GPT-6 Astra
OpenAI frontier flagship with Critical-tier autonomous cybersecurity, extreme token efficiency, and 72.6% on OSWorld 2.0.
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
Les axes du radar sont approximés depuis les scores par catégorie lorsque les mesures directes manquent — voir Méthodologie.
Points forts & compromis
Là où il gagne
- 72.6% OSWorld 2.0 — best-in-class computer use
- Flagship agentic + reasoning scores across the board
- 131K output tokens — rewrites entire files in one pass
- Strong tool-use reliability on terminal workflows
Points de vigilance
- $10/$50 premium pricing tier
- Higher effort levels multiply latency and token spend
- Closed weights — no self-hosting
Cas d'usage clés
Démarrage rapide développeur & API
OPENAI_API_KEY — obtenir une clé APIcurl https://api.openai.com/v1/responses \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-H "content-type: application/json" \
-d '{
"model": "gpt-6-astra",
"input": "Refactor this function for O(n) time and explain the change."
}'from openai import OpenAI
client = OpenAI() # reads OPENAI_API_KEY
resp = client.responses.create(
model="gpt-6-astra",
input="Refactor this function for O(n) time and explain the change.",
)
print(resp.output_text)const res = await fetch("https://api.openai.com/v1/responses", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.OPENAI_API_KEY}`,
"content-type": "application/json",
},
body: JSON.stringify({ model: "gpt-6-astra", input: "Refactor this function for O(n) time and explain the change." }),
});
console.log((await res.json()).output_text);Ressources & liens officiels
Comparer hôtes et latence
Débit, TTFT et tarifs de cache chez Groq, Cerebras, Together, DeepInfra, Fireworks, Bedrock et Azure.
Scores par Source
Scores par Catégorie
Résultats de Benchmarks
Specs en direct & architecture
- Reasoning effort levels: low / medium / high / xhigh — one weight set, four budgets
- Critical-tier autonomous cybersecurity designation
- 1.1M context with a 131K max output window
- Extreme token efficiency on long agent runs
Facture mensuelle estimée
Citer et intégrer
Loading badge markdown...@misc{llmpodium2026gpt6astra,
title = {Benchmark Analysis and Intelligence Evaluation of GPT-6 Astra},
author = {LLMPodium Research},
year = {2026},
url = {https://llmpodium.com/models/gpt-6-astra}
}Intégrer le badge de score
Ajoutez un badge Podium Score en direct à votre README GitHub ou Hugging Face — il renvoie vers le classement en direct.
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