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GPT-5.6 Terra vs Llama 4 Maverick

Compare GPT-5.6 Terra and Llama 4 Maverick side-by-side. See how these vision models stack up in Open Prompt, OCR, and Image Captioning.

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OpenAIGPT-5.6 Terra
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MetaLlama 4 Maverick
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GPT-5.6 Terra vs Llama 4 Maverick: Overview

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.

Llama 4 Maverick

Llama 4 Maverick, introduced on April 5, 2025, is one of the first models in Meta’s Llama 4 family, designed as a natively multimodal model supporting text + image inputs with text outputs. It employs a Mixture-of-Experts (MoE) architecture with 128 experts, activating ~17B parameters per token out of a pool of ~400B total parameters. This design improves scalability, efficiency, and reasoning capacity. Maverick has a 1M-token context window, enabling it to handle large documents, extended conversations, and multimodal reasoning. Its knowledge cutoff is August 2024.

The model is released under the Llama 4 Community License and comes in both base and instruction-tuned (“Instruct”) versions. Maverick is widely deployed via Hugging Face, Google Vertex AI, Amazon Bedrock, and Oracle Cloud, making it one of the most accessible large open-weight models. However, it outputs text only (no image/audio generation) and, while input capacity is huge, output limits are typically much smaller. The MoE design also raises hardware demands, as maintaining 128 experts requires significant compute resources, and Meta’s license introduces restrictions around commercial-scale use.

GPT-5.6 Terra vs Llama 4 Maverick Comparison Table

PropertyGPT-5.6 TerraLlama 4 Maverick
OrganizationOpenAIMeta
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateJul 2026Apr 2025
Context Window1.1M1.0M
Parameters400B
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$2.50$0.150
Output $/1M$15.00$0.600
Vision Tasks
CaptioningDemoDemo
Object DetectionDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
classificationDemo
Document Question Answering
Model Features
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
59.7%
Avg Response Time2.30s
Median input tokensincl. image tokens2.4K
Median output tokens7
Est. cost / taskon this benchmark$0.0004
Defect Detection
66.7%(10/15)
Document Understanding
66.7%(6/9)
Object Counting
30%(3/10)
Object Understanding
64.3%(9/14)
Spatial Understanding
63.2%(12/19)
OCR
Overall Score
78.6%
Avg Response Time0.87s
Median input tokensincl. image tokens472
Median output tokens10
Est. cost / taskon this benchmark$0.0001
Focused Scene OCR
76.8%(76/99)
Handwritten Math
60%(6/10)
License Plate Recognition
93.3%(28/30)
Text Recognition
83.3%(25/30)
VQA & Extraction
75%(45/60)

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