Gemma 3 27B vs Llama 4 Maverick
We don't benchmark Gemma 3 27B or Llama 4 Maverick on Vision Evals. Gemma 3 27B is open-weight under Custom. Llama 4 Maverick is open-weight under Custom. Gemma 3 27B is cheaper per token ($0.08/M vs $0.35/M input). Test them side by side on your own images below.
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Gemma 3 27B vs Llama 4 Maverick Comparison Table
Pricing updated November 5, 2026
| Property | Gemma 3 27B | Llama 4 Maverick |
|---|---|---|
| Organization | Meta | |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Mar 2025 | Apr 2025 |
| Context Window | 128K | 1.0M |
| Parameters | Unknown | 400B |
| License | Custom | Custom |
| Commercial use | See license terms | See license terms |
| Pricing per 1M tokens | ||
| Input $/1M | $0.080 | $0.350 |
| Output $/1M | $0.350 | $1.00 |
| Vision Tasks | ||
| Object Detection | Not listed | Supported |
| Model Features | ||
Both support 3 more features | ||
Gemma 3 27B vs Llama 4 Maverick: 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.
Llama 4 Maverick, introduced on April 5, 2025, is one of the first models in Meta’s Llama 4 family, designed as a natively multimodal model supporting text + image inputs with text outputs. It employs a Mixture-of-Experts (MoE) architecture with 128 experts, activating ~17B parameters per token out of a pool of ~400B total parameters. This design improves scalability, efficiency, and reasoning capacity. Maverick has a 1M-token context window, enabling it to handle large documents, extended conversations, and multimodal reasoning. Its knowledge cutoff is August 2024.
The model is released under the Llama 4 Community License and comes in both base and instruction-tuned (“Instruct”) versions. Maverick is widely deployed via Hugging Face, Google Vertex AI, Amazon Bedrock, and Oracle Cloud, making it one of the most accessible large open-weight models. However, it outputs text only (no image/audio generation) and, while input capacity is huge, output limits are typically much smaller. The MoE design also raises hardware demands, as maintaining 128 experts requires significant compute resources, and Meta’s license introduces restrictions around commercial-scale use.