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OpenAI GPT-5 System Card
Mixed citation behavior. Most common role is background (51%).
358
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Background
51% of classified citations
abstract
This is the system card published alongside the OpenAI GPT-5 launch, August 2025. GPT-5 is a unified system with a smart and fast model that answers most questions, a deeper reasoning model for harder problems, and a real-time router that quickly decides which model to use based on conversation type, complexity, tool needs, and explicit intent (for example, if you say 'think hard about this' in the prompt). The router is continuously trained on real signals, including when users switch models, preference rates for responses, and measured correctness, improving over time. Once usage limits are reached, a mini version of each model handles remaining queries. This system card focuses primarily on gpt-5-thinking and gpt-5-main, while evaluations for other models are available in the appendix. The GPT-5 system not only outperforms previous models on benchmarks and answers questions more quickly, but -- more importantly -- is more useful for real-world queries. We've made significant advances in reducing hallucinations, improving instruction following, and minimizing sycophancy, and have leveled up GPT-5's performance in three of ChatGPT's most common uses: writing, coding, and health. All of the GPT-5 models additionally feature safe-completions, our latest approach to safety training to prevent disallowed content. Similarly to ChatGPT agent, we have decided to treat gpt-5-thinking as High capability in the Biological and Chemical domain under our Preparedness Framework, activating the associated safeguards. While we do not have definitive evidence that this model could meaningfully help a novice to create severe biological harm -- our defined threshold for High capability -- we have chosen to take a precautionary approach.
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- abstract This is the system card published alongside the OpenAI GPT-5 launch, August 2025. GPT-5 is a unified system with a smart and fast model that answers most questions, a deeper reasoning model for harder problems, and a real-time router that quickly decides which model to use based on conversation type, complexity, tool needs, and explicit intent (for example, if you say 'think hard about this' in the prompt). The router is continuously trained on real signals, including when users switch models, preference rates for responses, and measured correctness, improving over time. Once usage limits ar
co-cited works
representative citing papers
Introduces the TVR active viewpoint-matching task and TVRBench indoor simulation benchmark, where foundation models start at low single-digit success rates but reach 51.4% after visual-action SFT and multi-turn GRPO post-training.
AMNESIA is a benchmark suite of 70,560 medical QA pairs that evaluates unlearning methods and shows that patient-level unlearning erodes disease-shared knowledge.
FlowCompile performs compile-time design space exploration on structured LLM workflows to produce reusable high-quality configuration sets that outperform routing baselines with up to 6.4x speedup.
M³Att poisons medical multimodal RAG by pairing covert textual misinformation with query-agnostic visual perturbations that increase retrieval of the bad content, causing LLMs to generate clinically plausible but incorrect responses.
Soohak is a 439-problem mathematician-curated benchmark where frontier LLMs reach at most 30.4% on research math challenges and no model exceeds 50% on refusal for ill-posed problems.
TraceAV-Bench is the first benchmark for multi-hop trajectory reasoning over long audio-visual videos, showing top models reach only 51-68% accuracy with substantial room for improvement.
EVE enables verifiable self-evolution of MLLMs by using a Challenger-Solver architecture to generate dynamic executable visual transformations that produce VQA problems with absolute execution-verified ground truth.
VLMs hallucinate by prioritizing contradictory on-screen text over visual content, addressed via the VisualTextTrap benchmark with 6,057 human-validated samples and the VTHM-MoE dual-encoder framework using dimension-specific experts and adaptive routing.
OccuBench is a new benchmark for AI agents on real-world occupational tasks via LLM-driven simulators, showing no model dominates all industries, implicit faults are hardest, and larger models with more reasoning perform better.
MMRareBench provides 1,756 QA pairs and 7,958 images from PMC rare-disease cases to evaluate 23 MLLMs, revealing low treatment-planning scores and medical models underperforming general models on multi-image tasks due to capacity dilution.
DragOn provides a new drag-grounding benchmark and training dataset for GUI agents, with evaluations suggesting potential improvements on computer-use tasks.
OmniOPD replaces token-level logit matching in on-policy distillation with Monte Carlo chunk-level semantic verification and a peak-entropy scheduler.
MM-Snowball benchmark diagnoses hallucination snowballing in multi-turn MLLM dialogues; CAVR mitigates it via dual visual rectification at representation and logit levels.
Moral Trolley Arena shows frontier LLMs produce composite moral preferences that are compressed rather than additive functions of calibrated component act strengths across Moral Foundations Theory.
Frontier VLMs overconfidently answer spatial questions under occlusion (~30% accuracy) and perspective ambiguity (<10% accuracy) instead of abstaining, and often fail to select helpful additional views.
VLMs exhibit consistent vertical-distance entanglement in embeddings from perspective bias in natural images, producing accuracy gaps that a new synthetic benchmark SpatialTunnel exposes as model-intrinsic.
EvoRepair is the first experience-based self-evolving agent framework for automated vulnerability repair, reporting 90.46% overall success on PATCHEVAL and SEC-bench benchmarks.
CardioLens is a leakage-resistant CMR testbed of 473k slices and 13k QA pairs showing current MLLMs exhibit a large clinical reality gap with category-collapse failures on real workflows.
LLMs struggle to associate epistemic markers with stable internal confidence levels across distributions, even under model-centric interpretations, while maintaining somewhat consistent marker rankings.
