Gemini 3 Flash vs Grok 4.5
Compare Gemini 3 Flash and Grok 4.5 side-by-side. See how these vision models stack up in Object Detection, Classification, Open Prompt, OCR, and Image Captioning.
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Models in this comparison
Gemini 3 Flash vs Grok 4.5 on Vision Evals
Gemini 3 Flash scores higher on all five Vision Evals tasks.
The widest gap is Object Detection, where Gemini 3 Flash leads 38.6% to 19.0%.
Overall, Gemini 3 Flash averages 65.7% (#27 of 63) against 55.3% (#46 of 63) for Grok 4.5.
Gemini 3 Flash is both cheaper ($0.0024 vs $0.0078 per sample) and faster (5.8s vs 16.4s per sample).
Gemini 3 Flash vs Grok 4.5 Comparison Table
Evals updated October 13, 2026Pricing updated October 13, 2026
| Property | Gemini 3 Flash | Grok 4.5 |
|---|---|---|
| Organization | SpaceXAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Dec 2025 | 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 | $0.500 | $2.00 |
| Output $/1M | $3.00 | $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 | 65.7% | 55.3% |
| Avg cost / sample | $0.0024 | $0.0078 |
| Avg speed / sample | 5.83s | 16.41s |
| By task | ||
| Object Detection (low) | 38.6% | 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) | 67.6% | 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) | 93.8% | 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) | 63.9% | 57.1% |
| by category |
|
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| OCR (high) | 73.1% | 57.5% |
| by category |
|
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| Reasoning (low) | 64.9% | 57.6% ±1.7, Mean of 3 runs, range 55.6 to 58.9 |
| Reasoning (high) | 74.2% | 59.8% ±2.6, Mean of 3 runs, range 57.0 to 62.3 |
Gemini 3 Flash vs Grok 4.5: Overview
Gemini 3 Flash is a proprietary multimodal large language model developed by Google through Google DeepMind, designed to deliver fast, cost-efficient reasoning across real-time products and developer workflows. Released in December 2025, it is the Flash-tier variant of the Gemini 3 family, balancing low latency with reasoning quality approaching Pro models.
The model supports text, images, audio, and video, with an exceptionally large context window of roughly one million input tokens and outputs up to ~65k tokens. It emphasizes rapid responses for coding, summarization, analysis, and agentic tasks, and exposes configurable “thinking levels” via API to trade speed for deeper reasoning. Today, Gemini 3 Flash positions itself as a high-throughput, production-ready model, serving as the default in the Gemini app and Google Search’s AI Mode, optimized for scalable, interactive AI applications.
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.