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Gemma 4 12B vs GPT-6 Sol

Compare Gemma 4 12B and GPT-6 Sol side-by-side.

Compare Gemma 4 12B vs GPT-6 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.

Models in this comparison

OpenAI

Gemma 4 12B vs GPT-6 Sol Comparison Table

Evals updated September 22, 2026Pricing updated September 22, 2026

PropertyGemma 4 12BGPT-6 Sol
OrganizationGoogleOpenAI
Categoryopenclosed
Modalitymultimodalmultimodal
Release DateJun 2026Sep 2026
Context Window1.1M
Parameters12Bundisclosed
LicenseApache 2.0Proprietary
Vision Tasks
Captioning
OCR
Vision Language
Visual Question Answering
Chart Question Answering
Classification
Document Question Answering
Image Tagging
Multi-Label Classification
Object Detection
Model Features
Multimodal Vision
Foundation Vision
LLMs with Vision Capabilities
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort
OverallNot evaluated
80.7%
Avg cost / sample$0.0065
Avg speed / sample8.15s
By task
Object Detection (low)
73.6%
±0.6, Mean of 3 runs, range 72.9 to 74.2
$0.011
Object Detection (high)
75.2%
±0.9, Mean of 3 runs, range 74.1 to 75.9
$0.022
Counting (low)
74.8%
±2.7, Mean of 3 runs, range 71.6 to 77.0
$0.0039
Counting (high)
76.1%
±3.4, Mean of 3 runs, range 71.6 to 78.4
$0.0071
Identification (low)
91.7%
±3.1, Mean of 3 runs, range 87.5 to 93.8
$0.0027
Identification (high)
92.7%
±1.6, Mean of 3 runs, range 90.6 to 93.8
$0.0036
OCR (low)
91.7%
±0.4, Mean of 3 runs, range 91.3 to 92.1
$0.0070
OCR (high)
91.9%
±0.3, Mean of 3 runs, range 91.6 to 92.2
$0.019
Data Extraction (low)
80.4%
±0.0, Mean of 3 runs, range 80.4 to 80.4
$0.0031
Data Extraction (high)
82.5%
±1.0, Mean of 3 runs, range 81.4 to 83.5
$0.0045
Reasoning (low)
72.2%
±1.7, Mean of 3 runs, range 70.9 to 74.2
$0.0039
Reasoning (high)
77.9%
±2.6, Mean of 3 runs, range 75.5 to 80.8
$0.0069

Gemma 4 12B vs GPT-6 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 Sol

GPT-6 Sol is a proprietary multimodal reasoning model from OpenAI, released on September 22, 2026 alongside GPT-6 Luna as an efficiency-oriented tier of the GPT-6 family that began with GPT-6 Astra. OpenAI states that Sol and Luna are trained with methods similar to those used for Astra, carrying the same work on professional tasks, factuality, coding, computer use, and alignment into models that run faster. Sol accepts text and image input and returns text output, and OpenAI documents a context window of roughly one million tokens together with a knowledge cutoff of April 20, 2026.

The model targets complex coding and agentic workflows and exposes a configurable reasoning effort setting with levels of none, low, medium, high, xhigh, and max, which trades latency and token consumption against answer quality. OpenAI reports results including 33.2% on AutomationBench at xhigh effort and 56.4% on Agents' Last Exam at max effort, while its reported DeepSWE and OSWorld 2.0 figures of 68.8% and 64.4% fall below those of the earlier GPT-5.6 Sol. Its vision behavior covers image understanding tasks such as visual question answering, captioning, document and chart interpretation, and text recognition.

Frequently Asked Questions

Gemma 4 12B has not yet been evaluated on Roboflow's current Vision Evals, so this comparison shows specs, licensing, and pricing rather than benchmark scores.

Gemma 4 12B is released under Apache 2.0, while GPT-6 Sol uses Proprietary. Licensing often matters more than raw accuracy for commercial deployments, so check the terms against how you plan to ship.