Roboflow

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

PropertyGemma 4 12BGPT-6.1 Sol
OrganizationGoogleOpenAI
Categoryopenclosed
Modalitymultimodalmultimodal
Release DateJun 2026Sep 2026
Context Window—1.1M
Parameters12Bundisclosed
LicenseApache 2.0Proprietary
Commercial usefree for commercial usecommercial use per vendor terms
Pricing per 1M tokens
Input $/1MNo published price$2.00
Output $/1MNo published price$10.00
Vision Tasks
CaptioningSupportedDemo
OCRSupportedDemo
Visual Question AnsweringSupportedDemo
Chart Question AnsweringNot listedSupported
ClassificationNot listedDemo
Document Question AnsweringNot listedSupported
Image TaggingNot listedSupported
Multi-Label ClassificationNot listedSupported
Object DetectionNot listedDemo
Promptable Concept SegmentationNot listedDemo
Both support 1 more task
Model Features
Foundation VisionNot listedSupported
LLMs with Vision CapabilitiesNot listedSupported
Both support 1 more feature
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
OverallNot 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
$0.010
Object Detection (high)–
81.6%
±0.4, Mean of 3 runs, range 81.1 to 82.0
$0.022
Counting (low)–
78.8%
±3.4, Mean of 3 runs, range 75.7 to 82.4
$0.0037
Counting (high)–
80.2%
±3.4, Mean of 3 runs, range 77.0 to 83.8
$0.0057
Identification (low)–
89.6%
±1.6, Mean of 3 runs, range 87.5 to 90.6
$0.0025
Identification (high)–
88.5%
±1.6, Mean of 3 runs, range 87.5 to 90.6
$0.0030
OCR (low)–
75.8%
$0.0069
by category
Single value
58.3%
Transcription
95.7%
Structured JSON
89.5%
Text localization
74.4%
OCR (high)–
75.8%
$0.016
by category
Single value
56.5%
Transcription
96.0%
Structured JSON
90.4%
Text localization
76.7%
Reasoning (low)–
83.7%
±1.3, Mean of 3 runs, range 82.1 to 84.8
$0.0033
Reasoning (high)–
88.7%
±2.0, Mean of 3 runs, range 87.4 to 91.4
$0.0042

Gemma 4 12B vs GPT-6.1 Sol: Overview

Gemma 4 12B

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

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.