Gemini 3.1 Pro vs Grok 4.5
Compare Gemini 3.1 Pro and Grok 4.5 side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, Classification, OCR, and Object Detection.
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Models in this comparison
Gemini 3.1 Pro vs Grok 4.5 on Vision Evals
Gemini 3.1 Pro scores higher on all five Vision Evals tasks.
The widest gap is Object Detection, where Gemini 3.1 Pro leads 67.4% to 19.0%.
Overall, Gemini 3.1 Pro averages 76.8% (#13 of 63) against 55.3% (#46 of 63) for Grok 4.5.
Grok 4.5 is cheaper ($0.0078 vs $0.010 per sample), while Gemini 3.1 Pro is faster (8.4s vs 16.4s per sample).
Gemini 3.1 Pro vs Grok 4.5 Comparison Table
Evals updated October 13, 2026Pricing updated October 13, 2026
| Property | Gemini 3.1 Pro | Grok 4.5 |
|---|---|---|
| Organization | SpaceXAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | Jul 2026 |
| Context Window | 1.0M | 500K |
| Parameters | Unknown | Unknown |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $2.00 |
| Output $/1M | $12.00 | $6.00 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | Supported | Supported |
| Classification | Demo | Demo |
| Document Question Answering | Supported | Supported |
| Image Tagging | Supported | Supported |
| Multi-Label Classification | Supported | Supported |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | Supported | Supported |
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | Supported | Supported |
| LLMs with Vision Capabilities | Supported | Supported |
| Multimodal Vision | Supported | Supported |
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 76.8% | 55.3% |
| Avg cost / sample | $0.010 | $0.0078 |
| Avg speed / sample | 8.44s | 16.41s |
| By task | ||
| Object Detection (low) | 67.4% | 19.0% ±0.8, Mean of 3 runs, range 18.0 to 19.6 |
| Object Detection (high) | – | 17.8% ±0.3, Mean of 3 runs, range 17.5 to 18.0 |
| Counting (low) | 71.6% | 59.5% ±3.4, Mean of 3 runs, range 55.4 to 62.2 |
| Counting (high) | – | 57.7% ±3.4, Mean of 3 runs, range 54.0 to 60.8 |
| Identification (low) | 100.0% | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 |
| Identification (high) | – | 85.4% ±3.1, Mean of 3 runs, range 81.3 to 87.5 |
| OCR (low) | 72.6% | 57.1% |
| by category |
|
|
| OCR (high) | 73.1% | 57.5% |
| by category |
|
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| Reasoning (low) | 72.2% | 57.6% ±1.7, Mean of 3 runs, range 55.6 to 58.9 |
| Reasoning (high) | 74.8% | 59.8% ±2.6, Mean of 3 runs, range 57.0 to 62.3 |
Gemini 3.1 Pro vs Grok 4.5: Overview
Gemini 3.1 Pro is a proprietary multimodal model from Google’s Gemini 3 series, released in early 2026 and designed for advanced reasoning across large multimodal datasets. It accepts text, images, audio, video, and documents, supporting up to a 1-million-token input context with up to 64k output tokens. Compared with Gemini 3 Pro, it improves long-context synthesis and multi-step reasoning, enabling more reliable analysis of large documents, datasets, and software codebases.
The model also advances visual understanding and grounding, allowing it to interpret UI screenshots, diagrams, and real-world scenes while referencing specific regions within images or video. These capabilities make Gemini 3.1 Pro well suited for multimodal workflows involving document processing, interface analysis, robotics research, and complex visual reasoning.
Grok 4.5 is a proprietary reasoning model from SpaceXAI (xAI) that accepts interleaved text and image input and returns text, with a 500,000 token context window. xAI positions it as a model for coding, agentic software work, and knowledge tasks, and states it was trained in the company's Memphis data centers on datasets spanning science, engineering, and mathematics. Its reinforcement learning stage covers hundreds of thousands of multi step software engineering tasks scored by automated checks and model based grading, and training is reported to have run on tens of thousands of NVIDIA GB300 GPUs using an asynchronous scheme in which multi hour agentic rollouts continue while learning proceeds in parallel, targeting long horizon autonomous operation rather than single turn inference.
For vision, the model consumes JPEG and PNG images in any order relative to text prompts, covering visual question answering, description of chart and document imagery, and reading text rendered inside a scene. Reasoning effort is configurable, and the model supports function calling and structured outputs, so image inputs can be interleaved with tool calls inside agent loops. xAI has not published a technical report, architecture details, or parameter count, and reported mixture of experts sizing figures come from secondary coverage rather than official documentation.