Gemini 3.1 Pro vs Phi-4 Multimodal Instruct
Model Markets Rankings
Intelligence, Cost, and Efficiency
| Ranking | Gemini 3.1 Pro | Phi-4-multimodal-instruct |
|---|---|---|
| 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-multimodal-instruct |
|---|---|---|
| Developer | Google DeepMind | Microsoft |
| Family | Gemini 3 | Phi 4 Multimodal Instruct |
| Model | Gemini 3.1 Pro | Phi-4-multimodal-instruct |
| Version | Gemini 3.1 Pro | Phi-4-multimodal-instruct |
| Lifecycle | preview | active |
| Released | Unknown | 2025-02-26 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Video, Audio, Document | Text, Image, Audio |
| Output modalities | Text | Text |
| Context window | 1,049K | 131K |
| Total parameters | Unknown | 5.6B |
| 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 |
11 comparable fields · 10 material differences · Pair passes the primary-source comparison gate
Gemini 3.1 Pro Capabilities
Phi-4 Multimodal Instruct Capabilities
Internal Comparison Graph
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Primary Evidence
Sources and Freshness
Questions
Gemini 3.1 Pro vs Phi-4 Multimodal Instruct FAQs
Is Gemini 3.1 Pro or Phi-4 Multimodal Instruct better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 3.1 Pro and Phi-4 Multimodal Instruct, 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 Multimodal Instruct?+
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 Multimodal Instruct?+
Gemini 3.1 Pro has the larger sourced context window. Gemini 3.1 Pro supports 1,049K and Phi-4 Multimodal Instruct supports 131K.
Which performs better in benchmarks, Gemini 3.1 Pro or Phi-4 Multimodal Instruct?+
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 Multimodal Instruct be self-hosted?+
Phi-4 Multimodal Instruct is the only model in this pair currently marked as self-hostable. Gemini 3.1 Pro is not marked open weight; Phi-4 Multimodal Instruct is open weight.
Can Gemini 3.1 Pro and Phi-4 Multimodal Instruct understand images?+
Gemini 3.1 Pro is documented with image input; Phi-4 Multimodal Instruct is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Gemini 3.1 Pro or Phi-4 Multimodal Instruct?+
Neither has a larger sourced maximum output. Gemini 3.1 Pro is 66K and Phi-4 Multimodal Instruct is —.
Do Gemini 3.1 Pro and Phi-4 Multimodal Instruct support reasoning and tool use?+
Gemini 3.1 Pro: reasoning, tool calling, and image input. Phi-4 Multimodal Instruct: image input. Feature support does not establish relative quality.
Which is available from more inference providers, Gemini 3.1 Pro or Phi-4 Multimodal Instruct?+
Gemini 3.1 Pro has 2 sourced provider routes; Phi-4 Multimodal Instruct has 0, so Gemini 3.1 Pro has broader tracked availability.
Which offers better value, Gemini 3.1 Pro or Phi-4 Multimodal Instruct?+
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.