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GPT-5.6 Terra vs GPT-6 Sol

Compare GPT-5.6 Terra and GPT-6 Sol side-by-side. See how these vision models stack up in Classification, Open Prompt, Object Detection, OCR, and Image Captioning.

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OpenAIGPT-5.6 Terra
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OpenAIGPT-6 Sol
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Models in this comparison

OpenAI

GPT-5.6 Terra vs GPT-6 Sol on Vision Evals

GPT-6 Sol scores higher on all five Vision Evals tasks.

The widest gap is Object Detection, where GPT-6 Sol leads 74.7% to 60.6%.

Overall, GPT-5.6 Terra averages 66.0% (#26 of 63) against 76.9% (#12 of 63) for GPT-6 Sol.

GPT-6 Sol is cheaper ($0.0074 vs $0.010 per sample), while GPT-5.6 Terra is faster (9.5s vs 10.2s per sample).

GPT-5.6 TerraGPT-6 Sol

GPT-5.6 Terra vs GPT-6 Sol Comparison Table

Evals updated October 13, 2026Pricing updated October 16, 2026

PropertyGPT-5.6 TerraGPT-6 Sol
OrganizationOpenAIOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateJul 2026Sep 2026
Context Window1.1M1.1M
ParametersUnknownundisclosed
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$2.00$2.00
Output $/1M$12.00$10.00
Vision Tasks
CaptioningDemoDemo
Chart Question AnsweringSupportedSupported
ClassificationDemoDemo
Document Question AnsweringSupportedSupported
Image TaggingSupportedSupported
Multi-Label ClassificationSupportedSupported
Object DetectionDemoDemo
OCRDemoDemo
Vision LanguageSupportedSupported
Visual Question AnsweringDemoDemo
Promptable Concept SegmentationNot listedDemo
Model Features
Foundation VisionSupportedSupported
LLMs with Vision CapabilitiesSupportedSupported
Multimodal VisionSupportedSupported
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
66.0%
76.9%
Avg cost / sample$0.010$0.0074
Avg speed / sample9.52s10.20s
By task
Object Detection (low)
60.6%
±0.2, Mean of 3 runs, range 60.3 to 60.7
$0.014
74.7%
±0.7, Mean of 3 runs, range 74.1 to 75.5
$0.011
Object Detection (high)
61.3%
±0.4, Mean of 3 runs, range 60.8 to 61.6
$0.026
76.4%
±1.2, Mean of 3 runs, range 75.0 to 77.4
$0.022
Counting (low)
65.8%
±2.0, Mean of 3 runs, range 63.5 to 67.6
$0.0056
76.6%
±2.0, Mean of 3 runs, range 74.3 to 78.4
$0.0039
Counting (high)
62.6%
±2.7, Mean of 3 runs, range 59.5 to 64.9
$0.0076
77.5%
±1.3, Mean of 3 runs, range 75.7 to 78.4
$0.0070
Identification (low)
86.5%
±1.6, Mean of 3 runs, range 84.4 to 87.5
$0.0034
93.8%
±0.0, Mean of 3 runs, range 93.8 to 93.8
$0.0027
Identification (high)
86.5%
±3.1, Mean of 3 runs, range 84.4 to 90.6
$0.0038
92.7%
±1.6, Mean of 3 runs, range 90.6 to 93.8
$0.0036
OCR (low)
56.3%
$0.011
66.7%
$0.0077
by category
Single value
36.1%
Transcription
79.9%
Structured JSON
76.8%
Text localization
42.3%
Single value
45.6%
Transcription
88.3%
Structured JSON
83.4%
Text localization
65.7%
OCR (high)
56.9%
$0.021
68.8%
$0.018
by category
Single value
37.4%
Transcription
80.2%
Structured JSON
77.3%
Text localization
41.9%
Single value
47.4%
Transcription
91.0%
Structured JSON
85.3%
Text localization
68.7%
Reasoning (low)
60.9%
±2.0, Mean of 3 runs, range 59.6 to 63.6
$0.0051
72.6%
±3.6, Mean of 3 runs, range 69.5 to 76.8
$0.0037
Reasoning (high)
65.3%
±1.0, Mean of 3 runs, range 64.2 to 66.2
$0.0067
77.7%
±1.0, Mean of 3 runs, range 76.8 to 78.8
$0.0061

GPT-5.6 Terra vs GPT-6 Sol: 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.

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

On Roboflow's Vision Evals, GPT-6 Sol performed better. It scores higher on all five vision tasks and averages 76.9% (#12 of 63) against 66.0% (#26 of 63) for GPT-5.6 Terra. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.

No. On the Vision Evals Object Detection benchmark at low effort, GPT-6 Sol leads with 74.7% against 60.6%. This is the widest gap between the two models across the benchmark's tasks.

GPT-6 Sol is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0074 per sample against $0.010. GPT-5.6 Terra is priced at $2.00 per 1M input tokens and $12.00 per 1M output; GPT-6 Sol is priced at $2.00 per 1M input tokens and $10.00 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 9.5s per inference against 10.2s. 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 image classification and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.