Gemini 3.1 Pro vs OCR 4.1
At a Glance
| Compare |  Gemini 3.1 ProGoogle DeepMind |  OCR 4.1Mistral AI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #9 of 4679.7 score · 3/3 sources · complete | UnrankedNot in the 46-model eligible cohort |
| CostLower is better · Published-token output estimate | #27 of 44$0.161 per LiveBench case | UnrankedNot in the 44-model eligible cohort |
| EfficiencyHigher is better · MM Efficiency v1.5 | #9 of 3859.2 score · 3/3 sources · complete | UnrankedNot in the 38-model eligible cohort |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $2.00Google AI ↗ · Aug 29, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $12.00Google AI ↗ · Aug 29, 2026 | Not reported |
| Context windowMaximum documented tokens | 1,049K | Not reported |
| Model facts checked | Aug 29, 2026View model evidence → | Aug 29, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
Side-by-Side Facts
| Field | Gemini 3.1 Pro | OCR 4.1 |
|---|---|---|
| Developer | Google DeepMind | Mistral AI |
| Family | Gemini 3 | Mistral OCR |
| Model | Gemini 3.1 Pro | OCR 4.1 |
| Version | Gemini 3.1 Pro | OCR 4.1 |
| Lifecycle | preview | active |
| Released | Unknown | 2026-07-16 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Video, Audio, Document | Image, Document |
| Output modalities | Text | Text |
| Context window | 1,049K | Unknown |
| Total parameters | Unknown | Unknown |
| Active parameters | Unknown | Unknown |
| License | Unknown | Unknown |
| Open weights | No | No |
| API available | Yes | Yes |
| Self-hostable | No | No |
| Provider access | Google AI (Standard), Google Gemini (Standard) | Mistral AI (Standard) |
| Capabilities | chat, generation, reasoning, tools | bounding-box-extraction, document-ai, ocr, structured-annotations |
Gemini 3.1 Pro Capabilities
OCR 4.1 Capabilities
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Primary Evidence
Sources and Freshness
Questions
Gemini 3.1 Pro vs OCR 4.1 FAQs
Is Gemini 3.1 Pro or OCR 4.1 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 3.1 Pro and OCR 4.1, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Gemini 3.1 Pro or OCR 4.1?+
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 OCR 4.1?+
Neither model has a larger sourced context window in this comparison. Gemini 3.1 Pro is 1,049K and OCR 4.1 is —.
Which performs better in benchmarks, Gemini 3.1 Pro or OCR 4.1?+
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 OCR 4.1 be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. Gemini 3.1 Pro is not marked open weight; OCR 4.1 is not marked open weight.
Can Gemini 3.1 Pro and OCR 4.1 understand images?+
Gemini 3.1 Pro is documented with image input; OCR 4.1 is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Gemini 3.1 Pro or OCR 4.1?+
Neither has a larger sourced maximum output. Gemini 3.1 Pro is 66K and OCR 4.1 is —.
Do Gemini 3.1 Pro and OCR 4.1 support reasoning and tool use?+
Gemini 3.1 Pro: reasoning, tool calling, and image input. OCR 4.1: image input. Feature support does not establish relative quality.
Which is available from more inference providers, Gemini 3.1 Pro or OCR 4.1?+
Gemini 3.1 Pro has 2 sourced provider routes; OCR 4.1 has 1, so Gemini 3.1 Pro has broader tracked availability.
Which offers better value, Gemini 3.1 Pro or OCR 4.1?+
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.