Gemma 3 27B vs GPT-5 Nano
Compare Gemma 3 27B and GPT-5 Nano side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.
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Gemma 3 27B vs GPT-5 Nano Comparison Table
Evals updated August 6, 2026Pricing updated August 7, 2026
| Property | Gemma 3 27B | GPT-5 Nano |
|---|---|---|
| Organization | OpenAI | |
| Category | open | closed |
| Modality | multimodal | multimodal |
| Release Date | Mar 2025 | Aug 2025 |
| Context Window | 128K | 400K |
| Parameters | ||
| License | Custom | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.080 | $0.050 |
| Output $/1M | $0.450 | $0.400 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Object Detection | Demo | |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Gemma 3 27B vs GPT-5 Nano: Overview
Gemma 3 27B, announced on March 12, 2025, is the largest open-weight model in Google DeepMind’s Gemma 3 family. With around 27 billion parameters, it is multimodal—accepting both text and images as input and producing text outputs. It supports a 128,000-token context window and typically generates up to ~8,192 tokens, enabling it to process multi-page documents, extended conversations, or large batches of images in a single prompt.
The model is instruction-tuned in its “-it” variants for chat, reasoning, and summarization use cases, and it supports structured outputs and function calling. It is multilingual, covering over 140 languages. Deployment is flexible: the full BF16 model requires ~46 GB of VRAM, but quantization-aware training (QAT) versions in 8-bit or 4-bit reduce the footprint significantly, allowing more accessible use outside large-scale clusters. While it delivers stronger reasoning and multimodal performance than smaller Gemma models, it remains lighter and more open than proprietary systems, making it well-suited for research, development, and fine-tuned applications.
GPT-5 Nano, released by OpenAI on August 7, 2025, is the smallest and most cost-efficient model in the GPT-5 family. Like its larger counterparts, it is multimodal—accepting text and images, supporting tool use, structured outputs, and reasoning—but it is optimized for speed, low latency, and affordability. It features input and output token limits of roughly 272K and 128K tokens respectively, enabling large-context processing even at its compact scale. Its knowledge cutoff is around May 2024, slightly earlier than the full GPT-5 model.
GPT-5 Nano is well-suited for high-volume or cost-sensitive deployments such as mobile apps, embedded AI systems, or rapid-response APIs. While it offers less depth on complex reasoning and coding tasks compared to GPT-5 Mini or Pro, it retains core multimodal and agentic capabilities, making it an attractive option where efficiency and scale matter more than maximum performance.