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GLM 5.3 Flash vs GPT-5.6 Terra

Compare GLM 5.3 Flash and GPT-5.6 Terra side-by-side. See how these vision models stack up in Image Captioning, OCR, Classification, Object Detection, and Open Prompt.

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Z.aiGLM 5.3 Flash
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OpenAIGPT-5.6 Terra
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Models in this comparison

GLM 5.3 Flash vs GPT-5.6 Terra on Vision Evals

GPT-5.6 Terra scores higher on all five Vision Evals tasks.

The widest gap is Object Detection, where GPT-5.6 Terra leads 60.6% to 33.1%.

Overall, GLM 5.3 Flash averages 55.8% (#42 of 61) against 66.0% (#24 of 61) for GPT-5.6 Terra.

GLM 5.3 Flash is both cheaper ($0.0006 vs $0.010 per sample) and faster (9.3s vs 9.5s per sample).

GLM 5.3 FlashGPT-5.6 Terra

GLM 5.3 Flash vs GPT-5.6 Terra Comparison Table

Evals updated October 8, 2026Pricing updated October 10, 2026

PropertyGLM 5.3 FlashGPT-5.6 Terra
OrganizationZ.aiOpenAI
Categoryopenclosed
Modalitymultimodalmultimodal
Release DateAug 2026Jul 2026
Context Window1.0M1.1M
Parameters320B total, 18B activeUnknown
LicenseMITProprietary
Pricing per 1M tokens
Input $/1M$0.150$2.00
Output $/1M$0.500$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 5 vision tasks, pooled at low effort
Overall
55.8%
66.0%
Avg cost / sample$0.0006$0.010
Avg speed / sample9.29s9.52s
By task
Object Detection (low)
33.1%
$0.0008
60.6%
±0.2, Mean of 3 runs, range 60.3 to 60.7
$0.014
Object Detection (high)–
61.3%
±0.4, Mean of 3 runs, range 60.8 to 61.6
$0.026
Counting (low)
55.4%
$0.0002
65.8%
±2.0, Mean of 3 runs, range 63.5 to 67.6
$0.0056
Counting (high)–
62.6%
±2.7, Mean of 3 runs, range 59.5 to 64.9
$0.0076
Identification (low)
84.4%
$0.0002
86.5%
±1.6, Mean of 3 runs, range 84.4 to 87.5
$0.0034
Identification (high)–
86.5%
±3.1, Mean of 3 runs, range 84.4 to 90.6
$0.0038
OCR (low)
55.4%
$0.0007
56.3%
$0.011
by category
Single value
47.4%
Transcription
81.5%
Structured JSON
72.0%
Text localization
18.6%
Single value
36.1%
Transcription
79.9%
Structured JSON
76.8%
Text localization
42.3%
OCR (high)
55.3%
$0.0011
56.9%
$0.021
by category
Single value
50.9%
Transcription
72.2%
Structured JSON
72.0%
Text localization
14.5%
Single value
37.4%
Transcription
80.2%
Structured JSON
77.3%
Text localization
41.9%
Reasoning (low)
51.0%
$0.0002
60.9%
±2.0, Mean of 3 runs, range 59.6 to 63.6
$0.0051
Reasoning (high)
59.6%
$0.0003
65.3%
±1.0, Mean of 3 runs, range 64.2 to 66.2
$0.0067

GLM 5.3 Flash vs GPT-5.6 Terra: Overview

GLM 5.3 Flash

GLM-5.3-Flash is the first natively multimodal model in Z.ai's GLM-5 series, a mixture-of-experts transformer with roughly 320 billion total parameters and 18 billion activated per token. It routes each token through 8 of 288 experts across 45 language layers that interleave KDA linear attention with sparse multi-head latent attention, and pairs them with a 24-layer vision encoder that handles image and video input. The checkpoint declares a maximum context length of 1,048,576 tokens, ships in native FP8, and includes a multi-token prediction draft layer for speculative decoding. Z.ai reports that the hybrid attention design reduces attention computation by 3.01x and KV cache size by 4.44x relative to GLM-5.3.

The model starts from a newly trained base built on a 30 trillion token multimodal pre-training corpus and adopts Manifold-Constrained Hyper-Connections to improve scaling efficiency. Vision is integrated into the coding and agent loop, so the model can inspect interfaces, rendered output, and images while operating across code, browsers, and graphical user interfaces. Z.ai reports scores of 84.3 on Terminal-Bench 2.1, 63.4 on DeepSWE 1.1, 55.3 on Humanity's Last Exam with tools, and 48.8 on AutomationBench, and the model exposes low, high, and max thinking modes.

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 all five vision tasks and averages 66.0% (#24 of 61) against 55.8% (#42 of 61) for GLM 5.3 Flash. 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 33.1%. This is the widest gap between the two models across the benchmark's tasks.

GLM 5.3 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0006 per sample against $0.010. GLM 5.3 Flash is priced at $0.15 per 1M input tokens and $0.50 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.

GLM 5.3 Flash is faster. Across Roboflow's Vision Evals it averaged 9.3s per inference against 9.5s. 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 captioning and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.