Gemma 4 31B vs GPT-6.1 Sol
Compare Gemma 4 31B and GPT-6.1 Sol 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 GPT-6.1 Sol on Vision Evals
GPT-6.1 Sol scores higher on all six Vision Evals tasks.
The widest gap is Object Detection, where GPT-6.1 Sol leads 80.8% to 47.5%.
Overall, Gemma 4 31B averages 67.0% (#36 of 61) against 85.5% (#4 of 61) for GPT-6.1 Sol.
Gemma 4 31B is cheaper ($0.0015 vs $0.0061 per sample), while GPT-6.1 Sol is faster (14.3s vs 34.4s per sample).
Gemma 4 31B vs GPT-6.1 Sol Comparison Table
Evals updated September 29, 2026Pricing updated September 29, 2026
| Property | Gemma 4 31B | GPT-6.1 Sol |
|---|---|---|
| Organization | OpenAI | |
| Category | open | closed |
| Modality | multimodal | multimodal |
| Release Date | Apr 2026 | Sep 2026 |
| Context Window | 256K | 1.1M |
| Parameters | 31B | undisclosed |
| License | Apache 2.0 | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.090 | $2.00 |
| Output $/1M | $0.340 | $10.00 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | Demo |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Promptable Concept Segmentation | Demo | |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 67.0% | 85.5% |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0015 | $0.0061 |
| Avg speed / sample | 34.36s | 14.31s |
| By task | ||
| Object Detection (low) | 47.5% ±0.6, Mean of 3 runs, range 46.8 to 48.0 | 80.8% ±0.1, Mean of 3 runs, range 80.7 to 80.9 |
| Object Detection (high) | – | 81.6% ±0.4, Mean of 3 runs, range 81.1 to 82.0 |
| Counting (low) | 51.4% ±2.7, Mean of 3 runs, range 48.6 to 54.0 | 78.8% ±3.4, Mean of 3 runs, range 75.7 to 82.4 |
| Counting (high) | – | 80.2% ±3.4, Mean of 3 runs, range 77.0 to 83.8 |
| Identification (low) | 79.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| Identification (high) | – | 88.5% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| OCR (low) | 90.8% ±0.6, Mean of 3 runs, range 90.2 to 91.5 | 92.0% ±0.5, Mean of 3 runs, range 91.5 to 92.5 |
| OCR (high) | – | 91.7% ±0.3, Mean of 3 runs, range 91.2 to 91.9 |
| Data Extraction (low) | 80.4% ±1.0, Mean of 3 runs, range 79.4 to 81.4 | 88.0% ±0.5, Mean of 3 runs, range 87.6 to 88.7 |
| Data Extraction (high) | – | 90.0% ±1.0, Mean of 3 runs, range 88.7 to 90.7 |
| Reasoning (low) | 52.8% ±1.3, Mean of 3 runs, range 51.7 to 54.3 | 83.7% ±1.3, Mean of 3 runs, range 82.1 to 84.8 |
| Reasoning (high) | – | 88.7% ±2.0, Mean of 3 runs, range 87.4 to 91.4 |
Gemma 4 31B vs GPT-6.1 Sol: 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.
GPT-6.1 Sol is a reasoning model in OpenAI's GPT-6 series that accepts text and image input and returns text. It is an upgrade to GPT-6 Sol positioned to approach the intelligence of the larger GPT-6 Astra model on agentic coding, computer use, and professional knowledge work. The model exposes an adjustable reasoning effort control, ranging from low settings for simple turns to maximum settings for harder tasks, and can be driven with tool use enabled or disabled. It operates over a context window of roughly one million tokens and emits up to 128,000 output tokens in a single response, which supports long-running agent loops over large codebases and multi-document collections. Audio and video inputs are not supported.
On the visual side, the model is evaluated on GDP.pdf, a benchmark that asks professional questions about complex PDF documents containing tables, charts, diagrams, and fine-print details, and on OSWorld 2.0, which measures agents operating graphical computer applications. OpenAI reports that GPT-6.1 Sol performs on par with or better than GPT-6 Sol across its image input safety evaluations, and that the share of responses containing a factual error at low reasoning effort falls from 11.4 percent to 7.7 percent.
Frequently Asked Questions
On Roboflow's Vision Evals, GPT-6.1 Sol performed better. It scores higher on all six vision tasks and averages 85.5% (#4 of 61) against 67.0% (#36 of 61) 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 Object Detection benchmark at low effort, GPT-6.1 Sol leads with 80.8% against 47.5%. 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 $0.0061. Actual costs depend on your image sizes, prompts, and output length.
GPT-6.1 Sol is faster. Across Roboflow's Vision Evals it averaged 14.3s 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.