GPT-5.5 Launch: OpenAI's Newest Frontier Model with xhigh Reasoning Tier

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Key Highlights

• Newest frontier model: OpenAI’s flagship for the most complex professional work, just 6 weeks after GPT-5.4
• xhigh reasoning tier: reasoning.effort adds xhigh, bringing the lineup to none / low / medium (default) / high / xhigh
• Million-token context: 1,050,000 input window, 128,000 max output tokens
• Official direct-relay channel: APIYI relays OpenAI’s official endpoint — same model, same behavior
• Pricing matches the official rate: $5 / 1M input, $30 / 1M output, with cached input at just $0.50

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Background

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Detailed Analysis

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Core Features

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reasoning.effort — five tiers explained

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Performance Highlights

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Technical Specs

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Practical Use

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Recommended scenarios

1. Complex code engineering: large refactors, cross-file bug hunts, SWE-bench-style multi-step work
2. Professional research: legal, financial, medical, or scientific work where rigorous reasoning and low hallucinations matter
3. Long-context analysis: million-token codebase audits, cross-document comparison
4. Autonomous agents: multi-step planning workflows that need self-correction
5. xhigh reasoning tasks: problems other models simply can’t solve, justifying the top reasoning budget

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Code Examples

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Standard call (default medium reasoning)

from openai import OpenAI

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

response = client.chat.completions.create(
    model="gpt-5.5",
    messages=[
        {"role": "user", "content": "Refactor this Python code and explain your design decisions..."}
    ],
    max_tokens=8192
)

print(response.choices[0].message.content)

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Using the xhigh reasoning tier

response = client.chat.completions.create(
    model="gpt-5.5",
    messages=[
        {"role": "user", "content": "Here's a complex distributed-system deadlock. Give root cause and a fix..."}
    ],
    reasoning_effort="xhigh",
    max_tokens=16384
)

print(response.choices[0].message.content)

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Save cost by skipping reasoning (none)

response = client.chat.completions.create(
    model="gpt-5.5",
    messages=[
        {"role": "user", "content": "Translate this Chinese paragraph to English"}
    ],
    reasoning_effort="none",
    max_tokens=2048
)

print(response.choices[0].message.content)

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Best Practices

1. Match tier to difficulty: use none / low for simple tasks; only escalate to xhigh for genuinely hard ones
2. Use cached input: cached input drops to $0.50 / 1M — 1/10th of the standard rate
3. Be careful combining long context with xhigh: a million-token prompt at xhigh reasoning can be expensive — confirm you need both
4. Not every task needs GPT-5.5: chat, translation, and summarization are usually better served by GPT-5.4 or earlier

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Pricing & Availability

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Pricing (matches OpenAI’s official rate)

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Comparison with recent models

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Stack with deposit promotions

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Available Models

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Access

• Site: apiyi.com
• API endpoint: https://api.apiyi.com/v1
• OpenAI-compatible — just swap base_url and api_key

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Summary & Recommendation

• Already use GPT-5.4 and still hit reasoning ceilings on hard tasks
• Work in hallucination-sensitive domains (legal, finance, medical, research)
• Have multi-step coding or planning tasks that need xhigh to land

• Run general chat, translation, or summarization (older models are cheaper and good enough)
• Run high-volume calls where per-call cost matters
• Already have GPT-5.4 at medium reasoning solving the task reliably