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Gemma 4 26B A4B vs GPT-5.6 Terra

Compare Gemma 4 26B A4B and GPT-5.6 Terra side-by-side. See how these vision models stack up in Image Captioning, OCR, Open Prompt, Object Detection, and Classification.

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GoogleGemma 4 26B A4B
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OpenAIGPT-5.6 Terra
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Models in this comparison

Gemma 4 26B A4B vs GPT-5.6 Terra: Overview

Gemma 4 26B A4B

Gemma 4 26B A4B is the Mixture-of-Experts variant in Google's Gemma 4 family, with 25.2B total parameters but only 3.8B active per token. Built from the same Gemini 3 research as the 31B dense sibling and released as open weights under the Apache 2.0 license, it supports a 256K token context window with text and image input and configurable thinking mode. The "A4B" in the name refers to its approximately 4B active parameters. The MoE design makes it significantly faster at inference than the dense 31B, running nearly as fast as a 4B-parameter model while delivering roughly 97% of the dense model's quality.

For vision tasks, the 26B A4B shares the same multimodal capabilities as the 31B image understanding with variable aspect ratios and resolutions, and structured bounding box output for UI element detection. The tradeoff versus the 31B dense model is a small quality reduction in exchange for much faster inference and lower hardware requirements, fitting in 18GB of VRAM at 4-bit quantization. It ranked #6 among open models on the Arena AI text leaderboard at launch.

GPT-5.6 Terra

GPT-5.6 Terra is the mid-tier reasoning model in OpenAI's GPT-5.6 family, which also includes the flagship Sol and the lightweight Luna. Introduced in a limited preview on June 26, 2026, and made broadly available on July 9, 2026, Terra accepts text and image input and produces text output, supporting vision, function calling, tool use, and agentic workflows. It is designed as a balanced option for everyday professional and production workloads — including coding assistance, document analysis, customer support, and multi-step agent tasks — where both output quality and cost efficiency matter. OpenAI positions Terra as delivering performance competitive with GPT-5.5 at approximately half the price, with a context window of around 1,050,000 tokens. On Terminal-Bench 2.1, Terra scores 84.3%, matching Claude Fable 5 on that benchmark. Under OpenAI's Preparedness Framework, Terra is rated High for cybersecurity and biological capabilities, meaning it demonstrates meaningful capability in those domains without reaching the Critical threshold.

GPT-5.6 introduces a new naming convention in which the generation number (5.6) is paired with a durable capability tier name (Sol, Terra, or Luna), allowing each tier to advance on its own schedule. Terra carries the API identifier gpt-5.6-terra and supports the same reasoning effort controls available across the family, including adjustable reasoning depth. The model includes prompt caching with explicit cache breakpoints and a 30-minute minimum cache life, with cache writes billed at 1.25x the uncached input rate and cache reads receiving a 90% discount. GPT-5.6 Terra is a proprietary, closed-weights model served through the OpenAI API, Codex, and ChatGPT.

Gemma 4 26B A4B vs GPT-5.6 Terra Comparison Table

PropertyGemma 4 26B A4BGPT-5.6 Terra
OrganizationGoogleOpenAI
Categoryopenclosed
Modalitymultimodalmultimodal
Release DateApr 2026Jul 2026
Context Window256K1.1M
Parameters25.2B
LicenseApache 2.0Proprietary
Pricing per 1M tokens
Input $/1M$0.060$2.50
Output $/1M$0.330$15.00
Vision Tasks
CaptioningDemoDemo
classificationDemoDemo
Object DetectionDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Document Question Answering
Model Features
Multimodal Vision
LLMs with Vision Capabilities
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
68.66%
Avg Response Time30.23s
Median input tokensincl. image tokens294
Median output tokens214
Est. cost / taskon this benchmark$0.0001
Defect Detection
80%(12/15)
Document Understanding
88.9%(8/9)
Object Counting
10%(1/10)
Object Understanding
85.7%(12/14)
Spatial Understanding
68.4%(13/19)
OCR
Overall Score
83.84%
Avg Response Time12.05s
Median input tokensincl. image tokens290
Median output tokens42
Est. cost / taskon this benchmark<$0.0001
Focused Scene OCR
85.9%(85/99)
Handwritten Math
50%(5/10)
License Plate Recognition
93.3%(28/30)
Text Recognition
80%(24/30)
VQA & Extraction
83.3%(50/60)

Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology