GPT-5.5 Pro lands on official-relay: OpenAI's strongest reasoning model

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Key Highlights

• OpenAI’s strongest reasoning model today — flagship reasoning variant for the hardest professional workflows; significantly higher accuracy than base GPT-5.5
• Top-tier agentic / coding scores — Terminal-Bench 2.0 82.7%, Expert-SWE 73.1%, GDPval 84.9%
• Million-token context — 1,050,000 input window, 128,000 max output
• Tiered pricing — $30 / $180 per 1M tokens for the 0–272K range; $60 / $270 for 272K–∞ (long-context 2x premium)
• SVIP group only — not exposed on the Default group to prevent misuse: a single call can cost several dollars; confirm you need it before calling

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Background

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Detailed Breakdown

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Core Capabilities

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Benchmarks

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Tech Specs

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Practical Use

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Recommended Scenarios

1. Hardest code engineering — million-line codebase audits, cross-module deadlock root-cause, Expert-SWE-style long-chain tasks
2. Professional research — deep legal analysis, clinical decision support, complex financial modeling — anywhere errors are catastrophic
3. Long-context synthesis — million-token cross-document comparison, contract review, patent analysis
4. Autonomous agent planning — multi-step planning with self-correction in complex agent workflows
5. What base GPT-5.5 can’t crack — try gpt-5.5 first; only escalate to Pro when you’ve confirmed it can’t solve the task

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Code Examples

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Standard call

from openai import OpenAI

client = OpenAI(
    api_key="your-apiyi-key",  # Must be an SVIP-group key
    base_url="https://api.apiyi.com/v1"
)

response = client.chat.completions.create(
    model="gpt-5.5-pro",
    messages=[
        {"role": "user", "content": "Analyze this 800-page contract for compliance risks..."}
    ],
    max_tokens=16384
)

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

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Long-context (mind the 272K tier)

response = client.chat.completions.create(
    model="gpt-5.5-pro",
    messages=[
        {"role": "user", "content": long_codebase_audit_prompt}  # total tokens > 272K
    ],
    max_tokens=32768
)
# Note: above 272K context, input/output prices double
# Single-call cost can jump from a few dollars to double-digits — evaluate carefully

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Best Practices

1. Try base first — 90% of “hard” tasks are solved by gpt-5.5; only escalate when truly stuck
2. Watch the context budget — keep total tokens under 272K to avoid the 2x long-context tier
3. Hard budget limits in your app — enforce max_tokens and per-user quotas to prevent runaway spend
4. Batch jobs use Batch API — OpenAI’s official Batch API gives 50% off (APIYI hasn’t enabled this discount channel yet)
5. Not for real-time high-frequency apps — Pro responses are slower and pricier; don’t use it as a chat-bot backend

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Pricing & Availability

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Pricing (Tiered)

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Single-call cost estimates

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Price comparison with recent models

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Stack with site recharge promotions

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Available models & groups

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Summary & Recommendations

• You’ve already tried base GPT-5.5 and confirmed it can’t solve the task
• Single-error cost is catastrophic (legal, medical, finance, research)
• Long-horizon code, cross-file deep refactors, complex agent workflows
• Per-call value clearly exceeds the $5–$15 cost (high-ROI scenarios)

• Routine chat, translation, summarization, code completion (GPT-5.5 or 5.4 is enough)
• High-frequency, low-latency applications (Pro is slower)
• Cost-sensitive, high-volume consumer products