OpenAI
Smallest GPT-5.4 variant for cost-sensitive automation, classification, and lightweight assistant workloads.
OpenRouter
Price record: 2026-10-03. Source: openrouter.
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43.1
Quality Score
1199
Arena ELO
Undisclosed
Parameters
256K
Context
This measures the amount of verifiable public evidence we have, not how capable the model is. A missing field means it has not been verified yet, not that its value is zero.
17 of 22 public signals
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Mar 2026
Released
4/5 signals
4/4 signals
5/5 signals
1/4 signals
3/4 signals
Parameters
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Training compute
Not reported
Dataset scale
Not reported
Base model
Not reported
Source-reported access: API access · Likely confidence
Gaps we are still tracking
Launches
1
Benchmarks
4
API
1
Safety
2
Research
3
General
4
Recent launch, pricing, benchmark, and API signals linked to this model or its provider.
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
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
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 Sign in with ChatGPT Apps powered by your user's ChatGP
View sourceChatGPT 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 Sign in with ChatGPT Apps powered by your user's ChatGP
View sourceChatGPT 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 Sign in with ChatGPT Apps powered by your user's ChatGP
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Codex Security Cloud is getting a major upgrade, with access to cyber-capable models through Daybreak Blue included by default. It scans entire GitHub repos, continuously reviews new commits, investigates and deduplicates findings, and prepares fixes for review – even when your https://t.co/up1hpkiCAK
We’ve shared details on how AI agents in our research environment sent training and evaluation data to third-party services when they shouldn’t have. Most of that data did not come from users. We have discovered 53 cases where images that people had uploaded were posted to
After the Hugging Face incident, we committed to conducting a much broader review of actions taken by our models during training and evaluation and to being transparent about our findings. This is an extensive review that is ongoing. The vast majority of actions we’ve reviewed
Skills can improve the performance of Large Language Model (LLM) agents by providing task-specific procedural guidance, while skill optimization further improves their effectiveness through iterative refinement. However, existing skill optimization methods typically represent skills as unstructured natural-language instructions, creating two key challenges: 1) Unstructured skills often lack explicit workflow-level guidance and contain substantial redundancy, making them difficult for LLMs to execute; 2) the vast search space of unconstrained natural-language skills makes skill optimization ineffective. To address these challenges, we propose representing skills as graph-structured natural-language artifacts. In graph-structured skills, each node represents an execution step together with its operational guidance, while directed edges encode context-dependent transitions between steps. Compared to unstructured skills, graph-structured skills can provide clear workflow-level guidance. Moreover, the proposed graph-structured skill can also facilitate skill optimization. Building on this structured representation, we introduce GraphSkillEvo, a population-based evolutionary optimization framework with mutation and crossover operators for graph-structured skills. By maintaining multiple candidate skills and combining effective components, GraphSkillEvo enables broader and more comprehensive exploration of the structured skill space than purely LLM-based iterative self-refinement. Extensive experiments across five agent benchmarks demonstrate that GraphSkillEvo consistently outperforms the strong skill optimization baseline SkillOpt, improving average accuracy by 4.01% on GPT-5.4-nano and 1.76% on GPT-5.4. Our code is available at https://github.com/ruisun7/GraphSkillEvo.
While skill optimization for autonomous agents has gained traction, existing methods rely on complex pipelines. This leaves a fundamental question unaddressed: What constitutes a minimal viable pipeline for skill optimization, where every component is justified by theory or empirical necessity? We formalize skill optimization via Zeroth-Order (ZO) optimization, mapping classical counterparts (central difference, trust regions) to recent literature. Noting that unlike blind numerical perturbations in classical ZO, skill trajectories serve as interpretable debugging feedback. Grounded in Claude Code philosophy and PAC learning, we establish three principles for convergence and generalization: file-system-based trajectory exploration, consensus attribute mining, and independent validation gating. Eliminating redundancies, we propose SkillOpt-Lite. It accelerates convergence and outperforms full SkillOpt: improving LiveMath by +8.8 points on GPT-5.5 and +25.4 points on GPT-5.4-nano, allowing the nano model to surpass standard GPT-5.4 optimized by SkillOpt. Finally, we integrate our framework into production coding agents like VSCode Copilot, enabling developers to evolve agent skills via one line of vibe. Because our framework treats all agent components simply as standard editable code, this minimal pipeline naturally generalizes to full harness optimization (HarnessOpt). On SpreadsheetBench, HarnessOpt enables GPT-5.4-nano to achieve 0.7758 accuracy, outperforming the larger GPT-5.5 running standard pipelines (0.7620). Code is available at https://github.com/EvolvingLMMs-Lab/SkillOpt-Lite.
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 Sign in with ChatGPT Apps powered by your user's ChatGP
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 Sign in with ChatGPT Apps powered by your user's ChatGP
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 Sign in with ChatGPT Apps powered by your user's ChatGP