Introduces MUTATE benchmark for path-level and action-level divergent thinking in LLM agents and ReDNA method that decouples divergent generation from convergent selection to improve performance.
SD-MIA is a black-box membership inference attack that detects pre-training data in diffusion models via cross-modal perturbations on images and textual instructions.
Introduces EHR-ReasonCon benchmark with expert annotations and EHR-Inspector LLM framework for reasoning-intensive verification of consistency between clinical notes and structured tables in EHRs.
JobBench is a new benchmark with 130 occupational tasks where the best of 36 tested AI models achieves only 45.9% success.
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Only Say What You Know: Calibration-Aware Generation for Long-Form Factuality
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Heterogeneous Scientific Foundation Model Collaboration
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UniEP: Unified Expert-Parallel MoE MegaKernel for LLM Training
UniEP fuses MoE communication and computation into unified MegaKernels with deterministic token ordering, delivering 1.03x-1.38x speedups over prior work while preserving training accuracy.
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Do Emotions Influence Moral Judgment in Large Language Models?
Inducing emotions shifts LLM moral judgments in a valence-dependent manner that reverses decisions in up to 20% of cases and does not appear in humans.
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On the Reliability of Computer Use Agents
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Logic-Based Verification of Task Allocation for LLM-Enabled Multi-Agent Manufacturing Systems
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Beyond Distribution Sharpening: The Importance of Task Rewards
Task-reward reinforcement learning yields robust gains on math benchmarks for models like Llama-3.2-3B while distribution sharpening alone delivers only limited and unstable improvements.
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Bridging the Gap between User Intent and LLM: A Requirement Alignment Approach for Code Generation
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FD-NL2SQL: Feedback-Driven Clinical NL2SQL that Improves with Use
FD-NL2SQL is a feedback-driven clinical NL2SQL system that decomposes questions, retrieves exemplars via embeddings, synthesizes SQL, and expands its example bank from user edits plus logic-based mutations to improve without new annotations.
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Do BERT Embeddings Encode Narrative Dimensions? A Token-Level Probing Analysis of Time, Space, Causality, and Character in Fiction
BERT embeddings encode narrative dimensions of time, space, causality, and character at the token level, as a linear probe achieves 94% accuracy versus 47% on variance-matched random embeddings, though unsupervised clusters do not align with these categories.
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HOG-Layout: Hierarchical 3D Scene Generation, Optimization and Editing via Vision-Language Models
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Beyond Imperfect Alternatives with Rulemapping: A Neuro-Symbolic Case Study on Online Hate Speech
Rulemapping uses expert symbolic scaffolds to constrain LLMs, raising precision on §130(1) German hate-speech classification from 0.34-0.49 to 0.80-0.86 while preserving recall of 0.82-0.89.
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OpenVLThinkerV2: A Generalist Multimodal Reasoning Model for Multi-domain Visual Tasks
OpenVLThinkerV2 applies a new Gaussian GRPO training objective with response and entropy shaping to outperform prior open-source and proprietary models on 18 visual reasoning benchmarks.
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CoEnv: Driving Embodied Multi-Agent Collaboration via Compositional Environment
CoEnv introduces a compositional environment that integrates real and simulated spaces for multi-agent robotic collaboration, using real-to-sim reconstruction, VLM action synthesis, and validated sim-to-real transfer to achieve high success rates on multi-arm manipulation tasks.
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ECHO: Elastic Speculative Decoding with Sparse Gating for High-Concurrency Scenarios
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Towards Explainable Industrial Anomaly Detection via Knowledge-Guided Latent Reasoning
Reason-IAD improves explainable industrial anomaly detection by combining retrieval-augmented category knowledge with entropy-guided latent reasoning and dynamic visual patch injection in MLLMs.
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MAVEN: Improving Generalization in Agentic Tool Calling
MAVEN is a modular verification scaffold that lifts an open 120b model's tool-calling accuracy from 48% to 71% on MAVEN-Bench without retraining.
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Label Over Logic? How Source Cues Bias Human Fallacy Judgments More Than LLMs
Humans exhibit greater source-label bias in logical fallacy judgments than LLMs, which maintain more consistent evaluations regardless of source cues.
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PowLU: An Activation Function for Stable Pre-Training of LLMs
PowLU replaces SwiGLU with a rational-power activation to reduce outlier amplification and numerical instability during large-scale LLM pre-training while matching performance.
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A Simple Plug-in for Improving Eviction-Based KV Cache Compression
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Tracing the ongoing emergence of human-like reasoning in Large Language Models
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Automated Grading of Handwritten Mathematics Using Vision-Capable LLMs
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It Takes Two: Complementary Self-Distillation for Contextual Integrity in LLMs
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An LLM-Based System for Argument Mining
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Qwen-Scope: Turning Sparse Features into Development Tools for Large Language Models
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Human-LLM Dialogue Improves Diagnostic Accuracy in Emergency Care
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Risk Reporting for Developers' Internal AI Model Use
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Sound Agentic Science Requires Adversarial Experiments
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From Handwriting to Structured Data: Benchmarking AI Digitisation of Handwritten Forms
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Are vision-language models ready to zero-shot replace supervised classification models in agriculture?
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CLaC at SemEval-2026 Task 6: Response Clarity Detection in Political Discourse
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