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GPT-5.6 Terra vs Qwen3.6 Plus

Compare GPT-5.6 Terra and Qwen3.6 Plus 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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QwenQwen3.6 Plus
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GPT-5.6 Terra vs Qwen3.6 Plus: 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.

Qwen3.6 Plus

Qwen3.6 Plus is a flagship model in Alibaba’s Qwen Plus series, designed for agentic workflows, coding, and multi-step reasoning. It supports a 1 million token context window and up to 65,536 output tokens, with built-in reasoning capabilities. The model is available as a hosted, proprietary API through Alibaba Cloud.

Compared to Qwen3.5, it improves reliability in multi-step execution and frontend code generation, with stronger performance on agentic coding tasks. It also supports document and image understanding, though its vision capabilities are more limited than dedicated Qwen-VL models. Qwen3.6 Plus is part of a broader Qwen ecosystem that includes both closed-source APIs and open-weight models.

GPT-5.6 Terra vs Qwen3.6 Plus Comparison Table

PropertyGPT-5.6 TerraQwen3.6 Plus
OrganizationOpenAIQwen
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateJul 2026Apr 2026
Context Window1.1M1.0M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$2.50$0.325
Output $/1M$15.00$1.95
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
68.66%
Avg Response Time34.17s
Median input tokensincl. image tokens1.2K
Median output tokens47
Est. cost / taskon this benchmark$0.0005
Defect Detection
86.7%(13/15)
Document Understanding
77.8%(7/9)
Object Counting
20%(2/10)
Object Understanding
78.6%(11/14)
Spatial Understanding
68.4%(13/19)
OCR
Overall Score
58.52%
Avg Response Time5.49s
Median input tokensincl. image tokens124
Median output tokens18
Est. cost / taskon this benchmark$0.0001
Focused Scene OCR
76.8%(76/99)
Handwritten Math
80%(8/10)
License Plate Recognition
13.3%(4/30)
Text Recognition
50%(15/30)
VQA & Extraction
51.7%(31/60)

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