Gemma 4 12B vs GPT-6.1 Sol
GPT-6.1 Sol: 81.7% average across five Vision Evals tasks (#9 of 69 models). We have not run Gemma 4 12B through Roboflow's Vision Evals, so there is no head-to-head score.
Compare Gemma 4 12B vs GPT-6.1 Sol live
Run the same image across every model that supports a task and compare their outputs side-by-side.
These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.
Gemma 4 12B vs GPT-6.1 Sol Comparison Table
Evals updated November 12, 2026Pricing updated November 13, 2026
| Property | Gemma 4 12B | GPT-6.1 Sol |
|---|---|---|
| Organization | OpenAI | |
| Category | open | closed |
| Modality | multimodal | multimodal |
| Release Date | Jun 2026 | Sep 2026 |
| Context Window | — | 1.1M |
| Parameters | 12B | undisclosed |
| License | Apache 2.0 | Proprietary |
| Commercial use | free for commercial use | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | No published price | $2.00 |
| Output $/1M | No published price | $10.00 |
| Vision Tasks | ||
| Captioning | Supported | Demo |
| OCR | Supported | Demo |
| Visual Question Answering | Supported | Demo |
| Chart Question Answering | Not listed | Supported |
| Classification | Not listed | Demo |
| Document Question Answering | Not listed | Supported |
| Image Tagging | Not listed | Supported |
| Multi-Label Classification | Not listed | Supported |
| Object Detection | Not listed | Demo |
| Promptable Concept Segmentation | Not listed | Demo |
Both support 1 more task | ||
| Model Features | ||
| Foundation Vision | Not listed | Supported |
| LLMs with Vision Capabilities | Not listed | Supported |
Both support 1 more feature | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | Not evaluated | 81.7% |
| Avg cost / sample | – | $0.0068 |
| Avg speed / sample | – | 12.82s |
| By task | ||
| Object Detection (low) | – | 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) | – | 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) | – | 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) | – | 75.8% |
| by category |
| |
| OCR (high) | – | 75.8% |
| by category |
| |
| Reasoning (low) | – | 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 12B vs GPT-6.1 Sol: Overview
Gemma 4 12B is an open-weight multimodal model from Google in the Gemma 4 family. It is intended for text and image understanding tasks such as visual question answering, OCR, captioning, and document understanding, with a smaller parameter footprint than the larger Gemma 4 variants.
This entry is connected to Roboflow Playground vision evals for comparison. No runnable Playground workflow is configured yet, so the model page is used for discovery and benchmark context rather than direct hosted inference.
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.
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.