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Gemma 3 4B vs GPT-5.6 Sol

Compare Gemma 3 4B and GPT-5.6 Sol side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.

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GoogleGemma 3 4B
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OpenAIGPT-5.6 Sol
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Gemma 3 4B vs GPT-5.6 Sol: Overview

Gemma 3 4B

Gemma 3 4B, released on March 12, 2025, is the mid-sized member of Google DeepMind’s open-weight Gemma 3 family. With about 4 billion parameters, it is multimodal—supporting text and image inputs and generating text outputs. Like the larger Gemma 3 models, it features a 128,000-token input context window with an output capacity of ~8,192 tokens, enabling it to handle long documents and mixed text–image reasoning tasks.

The 4B variant is designed as a balance between efficiency and capability: it offers multilingual support across 140+ languages, strong summarization and reasoning performance, and compatibility with moderate hardware. Inference can run with ~6.4 GB VRAM in BF16, or significantly less in quantized 8-bit (~4.4 GB) or 4-bit (~3.4 GB) modes, making it accessible to developers outside large-scale infrastructure. While it lags behind the 12B and 27B versions on the most complex reasoning and multimodal benchmarks, its lower compute footprint makes it ideal for research, prototyping, and practical deployment where efficiency matters.

GPT-5.6 Sol

GPT-5.6 Sol is the flagship model in OpenAI's GPT-5.6 family, which also includes Terra (a balanced everyday-work tier) and Luna (a fast, cost-efficient tier). Sol is designed for demanding reasoning, long-horizon agentic workflows, software engineering, computer use, scientific research, and cybersecurity tasks. It introduces two new capability modes: a "max" reasoning effort setting that allocates additional compute time for difficult problems, and an "ultra" mode that coordinates multiple subagents in parallel to accelerate complex, multi-step work. The model supports native multimodal input, allowing it to process screenshots, diagrams, charts, documents, and photographs alongside text. A reported context window of approximately 1.5 million tokens enables processing of large codebases, lengthy research documents, and extended agentic sessions.

GPT-5.6 Sol was announced on June 26, 2026, initially in a limited preview for trusted partners, and reached general availability on July 9, 2026. On the Agents' Last Exam benchmark, which evaluates long-running professional workflows across 55 fields, Sol scores 53.6. On Terminal-Bench 2.1, which tests command-line agentic coding workflows, Sol Ultra achieves 91.9%. The model also demonstrates gains in life sciences evaluations, including long-horizon genomics and quantitative biology analyses. OpenAI paired the release with its most extensive safety evaluation to date, combining human red teaming with large-scale automated testing, and classified Sol as High capability in both cybersecurity and biological risk under its Preparedness Framework, though it does not cross the Critical threshold in either category.

Gemma 3 4B vs GPT-5.6 Sol Comparison Table

PropertyGemma 3 4BGPT-5.6 Sol
OrganizationGoogleOpenAI
Categoryopenclosed
Modalitymultimodalmultimodal
Release DateMar 2025Jul 2026
Context Window128K1.5M
Parameters4B
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.050$5.00
Output $/1M$0.100$30.00
Vision Tasks
CaptioningDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Chart Question Answering
classificationDemo
Document Question Answering
object-detectionDemo
Model Features
Multimodal Vision
LLMs with Vision Capabilities
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
37.31%
Avg Response Time16.80s
Defect Detection
60%(9/15)
Document Understanding
55.6%(5/9)
Object Counting
0%(0/10)
Object Understanding
42.9%(6/14)
Spatial Understanding
26.3%(5/19)
OCR
Overall Score
64.19%
Avg Response Time0.92s
Median input tokensincl. image tokens300
Median output tokens12
Est. cost / taskon this benchmark<$0.0001
Focused Scene OCR
63.6%(63/99)
Handwritten Math
10%(1/10)
License Plate Recognition
86.7%(26/30)
Text Recognition
73.3%(22/30)
VQA & Extraction
58.3%(35/60)