GPT-5.6 API Pricing: The November 21 Deadline

Last updated: October 2026

If you're running GPT-5.6 through the API right now, you're on a promotional price. And that promotion has an expiry date: November 21, 2026.

OpenAI's pricing page states the current rate is "available at least through November 21, 2026." Read that carefully. "At least through" — not "until." The promo could extend. It could also end exactly on that date. OpenAI has published no pricing for what comes after.

This post lays out exactly what the pricing is today, what it costs on a realistic workload, and how to budget for the unknown that follows November 21.

The Current Pricing, Exactly

GPT-5.6 is available in several variants. The one most developers are using — the flagship reasoning model, referred to here as GPT-5.6 Sol — is currently priced at:

  • Input: $4.00 per 1M tokens
  • Cached input: $0.40 per 1M tokens
  • Output: $20.00 per 1M tokens

    • That is promotional pricing. Before the promo, announced August 21, 2026, the list price was $5.00 input / $30.00 output per 1M tokens.

    So the promo is a 20% cut on input and a 33% cut on output. Meaningful — but temporary, or at least not guaranteed past November 21.

    The Fine Print That Actually Matters

    Three pricing details that bite people:

    1. The 272K token threshold. If a single request contains more than 272K input tokens, the entire request is billed at a higher rate: 2x on input, 1.5x on output. Not just the tokens above the threshold — the full request. On the promo rate, that means $8.00/1M input and $30.00/1M output for the whole call.

    This matters most for coding agents with large context windows. A repo loaded into context plus a long conversation history can cross 272K faster than you'd expect. If your agent regularly runs hot on context, model your costs at the 2x/1.5x rate, not the headline rate.

    2. Cache writes cost 1.25x. Prompt caching is the single biggest cost lever on this model (more on that below), but writing to the cache isn't free: cached tokens you write are billed at 1.25x the input rate. Reads are the cheap part at $0.40/1M. The math still overwhelmingly favors caching — a token read from cache costs a tenth of a fresh token — but don't model cache writes as free.

    3. Batch API and Flex processing: 50% off. If your workload isn't latency-sensitive — evals, bulk processing, overnight jobs — the Batch API halves your bill. Same model, same quality, just slower turnaround. Flex processing offers a similar discount tier for eligible workloads. If you're not using batch for anything that can wait, you're leaving money on the table.

    The Workload Math

    Headline rates don't tell you what you'll actually pay. Let's run a realistic coding-agent workload.

    Assumptions (stated up front so you can adjust):

    • 50 tasks per day, 22 working days per month = 1,100 tasks/month
    • Per task: 12K fresh input tokens, 48K cached input tokens, 6K output tokens

    That token mix is typical for an agentic coding loop: a stable system prompt and repo context served from cache, plus fresh tokens for the current task and conversation, with moderate output per step.

    At promo pricing ($4.00 / $0.40 / $20.00):

    • Fresh input: 12,000 × $4.00/1M = $0.0480
    • Cached input: 48,000 × $0.40/1M = $0.0192
    • Output: 6,000 × $20.00/1M = $0.1200
    • Per task: $0.1872 → per month: $205.92

    If pricing reverts to the pre-promo $5/$30 (the only concrete number we have for "after"):

    • Fresh input: 12,000 × $5.00/1M = $0.0600
    • Cached input: 48,000 × $0.50/1M = $0.0240 (cache scales proportionally)
    • Output: 6,000 × $30.00/1M = $0.1800
    • Per task: $0.2640 → per month: $290.40

    That's a 41% increase overnight, on identical usage.

    To be clear: nobody has said the price reverts to $5/$30. It might stay at $4/$20. It might land somewhere in between. There is no official post-November-21 pricing — I've checked, and as of this writing OpenAI has published nothing. The $290.40 figure is a scenario, not a prediction. But it's the only scenario with a real number attached, which makes it the one to budget against.

    The Fallbacks Worth Knowing

    If the promo ends and the price jumps, you have options. Two worth modeling on the same workload:

    GPT-6.1 Sol: $2/$10

    The newer, smaller sibling is priced at $2.00 input / $10.00 output per 1M tokens, with cached reads at just $0.10 — a twentieth of the input rate. On our workload:

    • Fresh input: 12,000 × $2.00/1M = $0.0240
    • Cached input: 48,000 × $0.10/1M = $0.0048
    • Output: 6,000 × $10.00/1M = $0.0600
    • Per task: $0.0888 → per month: $97.68

    Less than half the promo cost of GPT-5.6 Sol. The tradeoff is capability — it's a smaller model. For routine coding tasks it may be plenty; for the hardest reasoning work, maybe not. The honest answer is to benchmark it on your tasks before November 21, while you still have the promo as a safety net.

    Claude Opus 5.5: $4/$20

    Anthropic's flagship is priced at $4.00 input / $20.00 output — identical headline rates to the GPT-5.6 Sol promo — but with cheaper cache reads at $0.20 (0.05x). On our workload:

    • Fresh input: 12,000 × $4.00/1M = $0.0480
    • Cached input: 48,000 × $0.20/1M = $0.0096
    • Output: 6,000 × $20.00/1M = $0.1200
    • Per task: $0.1776 → per month: $195.36

    That's 5.1% cheaper than the Sol promo on a cache-heavy workload, and the gap widens the more you lean on caching. If your workload is cache-dominated — and most agentic loops are — Opus 5.5's cache economics deserve a serious look regardless of what happens on November 21.

    What To Do Before November 21

    Three concrete moves:

    1. Measure your actual token mix. The math above uses assumed numbers. Pull your real usage from the OpenAI dashboard — fresh vs. cached input ratio, average output per request, how often you cross 272K. Your numbers will differ; the method is what matters.

    2. Benchmark the fallbacks now. Run GPT-6.1 Sol and Claude Opus 5.5 against a sample of your real tasks while the promo is still active. You want to know before the deadline whether the cheaper option clears your quality bar — not scramble after it.

    3. Budget on the later price, not the promo. This is the core discipline. If your unit economics only work at $4/$20, then November 21 is a 41% cost shock waiting to happen. Model the $5/$30 scenario (or higher), and make sure the business still works. If it doesn't, you have a month to fix it — via caching, batching, or a cheaper model — instead of discovering it on the invoice.

    The Bottom Line

    GPT-5.6 Sol at $4/$20 is a good price with an expiry date nobody can see past. OpenAI's page says "at least through November 21, 2026" and nothing more. The developers who'll be fine are the ones who measured their usage, tested the alternatives, and budgeted for the worst case while the promo was still running.

    November 21 is a month away. That's enough time — if you start now.



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