OpenAI
Smallest GPT-5.4 variant for cost-sensitive automation, classification, and lightweight assistant workloads.
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35.8
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
3
API
1
Safety
2
Research
2
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
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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
Retrieval for search agents is still inherited from non-agentic information retrieval: a retriever ranks the corpus and the agent reads a small set of returned documents. Recent direct corpus interaction (DCI) work shows that agents can instead interact with the raw corpus through shell tools such as grep and file reads. But unbounded interaction does not scale: every broad shell command is a scan over the whole corpus, and latency degrades sharply as the corpus grows. We argue that the role of retrieval for agentic search is not just to select documents that fit in the LLM context window, but to construct an interaction space: a bounded subset of the corpus the agent can explore with associated tools. Two design consequences follow. The space needs a boundary supplied by retrieval, and the objects within it should be processed for interaction. As a proof of concept, we propose RISE (Retrieving Interaction SpacE): we use BM25 to construct the interaction space; meanwhile, its documents are processed during indexing for shell-style navigation. On BrowseComp-Plus, RISE matches the pure-shell DCI baseline at 78% accuracy with gpt-5.4-mini at roughly one quarter of the per-query cost. At 1M documents, RISE-BM25 reaches 81% on gpt-5.4-mini, whereas DCI on gpt-5.4-nano degrades to 60% with 33 of 100 wall-clock failures.
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