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54.6
Quality Score
1240
Arena ELO
Undisclosed
Parameters
256K
Context
Evidence profile
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Introducing GPT-5.1 for developers | OpenAI Skip to main content Research Products Business Developers Company Foundation (opens in a new window) Log in Try ChatGPT (opens in a new window) Research Products Business Developers Company Foundation (opens in a new window) Try ChatGPT (opens in a new window) Login OpenAI November 13, 2025 Product Introducing GPT‑5.1 for developers Loading… Share Efficient reasoning across tasks Efficient reasoning across tasks Adaptive reasoning
Introducing GPT-5 for developers | OpenAI Skip to main content Research Products Business Developers Company Foundation (opens in a new window) Log in Try ChatGPT (opens in a new window) Research Products Business Developers Company Foundation (opens in a new window) Try ChatGPT (opens in a new window) Login OpenAI August 7, 2025 Product Introducing GPT‑5 for developers The best model for coding and agentic tasks. Loading… Share Introduction Introduction Coding Frontend engin
Models | OpenAI API Home API Docs Guides and concepts for the OpenAI API API reference Endpoints, parameters, and responses Codex Docs Guides, concepts, and product docs for Codex Use cases Example workflows and tasks teams hand to Codex ChatGPT Apps SDK Build apps to extend ChatGPT Commerce Build commerce flows in ChatGPT Ads Publish and measure ads in ChatGPT Resources Showcase Demo apps to get inspired Blog Learnings and experiences from developers Cookbook Notebook exampl
GPT-Rosalind surfaced a new lead for drug discovery: could restoring cholesterol transport protect neurons from degeneration? DCTN1 is a gene linked to Perry syndrome, a rare neurodegenerative disorde
GPT-Rosalind surfaced a new lead for drug discovery: could restoring cholesterol transport protect neurons from degeneration? DCTN1 is a gene linked to Perry syndrome, a rare neurodegenerative disorder. In mouse-brain data, disrupting it increased expression of genes involved in
Our latest model advances bring stronger capabilities for building and migrating software in low-resource programming languages such as COBOL, Fortran, and Visual Basic. These languages power software
Our latest model advances bring stronger capabilities for building and migrating software in low-resource programming languages such as COBOL, Fortran, and Visual Basic. These languages power software that banks, governments, and businesses depend on. Progress here means https://t.co/Mulo5P3iCO
HandsOn: A Large-Scale Simulation Benchmark for Generalizable Dexterous Manipulation
General-purpose manipulation requires reliable behavior across embodiments, objects, and task demands. We introduce HandsOn, a composable simulation platform and benchmark for cross-embodiment dexterous manipulation. It supports five robotic arms, six dexterous hands, and approximately 200 rigid, articulated, and deformable assets in randomized scenes. The benchmark pairs 18,000 curated human demonstrations with 66 human-verified tasks: Core-50 supports demonstration-based learning and execution tests, while Transfer-16 evaluates asset and relation transfer, skill composition, and deformable manipulation without target-task training demonstrations. Qualitative real-robot rollouts illustrate sim-to-real transfer. Across diffusion, vision-language-action, world-action, and prompted VLM-based methods, the best trained multi-task policy achieves 29.1% Core-50 success. Without benchmark finetuning, GPT-6 using move_eef for palm-and-finger control achieves 5.2% on Core-50 and 1.9% on Transfer-16 over 660 trials. Trained-policy stage diagnostics expose failures in contact establishment, action coordination, and maintaining achieved goals, with additional sequencing errors in composed tasks. HandsOn provides a shared basis for investigating manipulation generalization and the limits of reliable physical interaction.
GEditBench v2: A Human-Aligned Benchmark for General Image Editing
Recent advances in image editing have enabled models to handle complex instructions with impressive realism. However, existing evaluation frameworks lag behind: current benchmarks suffer from narrow task coverage, while standard metrics fail to adequately capture visual consistency, i.e., the preservation of identity, structure and semantic coherence between edited and original images. To address these limitations, we introduce GEditBench v2, a comprehensive benchmark with 1,200 real-world user queries spanning 23 tasks, including a dedicated open-set category for unconstrained, out-of-distribution editing instructions beyond predefined tasks. Furthermore, we propose PVC-Judge, an open-source pairwise assessment model for visual consistency, trained via two novel region-decoupled preference data synthesis pipelines. Besides, we construct VCReward-Bench using expert-annotated preference pairs to assess the alignment of PVC-Judge with human judgments on visual consistency evaluation. Experiments show that our PVC-Judge achieves state-of-the-art evaluation performance among open-source models and even surpasses GPT-5.1 on average. Finally, by benchmarking 16 frontier editing models, we show that GEditBench v2 enables more human-aligned evaluation, revealing critical limitations of current models, and providing a reliable foundation for advancing precise image editing.
