Gemma 3 4B vs GPT-5
Compare Gemma 3 4B and GPT-5 side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.
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Gemma 3 4B vs GPT-5: Overview
Gemma 3 4B, released on March 12, 2025, is the mid-sized member of Google DeepMind’s open-weight Gemma 3 family. With about 4 billion parameters, it is multimodal—supporting text and image inputs and generating text outputs. Like the larger Gemma 3 models, it features a 128,000-token input context window with an output capacity of ~8,192 tokens, enabling it to handle long documents and mixed text–image reasoning tasks.
The 4B variant is designed as a balance between efficiency and capability: it offers multilingual support across 140+ languages, strong summarization and reasoning performance, and compatibility with moderate hardware. Inference can run with ~6.4 GB VRAM in BF16, or significantly less in quantized 8-bit (~4.4 GB) or 4-bit (~3.4 GB) modes, making it accessible to developers outside large-scale infrastructure. While it lags behind the 12B and 27B versions on the most complex reasoning and multimodal benchmarks, its lower compute footprint makes it ideal for research, prototyping, and practical deployment where efficiency matters.
GPT-5, released by OpenAI in August 2025, is a multimodal large language model that advances beyond the GPT-4 family with a new “unified system” architecture. This design allows the model to dynamically choose between fast responses and extended reasoning depending on task complexity. It supports text, code, and images, alongside stronger tool use and agentic workflows, making it more adaptable for real-world problem solving. While its exact context window size is not disclosed, GPT-5 is optimized for long-horizon reasoning and multi-step tool chaining, indicating substantially expanded capacity over its predecessors.
The release introduced specialized variants: GPT-5 Pro, offering extended reasoning for complex workflows, and GPT-5 Codex, optimized for advanced coding tasks such as large-scale refactoring and code review. GPT-5 shows benchmark gains in coding, biomedical reasoning, multimodal analysis, and scientific tasks. Developers also gain new controls, such as verbosity and personalization parameters, for greater steerability. With these improvements, GPT-5 positions itself as OpenAI’s most capable and versatile model, suited for enterprise automation, research, healthcare, and sophisticated coding environments.
Gemma 3 4B vs GPT-5 Comparison Table
| Property | Gemma 3 4B | GPT-5 |
|---|---|---|
| Organization | OpenAI | |
| Category | open | closed |
| Modality | multimodal | multimodal |
| Release Date | Mar 2025 | Aug 2025 |
| Context Window | 128K | — |
| Parameters | 4B | |
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.050 | $1.25 |
| Output $/1M | $0.100 | $10.00 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Classification | Demo | |
| Object Detection | Demo | |
| Model Features | ||
| Multimodal Vision | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Overall Score | 37.31% | |
| Avg Response Time | 16.80s | |
| Defect Detection | 60%(9/15) | |
| Document Understanding | 55.6%(5/9) | |
| Object Counting | 0%(0/10) | |
| Object Understanding | 42.9%(6/14) | |
| Spatial Understanding | 26.3%(5/19) | |