OpenAI
Fast, cost-efficient reasoning model in the o-series. Balances strong STEM performance with lower inference cost and adjustable reasoning effort.
61.6
Quality Score
1363
Arena ELO
Undisclosed
Parameters
200K
Context
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Apr 2025
Released
Launches
3
Benchmarks
21
Research
2
General
4
Recent launch, pricing, benchmark, and API signals linked to this model or its provider.
People are generating over 1.5 billion images a week in ChatGPT. Researcher @kenjihata joins Product lead @adele__li and host @AndrewMayne to explore the new use cases and trends emerging since the launch of Images 2.0. https://t.co/INhLS7TDri
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Quality: 88/100 | Speed: 55/100 | Price: $2.2/M tokens | MMLU: 89% | HumanEval: 93.4%
Arena-Hard-Auto official Gemini-2.5 judged score 74.6 with CI -1.8/1.6
Introducing OpenAI Guaranteed Capacity: a new offering that enables customers to guarantee long-term access to OpenAI compute. We’ve made long-term investments in infrastructure, partnerships, and capacity planning to help customers scale reliably. Now, Guaranteed Capacity
View sourceArena-Hard-Auto official Gemini-2.5 judged score 74.6 with CI -1.8/1.6
View sourceArena-Hard-Auto official Gemini-2.5 judged score 79.1 with CI -1.4/1.2
View sourceToday, we share a breakthrough on the planar unit distance problem, a famous open question first posed by Paul Erdős in 1946. For nearly 80 years, mathematicians believed the best possible solutions looked roughly like square grids. An OpenAI model has now disproved that https://t.co/j2g3Ze0zEG
People are generating over 1.5 billion images a week in ChatGPT. Researcher @kenjihata joins Product lead @adele__li and host @AndrewMayne to explore the new use cases and trends emerging since the launch of Images 2.0. https://t.co/INhLS7TDri
Introducing OpenAI Guaranteed Capacity: a new offering that enables customers to guarantee long-term access to OpenAI compute. We’ve made long-term investments in infrastructure, partnerships, and capacity planning to help customers scale reliably. Now, Guaranteed Capacity
Quality: 88/100 | Speed: 55/100 | Price: $2.2/M tokens | MMLU: 89% | HumanEval: 93.4%
Quality: 88/100 | Speed: 55/100 | Price: $2.2/M tokens | MMLU: 89% | HumanEval: 93.4%
Quality: 88/100 | Speed: 55/100 | Price: $2.2/M tokens | MMLU: 89% | HumanEval: 93.4%
Quality: 88/100 | Speed: 55/100 | Price: $2.2/M tokens | MMLU: 89% | HumanEval: 93.4%
Quality: 88/100 | Speed: 55/100 | Price: $2.2/M tokens | MMLU: 89% | HumanEval: 93.4%
Quality: 88/100 | Speed: 55/100 | Price: $2.2/M tokens | MMLU: 89% | HumanEval: 93.4%
Quality: 88/100 | Speed: 55/100 | Price: $2.2/M tokens | MMLU: 89% | HumanEval: 93.4%
Quality: 88/100 | Speed: 55/100 | Price: $2.2/M tokens | MMLU: 89% | HumanEval: 93.4%
Quality: 88/100 | Speed: 55/100 | Price: $2.2/M tokens | MMLU: 89% | HumanEval: 93.4%
Quality: 88/100 | Speed: 55/100 | Price: $2.2/M tokens | MMLU: 89% | HumanEval: 93.4%
Quality: 88/100 | Speed: 55/100 | Price: $2.2/M tokens | MMLU: 89% | HumanEval: 93.4%
Quality: 88/100 | Speed: 55/100 | Price: $2.2/M tokens | MMLU: 89% | HumanEval: 93.4%
Quality: 88/100 | Speed: 55/100 | Price: $2.2/M tokens | MMLU: 89% | HumanEval: 93.4%
Quality: 88/100 | Speed: 55/100 | Price: $2.2/M tokens | MMLU: 89% | HumanEval: 93.4%
Quality: 88/100 | Speed: 55/100 | Price: $2.2/M tokens | MMLU: 89% | HumanEval: 93.4%
Large language model (LLM) agents increasingly operate over long and recurring external contexts, like document corpora and code repositories. Across invocations, existing approaches preserve either the agent's trajectory, passive access to raw material, or task-level strategies. None of them preserves what we argue is most needed for repeated same-context workloads: reusable orientation knowledge (e.g., what the context contains, how it is organized, and which entities, constants, and schemas have historically been useful) about the recurring context itself. We introduce PEEK, a system that caches and maintains this orientation knowledge as a context map: a small, constant-sized artifact in the agent's prompt that gives it a persistent peek into the external context. The map is maintained by a programmable cache policy with three modules: a Distiller that extracts transferable knowledge from inference-time signals, a Cartographer that translates it into structured edits, and a priority-based Evictor that enforces a fixed token budget. On long-context reasoning and information aggregation, PEEK improves over strong baselines by 6.3-34.0% while using 93-145 fewer iterations and incurring 1.7-5.8x lower cost than the state-of-the-art prompt-learning framework, ACE. On context learning, PEEK improves solving rate and rubric accuracy by 6.0-14.0% and 7.8-12.1%, respectively, at 1.4x lower cost than ACE. These gains generalize across LMs and agent architectures, including OpenAI Codex, a production-grade coding agent. Together, these results show that a context map helps long-context LLM agents interact with recurring external contexts more accurately and efficiently.
GPU kernel optimization is increasingly critical for efficient deep learning systems, but writing high-performance kernels still requires substantial low-level expertise. Recent AI coding agents can iteratively read code, invoke compilers and profilers, and refine implementations, yet existing kernel benchmarks evaluate single LLM calls rather than full agent workflows, and none include both kernel-to-kernel optimization and unseen-configuration generalization testing. We present AgentKernelArena, an open-source benchmark for measuring AI coding agents on GPU kernel optimization. The benchmark contains 196 tasks spanning HIP-to-HIP optimization, Triton-to-Triton optimization, and PyTorch-to-HIP translation, and evaluates complete agent workflows in isolated workspaces using gated compilation, correctness, and performance checks, centralized scoring and an unseen-configuration generalization protocol that tests whether optimizations transfer to input configurations the agent never observed. Across production agents including Cursor Agent, Claude Code, and Codex Agent, we find near-perfect compilation and high correctness rates on most task categories, with the strongest configurations achieving mean speedups of up to 6.89x on PyTorch-to-HIP, 6.69x on HIP-to-HIP, and 2.13x on Triton-to-Triton tasks. Our unseen-configuration evaluation shows that HIP-to-HIP and Triton-to-Triton optimizations largely transfer to unseen input shapes, while PyTorch-to-HIP exhibits substantial correctness drops, indicating that agents generating kernels from scratch frequently hardcode shape-specific assumptions. AgentKernelArena is designed as a modular, extensible framework for rigorous evaluation of agentic GPU kernel optimization across agents, tasks, and hardware targets.
Arena-Hard-Auto official Gemini-2.5 judged score 79.1 with CI -1.4/1.2
SWE-Bench Verified resolved rate 45.0
LiveCodeBench pass@1 77.4 across 1055 tasks
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 Table of contents Introduction New features Safety Availability & pricing Detailed benchmarks August 7, 2025 Product Introducing GPT‑5 for developers The best