Gemma 3 27B vs GPT-5.4
Compare Gemma 3 27B and GPT-5.4 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-5.4: 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-5.4 is a proprietary multimodal large language model developed by OpenAI and released on March 5, 2026. It is designed for professional workloads such as advanced software development, research, and agentic automation. The model combines the general reasoning capabilities of the GPT-5 series with software engineering improvements derived from GPT-5.3-Codex. In the API and Codex environments it supports context windows of up to 1 million tokens, enabling long-context reasoning and large-scale code or document workflows.
Compared with GPT-5.2, GPT-5.4 reduces false individual claims by 33% and lowers overall response errors by 18%, improving factual reliability across complex tasks. It is also the first general-purpose OpenAI release with native computer-use capabilities, allowing agents to interact with desktops, browsers, and external applications to complete multi-step workflows. The model family includes three variants: GPT-5.4 (standard), GPT-5.4 Pro for higher-performance workloads, and GPT-5.4 Thinking, a reasoning-oriented version in ChatGPT that presents an upfront plan before generating its response. The API also introduces a Tool Search system that allows models to retrieve tool definitions dynamically, reducing token usage in tool-heavy integrations.
Gemma 3 27B vs GPT-5.4 Comparison Table
| Property | Gemma 3 27B | GPT-5.4 |
|---|---|---|
| Organization | OpenAI | |
| Category | open | closed |
| Modality | multimodal | multimodal |
| Release Date | Mar 2025 | Mar 2026 |
| Context Window | 128K | 1.1M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.080 | $2.50 |
| Output $/1M | $0.160 | $15.00 |
| 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 | 58.21% | 77.61% |
| Avg Response Time | 33.60s | 7.16s |
| Median input tokensincl. image tokens | 1.4K | |
| Median output tokens | 108 | |
| Est. cost / taskon this benchmark | $0.0052 | |
| Defect Detection | 60%(9/15) | 86.7%(13/15) |
| Document Understanding | 77.8%(7/9) | 88.9%(8/9) |
| Object Counting | 10%(1/10) | 40%(4/10) |
| Object Understanding | 71.4%(10/14) | 85.7%(12/14) |
| Spatial Understanding | 63.2%(12/19) | 78.9%(15/19) |
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