GPT-5 Mini vs PaliGemma
Compare GPT-5 Mini and PaliGemma side-by-side.
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These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.
Models in this comparison
GPT-5 Mini vs PaliGemma: Overview
GPT-5 Mini, released by OpenAI on August 7, 2025, is a mid-tier variant of the GPT-5 family that balances cost, speed, and capability. It is multimodal, supporting both text and image inputs, and offers a substantial input context window of ~400,000 tokens with output lengths up to ~128,000 tokens. While less powerful than the full GPT-5, it inherits its safety tuning, instruction-following improvements, and multimodal reasoning, making it a practical choice for developers who need large context handling without the expense of premium models.
GPT-5 Mini is optimized for affordability while retaining strong reasoning performance. Benchmarks show it outperforming earlier models such as GPT-4o on many multimodal and medical VQA tasks, though it lags behind GPT-5 on the most complex problems. Ideal use cases include prototyping, scalable content generation, document analysis, and mid-range reasoning tasks where efficiency and context capacity matter more than top-tier accuracy.
PaliGemma is a vision-language model released in May 2024 by Google, built by pairing the SigLIP-So400m vision encoder with the Gemma 2B language model. It is designed primarily as a compact, transfer-friendly base model for fine-tuning to downstream vision-language tasks, rather than as a chat-optimized assistant. PaliGemma draws architectural inspiration from the PaLI-3 model at Google Research, applying a similar encoder-decoder approach at a smaller and more accessible parameter scale.
PaliGemma accepts an image together with a text prompt and generates text output, supporting image captioning, visual question answering, optical character recognition, object detection, referring expression segmentation, and a range of related vision-language tasks when fine-tuned on task-specific data. The model is released at three input resolutions (224, 448, and 896 pixels), with higher resolutions providing stronger performance on tasks requiring fine visual detail such as OCR and document understanding. Google released pretrained (PT) checkpoints intended as fine-tuning bases, along with Mix variants that have been fine-tuned on a mixture of downstream tasks for direct use without additional training. PaliGemma is distributed under the Gemma license, a custom license from Google that permits commercial use subject to the terms of the Gemma Prohibited Use Policy. It was succeeded by PaliGemma 2 in December 2024, which extends the architecture to larger Gemma 2 language backbones at 3B, 10B, and 28B parameter sizes.
GPT-5 Mini vs PaliGemma Comparison Table
| Property | GPT-5 Mini | PaliGemma |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Aug 2025 | May 2024 |
| Context Window | 400K | — |
| Parameters | 3B | |
| License | Proprietary | Custom |
| Pricing per 1M tokens | ||
| Input $/1M | $0.250 | |
| Output $/1M | $2.00 | |
| Vision Tasks | ||
| Captioning | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Classification | Demo | |
| Object Detection | Demo | |
| OCR | Demo | |
| Model Features | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
| Foundation Vision | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Overall Score | 73.13% | |
| Avg Response Time | 11.72s | |
| Median input tokensincl. image tokens | 1.4K | |
| Median output tokens | 143 | |
| Est. cost / taskon this benchmark | $0.0006 | |
| Defect Detection | 80%(12/15) | |
| Document Understanding | 77.8%(7/9) | |
| Object Counting | 10%(1/10) | |
| Object Understanding | 85.7%(12/14) | |
| Spatial Understanding | 89.5%(17/19) | |
Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology