GLM-OCR vs GPT-5.6 Sol

GLM-OCR is a multimodal OCR model for complex document understanding, built on the GLM-V encoder-decoder architecture by Zhipu AI. The model combines a 0.4B-parameter CogViT visual encoder pre-trained on large-scale image-text data, a lightweight cross-modal connector with efficient token downsampling, and a 0.5B-parameter GLM language decoder, totaling 0.9B parameters. To address the inefficiency of standard autoregressive decoding in deterministic OCR tasks, GLM-OCR introduces a Multi-Token Prediction (MTP) mechanism that predicts multiple tokens per step, significantly improving decoding throughput while keeping memory overhead low through shared parameters. Training proceeds through four stages: visual encoder pretraining with MIM, CLIP, and distillation objectives; vision-language pretraining on document parsing, grounding, and VQA data; supervised fine-tuning on curated OCR datasets covering text, formula, table, and key information extraction; and full-task reinforcement learning to improve accuracy and structural consistency.

At the system level, GLM-OCR adopts a two-stage pipeline in which PP-DocLayout-V3 first performs layout analysis, followed by parallel region-level recognition. This design enables robust handling of diverse document layouts including tables, formulas, and multi-column text. The model supports document parsing and targeted recognition tasks, producing structured outputs in Markdown, JSON, and LaTeX formats across more than 100 languages. On the OmniDocBench V1.5 benchmark, GLM-OCR scores 94.62, and achieves 94.0 on OCRBench and 96.5 on UniMERNet for formula recognition.

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.