GPT-5 Mini vs PaliGemma

Compare GPT-5 Mini and PaliGemma side-by-side.

Compare GPT-5 Mini vs PaliGemma live

Run the same image across every model that supports a task and compare their outputs side-by-side.

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

Google

GPT-5 Mini vs PaliGemma: Overview

GPT-5 Mini

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

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

PropertyGPT-5 MiniPaliGemma
OrganizationOpenAIGoogle
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateAug 2025May 2024
Context Window400K
Parameters3B
LicenseProprietaryCustom
Pricing per 1M tokens
Input $/1M$0.250
Output $/1M$2.00
Vision Tasks
CaptioningDemo
Vision Language
Visual Question AnsweringDemo
ClassificationDemo
Object DetectionDemo
OCRDemo
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 Time11.72s
Median input tokensincl. image tokens1.4K
Median output tokens143
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