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GPT-5.6 Sol vs GPT-5.6 Terra

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

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Models in this comparison

GPT-5.6 Sol vs GPT-5.6 Terra on Vision Evals

GPT-5.6 Sol scores higher on all six Vision Evals tasks.

The widest gap is Object Detection, where GPT-5.6 Sol leads 68.2% to 60.7%.

Overall, GPT-5.6 Sol averages 76.9% (#9 of 25) against 72.4% (#12 of 25) for GPT-5.6 Terra.

GPT-5.6 Terra is both cheaper ($0.0044 vs $0.025 per sample) and faster (7.2s vs 11.7s per sample).

GPT-5.6 SolGPT-5.6 Terra

GPT-5.6 Sol vs GPT-5.6 Terra Comparison Table

Evals updated August 6, 2026Pricing updated August 11, 2026

PropertyGPT-5.6 SolGPT-5.6 Terra
OrganizationOpenAIOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateJul 2026Jul 2026
Context Window1.5M1.1M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$5.00$1.00
Output $/1M$30.00$6.00
Vision Tasks
CaptioningDemoDemo
Chart Question Answering
ClassificationDemoDemo
Document Question Answering
Image Tagging
Multi-Label Classification
Object DetectionDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort
Overall
76.9%
72.4%
Avg cost / sample$0.025$0.0044
Avg speed / sample11.72s7.15s
By task
Object Detection
68.2%
$0.045
60.7%
$0.0070
Counting
73.0%
$0.013
67.6%
$0.0030
Identification
81.3%
$0.0070
78.1%
$0.0020
OCR
90.7%
$0.032
88.8%
$0.0065
Data Extraction
82.5%
$0.0085
79.4%
$0.0018
Reasoning (low)
65.6%
$0.011
59.6%
$0.0025
Reasoning (high)
72.2%
$0.016
64.2%
$0.0033

GPT-5.6 Sol vs GPT-5.6 Terra: Overview

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.

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.

Frequently Asked Questions

On Roboflow's Vision Evals, GPT-5.6 Sol performed better. It scores higher on all six vision tasks and averages 76.9% (#9 of 25) against 72.4% (#12 of 25) for GPT-5.6 Terra. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.

Yes. On the Vision Evals Object Detection benchmark, GPT-5.6 Sol leads with 68.2% against 60.7%. This is the widest gap between the two models across the benchmark's tasks.

GPT-5.6 Terra is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0044 per sample against $0.025. GPT-5.6 Sol is priced at $5.00 per 1M input tokens and $30.00 per 1M output; GPT-5.6 Terra is priced at $1.00 per 1M input tokens and $6.00 per 1M output. Actual costs depend on your image sizes, prompts, and output length.

GPT-5.6 Terra is faster. Across Roboflow's Vision Evals it averaged 7.2s per inference against 11.7s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.

Yes. The comparison demo on this page runs both models on the same image side by side for OCR and image captioning in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.