AuthorMix: Modular Authorship Style Transfer via Layer-wise Adapter Mixing
The task of authorship style transfer involves rewriting text in the style of a target author while preserving the meaning of the original text. Existing style transfer methods train a single model on large corpora to model all target styles at once: this high-cost approach offers limited flexibility for target-specific adaptation, and often sacrifices meaning preservation for style transfer. In this paper, we propose AuthorMix: a lightweight, modular, and interpretable style transfer framework. We train individual, style-specific LoRA adapters on a small set of high-resource authors, allowing the rapid training of specialized adaptation models for each new target via learned, layer-wise adapter mixing, using only a handful of target style training examples. AuthorMix outperforms existing, SoTA style-transfer baselines -- as well as GPT-5.1 -- for low-resource targets, achieving the highest overall score and substantially improving meaning preservation.
ReflexiCoder: Teaching Large Language Models to Self-Reflect on Generated Code and Self-Correct It via Reinforcement Learning
While Large Language Models (LLMs) have revolutionized code generation, standard "System 1" approaches, generating solutions in a single forward pass, often hit a performance ceiling when faced with complex algorithmic tasks. Existing iterative refinement strategies attempt to bridge this gap at inference time, yet they predominantly rely on external oracles, execution feedback, or computationally expensive prompt-response cycles. In this work, we propose ReflexiCoder, a novel reinforcement learning (RL) framework that internalizes the structured reasoning trajectory, encompassing initial generation, bug and optimization aware reflection, and self-correction, directly into the model's weights. Unlike prior methods, ReflexiCoder shifts the paradigm from external-dependent refinement to an intrinsic, fully autonomous self-reflection and self-correction capabilities at inference time. We utilize an RL-zero training paradigm with granular reward functions to optimize the entire reflection-correction trajectory, teaching the model how to debug without reliance on ground-truth feedback or execution engines at inference time. Extensive experiments across seven benchmarks demonstrate that our ReflexiCoder-8B establishes a new state-of-the-art (SOTA) among leading open-source models in the 1.5B-14B range, achieving 94.51% (87.20%) on HumanEval (Plus), 81.80% (78.57%) on MBPP (Plus), 35.00% on BigCodeBench, 52.21% on LiveCodeBench, and 37.34% on CodeForces in a single-attempt setting, rivaling or surpassing proprietary models like GPT-5.1. Notably, our framework is significantly more token-efficient than base models, reducing inference-time compute overhead by approximately 40% through disciplined, high-speed reasoning and reflection patterns. Source code is available at https://github.com/juyongjiang/ReflexiCoder.
Introducing GPT-5.1 for developers | OpenAI Skip to main content Research Products Business Developers Company Foundation (opens in a new window) Log in Try ChatGPT (opens in a new window) Research Products Business Developers Company Foundation (opens in a new window) Try ChatGPT (opens in a new window) Login OpenAI November 13, 2025 Product Introducing GPT‑5.1 for developers Loading… Share Efficient reasoning across tasks Efficient reasoning across tasks Adaptive reasoning
Introducing GPT-5 for developers | OpenAI Skip to main content Research Products Business Developers Company Foundation (opens in a new window) Log in Try ChatGPT (opens in a new window) Research Products Business Developers Company Foundation (opens in a new window) Try ChatGPT (opens in a new window) Login OpenAI August 7, 2025 Product Introducing GPT‑5 for developers The best model for coding and agentic tasks. Loading… Share Introduction Introduction Coding Frontend engin
Models | OpenAI API Home API Docs Guides and concepts for the OpenAI API API reference Endpoints, parameters, and responses Codex Docs Guides, concepts, and product docs for Codex Use cases Example workflows and tasks teams hand to Codex ChatGPT Apps SDK Build apps to extend ChatGPT Commerce Build commerce flows in ChatGPT Ads Publish and measure ads in ChatGPT Resources Showcase Demo apps to get inspired Blog Learnings and experiences from developers Cookbook Notebook exampl
ChatGPT Start searching API Dashboard Try ChatGPT Home API Overview Get started with the OpenAI API Models Explore models and compare capabilities Agents Build persistent agents on hosted infrastructure Tools Connect models to tools and data Audio & voice Build speech and realtime voice experiences Production Deploy and scale your API integrations API reference Explore endpoints, parameters, and responses ChatGPT Plugins Extend ChatGPT and Codex Sign in with ChatGPT Apps
ChatGPT Start searching API Dashboard Try ChatGPT Home API Overview Get started with the OpenAI API Models Explore models and compare capabilities Agents Build persistent agents on hosted infrastructure Tools Connect models to tools and data Audio & voice Build speech and realtime voice experiences Production Deploy and scale your API integrations API reference Explore endpoints, parameters, and responses ChatGPT Plugins Extend ChatGPT and Codex Sign in with ChatGPT Apps
ChatGPT Start searching API Dashboard Try ChatGPT Home API Overview Get started with the OpenAI API Models Explore models and compare capabilities Agents Build persistent agents on hosted infrastructure Tools Connect models to tools and data Audio & voice Build speech and realtime voice experiences Production Deploy and scale your API integrations API reference Explore endpoints, parameters, and responses ChatGPT Plugins Extend ChatGPT and Codex Sign in with ChatGPT Apps
