Gemma 4 31B vs Grok 4.7
Compare Gemma 4 31B and Grok 4.7 side-by-side. See how these vision models stack up in Image Captioning, OCR, Open Prompt, Object Detection, and Classification.
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Models in this comparison
Gemma 4 31B vs Grok 4.7 on Vision Evals
Grok 4.7 scores higher on 3 of the 4 Vision Evals tasks.
The widest gap is Reasoning, where Grok 4.7 leads 64.2% to 52.8%.
Overall, Gemma 4 31B averages 57.7% (#39 of 63) against 63.5% (#31 of 63) for Grok 4.7.
Gemma 4 31B is cheaper ($0.0015 vs $0.020 per sample), while Grok 4.7 is faster (33.3s vs 34.4s per sample).
Gemma 4 31B vs Grok 4.7 Comparison Table
Evals updated October 13, 2026Pricing updated October 13, 2026
| Property | Gemma 4 31B | Grok 4.7 |
|---|---|---|
| Organization | SpaceXAI | |
| Category | open | closed |
| Modality | multimodal | — |
| Release Date | Apr 2026 | Sep 2026 |
| Context Window | 256K | 500K |
| Parameters | 31B | Unknown |
| License | Apache 2.0 | Proprietary |
| Commercial use | free for commercial use | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $0.090 | $2.00 |
| Output $/1M | $0.340 | $6.00 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | Supported | Supported |
| Classification | Demo | Demo |
| Document Question Answering | Supported | Supported |
| Image Tagging | Supported | Supported |
| Multi-Label Classification | Supported | Supported |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | Supported | Supported |
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | Supported | Not listed |
| LLMs with Vision Capabilities | Supported | Not listed |
| Multimodal Vision | Supported | Not listed |
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 57.7% 4/5 tasks | 63.5% |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0015 | $0.020 |
| Avg speed / sample | 34.36s | 33.31s |
| By task | ||
| Object Detection (low) | 47.5% ±0.6, Mean of 3 runs, range 46.8 to 48.0 | 40.4% ±0.6, Mean of 3 runs, range 39.8 to 41.0 |
| Object Detection (high) | – | 41.2% ±1.6, Mean of 3 runs, range 39.6 to 42.8 |
| Counting (low) | 51.4% ±2.7, Mean of 3 runs, range 48.6 to 54.0 | 61.7% ±1.3, Mean of 3 runs, range 60.8 to 63.5 |
| Counting (high) | – | 60.8% ±1.3, Mean of 3 runs, range 59.5 to 62.2 |
| Identification (low) | 79.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 | 87.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 |
| Identification (high) | – | 80.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 |
| OCR | – | 63.6% |
| by category |
| |
| Reasoning (low) | 52.8% ±1.3, Mean of 3 runs, range 51.7 to 54.3 | 64.2% ±2.3, Mean of 3 runs, range 62.3 to 66.9 |
| Reasoning (high) | – | 66.9% ±1.3, Mean of 3 runs, range 65.6 to 68.2 |
Gemma 4 31B vs Grok 4.7: 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.
Grok 4.7 is a proprietary multimodal reasoning model from SpaceXAI that accepts images alongside text and returns text-only output. On visual inputs it supports image captioning, visual question answering, OCR, document and chart question answering, and image classification and tagging, with all results expressed as generated text rather than bounding boxes or masks. Its 500,000 token context window leaves room for several images, long documents, or extended conversations about visual content in a single request.
The model exposes a configurable reasoning effort setting with low, medium, high, and xhigh levels (high by default), letting callers trade latency for the amount of deliberation spent on a prompt, including multi-step questions about an image. Built on a larger base model than Grok 4.6 with extended reinforcement learning on harder tasks, it works longer on difficult problems and checks its own work more carefully at the same serving speed. SpaceXAI's launch materials focus on coding and agentic knowledge work and report no image benchmark results.
Frequently Asked Questions
On Roboflow's Vision Evals, Grok 4.7 performed better. It scores higher on 3 of the 4 vision tasks and averages 63.5% (#31 of 63) against 57.7% (#39 of 63) for Gemma 4 31B. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Reasoning benchmark at low effort, Grok 4.7 leads with 64.2% against 52.8%. This is the widest gap between the two models across the benchmark's tasks.
Gemma 4 31B is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0015 per sample against $0.020. Actual costs depend on your image sizes, prompts, and output length.
Grok 4.7 is faster. Across Roboflow's Vision Evals it averaged 33.3s per inference against 34.4s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.
Yes. The comparison demo on this page runs both models on the same image side by side for image captioning and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.