GPT-6 Astra Is Live: OpenAI's New Flagship Open for Calls

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Key Points

• A new-generation flagship: OpenAI released gpt-6-astra on September 3, 2026, calling it “the most intelligent and aligned model in the world”, built for computer use, software engineering, science, and long-horizon agent work. APIYI has it live in the default / svip official-relay groups and the half-price Codex_Reverse group
• Priced item-for-item with OpenAI: $10 in / $50 out per 1M tokens, $1 cached reads, $12.50 cache writes. That is 2.5x the current promotional price of gpt-5.6-sol ($4 / $20) and identical to Claude Fable 5.1 on input and output
• Specs stepped up across the board: 1,050,000-token context, 128,000-token max output, knowledge cutoff April 30, 2026. Reasoning effort gains two new levels, xhigh and max, for five in total
• Big jumps on agent benchmarks: Terminal-Bench 4.0 from 37.3% to 57.9%, ScreenSpot-Pro from 76.9% to 92.7%, FrontierMath Tier 4 from 83.0% to 97.6%. OpenAI says it uses roughly 70% fewer tokens than Sol on equivalent tasks
• Cyber capability rated “Critical”: this is the first OpenAI model to cross the cybersecurity Critical threshold in its Preparedness Framework. The public API refuses vulnerability discovery and exploit-writing tasks. Regular development work is unaffected

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Background

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Deep Dive

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Benchmarks (retrieved September 5, 2026)

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Core Capabilities

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Specifications

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Practical Use

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Recommended Scenarios

• Long-horizon agent tasks: multi-step browser / desktop automation, cross-system data transfer and reconciliation. This is where Astra improves most over the previous generation
• Complex software engineering: large code migrations (OpenAI reports 68% migration accuracy, 10 points ahead of second place), cross-repo refactors, SRE incident triage
• Science and professional analysis: near-perfect FrontierMath Tier 4, well suited to mathematical derivation, scientific scripting, and professional document drafting
• Very long context: 1.05M tokens fits a mid-sized codebase or several hundred pages of documents in one request

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Code Example

from openai import OpenAI

client = OpenAI(api_key="YOUR_API_KEY", base_url="https://api.apiyi.com/v1")

response = client.chat.completions.create(
    model="gpt-6-astra",
    reasoning_effort="high",   # low / medium / high / xhigh / max
    messages=[
        {"role": "user", "content": "Migrate this repo from Python 3.9 to 3.13 and list every file that needs changes, with reasons"}
    ]
)
print(response.choices[0].message.content)
print(response.usage)

curl https://api.apiyi.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-6-astra",
    "reasoning_effort": "high",
    "messages": [{"role": "user", "content": "In one sentence, how does GPT-6 Astra differ from GPT-5.6 Sol?"}]
  }'

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Best Practices

• Pick the reasoning level per task: low / medium for deterministic steps, xhigh / max for planning and debugging that needs extended thinking. max grows reasoning tokens sharply and output bills at $50, so measure on a small sample before scaling up
• Lean on the cache: cached reads at $1 are one tenth of the standard input price. In agent loops, keep stable prefixes such as system prompts, tool definitions, and codebase context at the front of the conversation so every turn hits the cache
• Watch input size: as on OpenAI, a request whose input exceeds 272K tokens moves to a higher billing tier for the whole request (input and cache double). Unless you genuinely need the whole repo in one shot, keep day-to-day context under 272K
• Migrating from 5.6 Sol is a model-name change: request structure and parameters stay in the OpenAI-compatible format; swap the model field and enable xhigh / max as needed

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Pricing and Availability

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Pricing

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Compared with Peers

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Long-Context Tiered Billing

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Stack the Recharge Promotions

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Summary and Recommendations