Gemma 4 31B vs Llama 4 Maverick
Gemma 4 31B: 57.7% average across four Vision Evals tasks (#44 of 68 models). We have not run Llama 4 Maverick through Roboflow's Vision Evals, so there is no head-to-head score. Run both on your own image below.
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Gemma 4 31B vs Llama 4 Maverick Comparison Table
Evals updated November 10, 2026Pricing updated November 10, 2026
| Property | Gemma 4 31B | Llama 4 Maverick |
|---|---|---|
| Organization | Meta | |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Apr 2026 | Apr 2025 |
| Context Window | 256K | 1.0M |
| Parameters | 31B | 400B |
| License | Apache 2.0 | Custom |
| Commercial use | free for commercial use | See license terms |
| Pricing per 1M tokens | ||
| Input $/1M | $0.200 | $0.350 |
| Output $/1M | $0.400 | $1.00 |
| Vision Tasks | ||
| Classification | Demo | Supported |
| Object Detection | Demo | Supported |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks | ||
| Overall | 57.7%* | Not evaluated |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0015 | – |
| Avg speed / sample | 34.36s | – |
| By task | ||
| Object Detection | 47.5% ±0.6, Mean of 3 runs, range 46.8 to 48.0 | – |
| Counting | 51.4% ±2.7, Mean of 3 runs, range 48.6 to 54.0 | – |
| Identification | 79.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 | – |
| OCR | – | – |
| by category | ||
| Reasoning | 52.8% ±1.3, Mean of 3 runs, range 51.7 to 54.3 | – |
Gemma 4 31B vs Llama 4 Maverick: Overview
Gemma 4 31B is the largest dense model in Google's Gemma 4 family, built from the same research as Gemini 3 and released as open weights under the Apache 2.0 license. It supports a 256K token context window with text and image input, configurable thinking mode for step-by-step reasoning, and multilingual support across 140+ languages. The unquantized model fits on a single 80GB GPU.
For vision tasks, Gemma 4 31B supports image understanding with variable aspect ratios and resolutions, and can output structured bounding boxes for UI element detection, making it useful for document parsing and UI understanding. Compared to Gemma 3, it delivers stronger reasoning and multimodal performance. It is part of a four-size family alongside the 26B A4B MoE variant and two on-device models (E2B, E4B), with the 31B dense variant optimized for output quality and fine-tuning over inference speed.
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.