYes
Function callingNoNo
Tool useNoNo
Structured outputsNoNo
Code executionNoNo

Benchmarks

No shared benchmark rows are currently sourced for this pair.

Deep dive

The capability footprint differs most on reasoning mode: Phi-4 Mini Flash Reasoning. 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: Gemma 3n 2B (free) has no token price sourced yet and Phi-4 Mini Flash Reasoning has no token price sourced yet. Provider availability is 1 tracked routes versus 1. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.

Choose Gemma 3n 2B (free) when provider fit are central to the workload. Choose Phi-4 Mini Flash Reasoning when reasoning depth and larger context windows 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. It also helps separate model capability from provider packaging, which can change cost and latency. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.

FAQ

Which has a larger context window, Gemma 3n 2B (free) or Phi-4 Mini Flash Reasoning?

Phi-4 Mini Flash Reasoning supports 128K tokens, while Gemma 3n 2B (free) supports 8K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.

Is Gemma 3n 2B (free) or Phi-4 Mini Flash Reasoning open source?

Gemma 3n 2B (free) is listed under Open Source. 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 reasoning mode, Gemma 3n 2B (free) 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.

Where can I run Gemma 3n 2B (free) and Phi-4 Mini Flash Reasoning?

Gemma 3n 2B (free) is available on NVIDIA NIM. Phi-4 Mini Flash Reasoning is available on NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

When should I pick Gemma 3n 2B (free) over Phi-4 Mini Flash Reasoning?

Phi-4 Mini Flash Reasoning fits 16x more tokens; pick it for long-context work and Gemma 3n 2B (free) for tighter calls. If your workload also depends on provider fit, start with Gemma 3n 2B (free); if it depends on reasoning depth, run the same evaluation with Phi-4 Mini Flash Reasoning.

Continue comparing

Last reviewed: 2026-05-01. Data sourced from public model cards and provider documentation.