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GPT-5.4 Mini vs GPT-5.6 Terra

Compare GPT-5.4 Mini and GPT-5.6 Terra side-by-side. See how these vision models stack up in Open Prompt, Object Detection, Classification, Image Captioning, and OCR.

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Models in this comparison

GPT-5.4 Mini vs GPT-5.6 Terra on Vision Evals

GPT-5.6 Terra scores higher on 4 of the six Vision Evals tasks.

The widest gap is Object Detection, where GPT-5.6 Terra leads 60.6% to 15.8%.

Overall, GPT-5.4 Mini averages 64.7% (#45 of 60) against 73.8% (#24 of 60) for GPT-5.6 Terra.

GPT-5.4 Mini is both cheaper ($0.0030 vs $0.0088 per sample) and faster (5.3s vs 7.7s per sample).

GPT-5.4 MiniGPT-5.6 Terra

GPT-5.4 Mini vs GPT-5.6 Terra Comparison Table

Evals updated October 7, 2026Pricing updated October 7, 2026

PropertyGPT-5.4 MiniGPT-5.6 Terra
OrganizationOpenAIOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateMar 2026Jul 2026
Context Window400K1.1M
ParametersUnknownUnknown
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.750$2.00
Output $/1M$4.50$12.00
Vision Tasks
CaptioningDemoDemo
Chart Question AnsweringSupportedSupported
ClassificationDemoDemo
Document Question AnsweringSupportedSupported
Image TaggingSupportedSupported
Multi-Label ClassificationSupportedSupported
Object DetectionDemoDemo
OCRDemoDemo
Vision LanguageSupportedSupported
Visual Question AnsweringDemoDemo
Model Features
Foundation VisionSupportedSupported
LLMs with Vision CapabilitiesSupportedSupported
Multimodal VisionSupportedSupported
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort
Overall
64.7%
73.8%
Avg cost / sample$0.0030$0.0088
Avg speed / sample5.25s7.74s
By task
Object Detection (low)
15.8%
±0.4, Mean of 3 runs, range 15.3 to 16.1
$0.0044
60.6%
±0.2, Mean of 3 runs, range 60.3 to 60.7
$0.014
Object Detection (high)
16.6%
±0.8, Mean of 3 runs, range 15.8 to 17.4
$0.030
61.3%
±0.4, Mean of 3 runs, range 60.8 to 61.6
$0.026
Counting (low)
58.6%
±2.0, Mean of 3 runs, range 56.8 to 60.8
$0.0019
65.8%
±2.0, Mean of 3 runs, range 63.5 to 67.6
$0.0056
Counting (high)
64.9%
±2.0, Mean of 3 runs, range 63.5 to 67.6
$0.0073
62.6%
±2.7, Mean of 3 runs, range 59.5 to 64.9
$0.0076
Identification (low)
83.3%
±1.6, Mean of 3 runs, range 81.3 to 84.4
$0.0013
86.5%
±1.6, Mean of 3 runs, range 84.4 to 87.5
$0.0034
Identification (high)
82.3%
±3.1, Mean of 3 runs, range 78.1 to 84.4
$0.0055
86.5%
±3.1, Mean of 3 runs, range 84.4 to 90.6
$0.0038
OCR (low)
89.5%
±1.3, Mean of 3 runs, range 88.1 to 90.6
$0.0042
89.4%
±0.8, Mean of 3 runs, range 88.8 to 90.3
$0.012
OCR (high)
89.0%
±1.8, Mean of 3 runs, range 87.7 to 91.2
$0.030
89.4%
±0.6, Mean of 3 runs, range 88.8 to 90.1
$0.023
Data Extraction (low)
84.2%
±2.1, Mean of 3 runs, range 82.5 to 86.6
$0.0014
79.7%
±0.5, Mean of 3 runs, range 79.4 to 80.4
$0.0038
Data Extraction (high)
82.1%
±2.1, Mean of 3 runs, range 80.4 to 84.5
$0.0036
80.4%
±1.0, Mean of 3 runs, range 79.4 to 81.4
$0.0043
Reasoning (low)
57.0%
±3.3, Mean of 3 runs, range 54.3 to 60.9
$0.0022
60.9%
±2.0, Mean of 3 runs, range 59.6 to 63.6
$0.0051
Reasoning (high)
64.0%
±1.3, Mean of 3 runs, range 62.9 to 65.6
$0.0096
65.3%
±1.0, Mean of 3 runs, range 64.2 to 66.2
$0.0067

GPT-5.4 Mini vs GPT-5.6 Terra: Overview

GPT-5.4 Mini

GPT-5.4 mini is a fast, cost-efficient model developed by OpenAI and released on March 17, 2026, optimized for high-throughput workloads and subagent orchestration. It supports text and image inputs within a 400,000-token context window, making it ideal for processing extensive visual datasets and large codebases in a single request. Designed for low-latency production environments, the model integrates with key API features including function calling, web search, and tool-based computer use, allowing it to assist in automated workflows that require navigating digital interfaces.

Compared to the previous GPT-5 mini, this version runs more than twice as fast while approaching the performance levels of the flagship GPT-5.4 on reasoning and coding benchmarks. While the larger GPT-5.4 introduces native, state-of-the-art computer-use capabilities, GPT-5.4 mini provides a scalable alternative for interpreting screenshots and reasoning over dense UI layouts. For vision tasks on Playground, it excels at extracting structured information from visual documents and assisting in agentic tasks that involve real-time interpretation of software interfaces alongside text.

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 Terra performed better. It scores higher on 4 of the six vision tasks and averages 73.8% (#24 of 60) against 64.7% (#45 of 60) for GPT-5.4 Mini. 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-5.6 Terra leads with 60.6% against 15.8%. This is the widest gap between the two models across the benchmark's tasks.

GPT-5.4 Mini is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0030 per sample against $0.0088. GPT-5.4 Mini is priced at $0.75 per 1M input tokens and $4.50 per 1M output; GPT-5.6 Terra is priced at $2.00 per 1M input tokens and $12.00 per 1M output. Actual costs depend on your image sizes, prompts, and output length.

GPT-5.4 Mini is faster. Across Roboflow's Vision Evals it averaged 5.3s per inference against 7.7s. 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 open prompts and object detection in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.