Gemini 3.1 Pro vs Phi-4 Reasoning
Model Markets Rankings
Intelligence, Cost, and Efficiency
| Ranking | Gemini 3.1 Pro | Phi-4-reasoning |
|---|---|---|
| CostLower is better · Published-token output estimate | #22 of 36$0.161 per LiveBench case | UnrankedNot in the 36-model eligible cohort |
Ranks come from the current complete eligible cohorts. Green highlights appear only when both models are ranked in the same metric. Missing required inputs remain unranked, and the three dimensions are not collapsed into an overall winner.
Benchmark Performance
Available Benchmarks
Technical Differences
Side-by-Side Facts
| Field | Gemini 3.1 Pro | Phi-4-reasoning |
|---|---|---|
| Developer | Google DeepMind | Microsoft |
| Family | Gemini 3 | Phi 4 Reasoning |
| Model | Gemini 3.1 Pro | Phi-4-reasoning |
| Version | Gemini 3.1 Pro | Phi-4-reasoning |
| Lifecycle | preview | active |
| Released | Unknown | 2025-04-30 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Video, Audio, Document | Text |
| Output modalities | Text | Text |
| Context window | 1,049K | 33K |
| Total parameters | Unknown | 14.7B |
| Active parameters | Unknown | Unknown |
| License | Unknown | mit |
| Open weights | No | Yes |
| API available | Yes | Unknown |
| Self-hostable | No | Yes |
| Provider access | Google AI (Standard), Google Gemini (Standard) | Unknown |
| Capabilities | chat, generation, reasoning, tools | chat, generation, reasoning |
11 comparable fields · 10 material differences · Pair passes the primary-source comparison gate
Gemini 3.1 Pro Capabilities
Phi-4 Reasoning Capabilities
Internal Comparison Graph
Related Comparisons
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|---|---|---|---|
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Gemini 3.1 ProGoogle DeepMind | vs |  Hy4 previewTencent | cross-developer peerstext |
Seed 2.1 ProByteDance Seed | vs | Gemini 3.1 ProGoogle DeepMind | cross-developer peerstext |
Gemini 3.1 ProGoogle DeepMind | vs |  Mistral Large 3Mistral AI | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
Gemini 3.1 Pro vs Phi-4 Reasoning FAQs
Is Gemini 3.1 Pro or Phi-4 Reasoning better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 3.1 Pro and Phi-4 Reasoning, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Gemini 3.1 Pro or Phi-4 Reasoning?+
Only Gemini 3.1 Pro has a directly sourced input price: $2.00 per million tokens. Only Gemini 3.1 Pro has a directly sourced output price: $12.00 per million tokens.
Which has a larger context window, Gemini 3.1 Pro or Phi-4 Reasoning?+
Gemini 3.1 Pro has the larger sourced context window. Gemini 3.1 Pro supports 1,049K and Phi-4 Reasoning supports 33K.
Which performs better in benchmarks, Gemini 3.1 Pro or Phi-4 Reasoning?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Gemini 3.1 Pro or Phi-4 Reasoning be self-hosted?+
Phi-4 Reasoning is the only model in this pair currently marked as self-hostable. Gemini 3.1 Pro is not marked open weight; Phi-4 Reasoning is open weight.
Can Gemini 3.1 Pro and Phi-4 Reasoning understand images?+
Gemini 3.1 Pro is documented with image input; Phi-4 Reasoning is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Gemini 3.1 Pro or Phi-4 Reasoning?+
Neither has a larger sourced maximum output. Gemini 3.1 Pro is 66K and Phi-4 Reasoning is —.
Do Gemini 3.1 Pro and Phi-4 Reasoning support reasoning and tool use?+
Gemini 3.1 Pro: reasoning, tool calling, and image input. Phi-4 Reasoning: reasoning. Feature support does not establish relative quality.
Which is available from more inference providers, Gemini 3.1 Pro or Phi-4 Reasoning?+
Gemini 3.1 Pro has 2 sourced provider routes; Phi-4 Reasoning has 0, so Gemini 3.1 Pro has broader tracked availability.
Which offers better value, Gemini 3.1 Pro or Phi-4 Reasoning?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.