Gemini 2.5 Pro vs Phi-4-reasoning
Benchmark Performance
Available Benchmarks
Technical Differences
Side-by-Side Facts
| Field | Gemini 2.5 Pro | Phi-4-reasoning |
|---|---|---|
| Developer | Google DeepMind | Microsoft |
| Family | Gemini 2 5 | Phi 4 Reasoning |
| Model | Gemini 2.5 Pro | Phi-4-reasoning |
| Version | Gemini 2.5 Pro | Phi-4-reasoning |
| Lifecycle | active | active |
| Released | Unknown | 2025-04-30 |
| Knowledge cutoff | 2025-01-01 | Unknown |
| Input modalities | Text, Image, Video, Audio, Document | Text |
| Output modalities | Text | Text |
| Context window | 1,048,576 | 32,768 |
| Total parameters | Unknown | 14,659,507,200 |
| Active parameters | Unknown | Unknown |
| License | Unknown | mit |
| Open weights | No | Yes |
| API available | Yes | Unknown |
| Self-hostable | No | Yes |
| Provider access | Google AI (Standard) | Unknown |
| Capabilities | chat, generation, reasoning, tools | chat, generation, reasoning |
11 comparable fields · 9 material differences · Pair passes the primary-source comparison gate
Gemini 2.5 Pro Capabilities
Phi-4-reasoning Capabilities
Internal Comparison Graph
Related Comparisons
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|---|---|---|---|
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Gemini Computer UseGoogle DeepMind | vs | Gemini 2.5 ProGoogle DeepMind | family variantstext |
Gemini Deep ResearchGoogle DeepMind | vs | Gemini 2.5 ProGoogle DeepMind | family variantstext |
Gemini Deep Research MaxGoogle DeepMind | vs | Gemini 2.5 ProGoogle DeepMind | family variantstext |
Gemini 2.5 ProGoogle DeepMind | vs |  GPT-5.6 TerraOpenAI | cross-developer peerstext |
Claude Sonnet 5Anthropic | vs | Gemini 2.5 ProGoogle DeepMind | cross-developer peerstext |
Phi-4-mini-reasoningMicrosoft | vs | Phi-4-reasoningMicrosoft | family variantstext |
Phi-4-reasoningMicrosoft | vs | phi-4Microsoft | family variantstext |
Gemini 2.5 ProGoogle DeepMind | vs | GPT-4.1 NanoOpenAI | cross-developer peerstext |
Phi-4-multimodal-instructMicrosoft | vs | Phi-4-reasoningMicrosoft | family variantstext |
Primary Evidence
Sources and Freshness
Questions
Gemini 2.5 Pro vs Phi-4-reasoning FAQs
Is Gemini 2.5 Pro or Phi-4-reasoning better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 2.5 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 2.5 Pro or Phi-4-reasoning?+
Only Gemini 2.5 Pro has a directly sourced input price: $1.25 per million tokens. Only Gemini 2.5 Pro has a directly sourced output price: $10.00 per million tokens.
Which has a larger context window, Gemini 2.5 Pro or Phi-4-reasoning?+
Gemini 2.5 Pro has the larger sourced context window. Gemini 2.5 Pro supports 1,048,576 and Phi-4-reasoning supports 32,768.
Which performs better in benchmarks, Gemini 2.5 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 2.5 Pro or Phi-4-reasoning be self-hosted?+
Phi-4-reasoning is the only model in this pair currently marked as self-hostable. Gemini 2.5 Pro is not marked open weight; Phi-4-reasoning is open weight.
Can Gemini 2.5 Pro and Phi-4-reasoning understand images?+
Gemini 2.5 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 2.5 Pro or Phi-4-reasoning?+
Neither has a larger sourced maximum output. Gemini 2.5 Pro is 65,536 and Phi-4-reasoning is —.
Do Gemini 2.5 Pro and Phi-4-reasoning support reasoning and tool use?+
Gemini 2.5 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 2.5 Pro or Phi-4-reasoning?+
Gemini 2.5 Pro has 1 sourced provider route; Phi-4-reasoning has 0, so Gemini 2.5 Pro has broader tracked availability.
Which offers better value, Gemini 2.5 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.