GPT-6 Astra
OpenAI's flagship September 2026 frontier model with a 1.05M context window, MoE architecture, and SOTA reasoning across coding, formal mathematics, and long-horizon agentic automation.
Empirical Evaluation Results
Architectural Profile & Capabilities
GPT-6 Astra represents OpenAI's flagship frontier foundation model deployed in September 2026. Designed for demanding end-to-end autonomous agentic work, scientific discovery, and complex systems engineering, Astra supersedes the GPT-5.6 family (Sol/Terra) by establishing new state-of-the-art records across automated programming, terminal tool orchestration, and formal mathematics. Astra incorporates a massive 1,050,000-token context window with multimodal vision and image ingestion capabilities, paired with a sparse Mixture-of-Experts (MoE) backbone engineered for test-time verification. While prior iterations like GPT-5.6 Sol prioritized extreme generation throughput via specialized hardware pipelines, Astra optimizes for token efficiency and verification accuracy. On Artificial Analysis Coding Agent benchmarks, Astra achieves an industry-leading score of 67.0 while expending approximately one-third the token volume of previous architectures at maximum effort. In empirical benchmark evaluations, GPT-6 Astra saturates formal mathematical reasoning, recording 97.6% (up to 98.0%) on FrontierMath Tier 4 and 96.0% on GPQA Diamond. Its agentic capabilities are demonstrated by 72.6% on OSWorld 2.0 (operating 47% faster per task than Sol) and 57.9% on Terminal-Bench 4.0, outperforming Claude Opus 5 (52.3%) and Grok 4.6 (20.3%). In binary analysis and reverse engineering, it reaches 88.0% single-attempt accuracy on SRE-Bench and 100% on ExploitBench, crossing OpenAI's internal critical cybersecurity capability boundary while maintaining strict guardrail containment (0.0% boundary evasion on ExploitGym). However, on Humanity's Last Exam (HLE w/ tools), Astra records 57.2%, trailing Anthropic's Claude Opus 5 (63.6%), and its ARC-AGI-3 benchmark shows stark sensitivity between standardized harnesses (62.7%) and adapter-augmented runs (99.9%).
Sparse Mixture-of-Experts (MoE) multimodal autoregressive transformer with deep test-time compute routing and integrated native diffusion/vision latent projection. Utilizes multi-head latent attention (MLA) / grouped-query attention (GQA) variants optimized for a 1,050,000 token context window, alongside an internal hierarchical reasoning token scratchpad that dynamically allocates verification budget across agentic and symbolic sub-goals.
Recommended Workloads & Primary Use Cases
- •**Pricing cliff at 272K:** standard pricing is $10.00/$50.00 per MTok, but requests exceeding 272K input tokens reprice the *entire request* at 2× input ($20.00) and 1.5× output ($75.00) — strict context management under 272K is vital
- •**Always-on reasoning:** reasoning effort cannot be turned off (`none` removed; levels are `low`, `medium`, `high`, `xhigh`, `max`) and reasoning tokens consume the 128K output budget at $50/M
- •**Monitorability:** recurrent depth processes computation in latent representations, making internal reasoning less monitorable than transparent chain-of-thought models
- •**Open-world synthesis:** trails Claude Opus 5 on open-world knowledge synthesis (HLE 57.2% vs 63.6%)
- •**Guardrails:** strict alignment blocks weaponized exploit generation and automated privilege escalation; cyber heuristics may pause low-level system calls
- •**Over-provisioning:** costly for routine syntax, single-file scripts, or basic summaries where Gemini 3.8 Flash delivers comparable results at >90% lower cost.
- •Extended context pricing applies above 272K tokens: $22/M in, $82.5/M out.
- •Harness Variance on Abstract Reasoning: Dramatic score divergence between OpenAI's customized Provider Adapter harness (99.9%) and standardized zero-shot evaluation (62.7%) on ARC-AGI-3 underscores dependence on proprietary scaffolding.
- •Elevated Latency under Deep Test-Time Compute: Output throughput throttles to deliberate speeds (~33-66 tps) when multi-turn verification loops and extended reasoning chains are active, leading to substantial Time-To-First-Token (TTFT) delays.
- •High Inference and Token Expenditure: At $10/1M input and $50/1M output, enterprise workloads operating across the 1.05M context window can generate significant compute costs if agent loop termination criteria are unconstrained.
- •Critical Dual-Use Cyber Safeguards: Because Astra saturates ExploitBench at 100%, OpenAI enforces aggressive runtime API monitoring and defensive guardrails that may trigger false-positive refusals on legitimate penetration testing workflows.
Calls to GPT-6 Astra are routed through strict Zero Data Retention inference channels. Prompts and outputs are never stored, indexed, or monitored by OpenAI.
More Models from OpenAI
Compare other engines available in this laboratory.