OpenAI API Pricing Explained (2026)

By Gia Gray · Updated November 2026 · 8 min read

OpenAI's lineup has turned over again: the current generation is GPT-6 Astra, GPT-6.1 Sol, GPT-6.1 Terra, and GPT-6 Luna, and the GPT-5.x, GPT-4 and o-series models are now legacy or being retired. I still see teams accidentally running deprecated models, paying for caching they never enabled, and reaching for the top-tier model on tasks a cheaper tier handles identically. Here's what I actually use to pick a model and estimate what it'll cost — with real numbers.

OpenAI's Current Model Lineup and Pricing

Source: developers.openai.com/api/docs/pricing (Standard tier), November 2026. GPT-4o and GPT-4o mini are still callable but legacy, with API shutdown scheduled for January 14, 2027; the GPT-5.x line has been superseded by GPT-6. Prompts above 272K input tokens are billed at a long-context premium. Always verify before production use.

GPT-6.1 Sol: The Workhorse for Most Production Use Cases

GPT-6.1 Sol is the model most teams should default to — OpenAI pitches it as near-Astra performance at a fraction of the cost, with a ~1M token context window, at $2.00/$10.00 per million tokens. For most text-based production workloads it's the sensible high-quality default. If you want to shave cost further, GPT-6.1 Terra ($1.00/$5.00) sits one step down at exactly half the price.

Output pricing ($10/M) is where cost accumulates. If your app generates long responses — document drafting, detailed code explanations, multi-step reasoning — it adds up fast. A workload generating 500 tokens per response at 50,000 requests/month is already $250 in output alone.

Sol is worth paying for when response quality directly affects the user experience, when you're doing complex reasoning or instruction-following, or when multimodal input is part of the pipeline.

GPT-6 Luna: The Right Default for High-Volume Work

GPT-6 Luna at $0.10/$0.50 per million tokens is one of the best deals in the OpenAI lineup — 20× cheaper than Sol and strong at classification, extraction, summarization, basic Q&A, translation, and simple code completion.

The common mistake is treating Luna as "Sol but worse." It's better framed as a different category — good enough for the large majority of tasks, drastically cheaper for the ones where quality differences don't reach users. Teams that route aggressively to Luna often cut their bill 50–70% with no meaningful user-facing impact.

GPT-6 Astra and Reasoning / Thinking Tokens

GPT-6 Astra ($10/$50) is the frontier tier for the hardest coding, math, and multi-step agentic work. Like the whole current generation, it leans on thinking tokens — internal reasoning generated before the visible answer, billed at the output rate. On hard problems those can dominate the cost of a single request.

That shifts the metric that matters from cost-per-token to cost-per-correct-answer: a pricier model that gets it right the first time can be cheaper end-to-end than a cheap one you re-run three times. Reserve Astra for tasks where the quality difference is real, and budget for thinking tokens explicitly on reasoning-heavy workloads.

Prompt Caching: 90–95% Off Repeated Input

OpenAI caches repeated prompt prefixes (a long system prompt or document sent across many requests) and charges cache hits at 5–10% of the normal input rate on the GPT-6 line (e.g. $0.10/M on GPT-6.1 Sol vs $2.00/M uncached).

This matters a lot for large, consistent system prompts. A 2,000-token system prompt across 100,000 requests/month is 200M input tokens. On GPT-6.1 Sol that's $400 uncached vs $20 on cache hits. Cache writes are now billed separately ($2.50/M for a 30-minute write on Sol), so caching pays off when a prefix is reused many times — structure prompts so the stable parts come first.

Batch API: 50% Off Non-Realtime Requests

OpenAI's Batch API processes requests asynchronously (within 24 hours) at half price. For workloads that don't need immediate responses — bulk document processing, overnight analysis, bulk content generation — it halves your cost with no quality tradeoff.

Flex/Batch pricing on GPT-6.1 Sol: $1.00 input / $5.00 output per million tokens. On GPT-6 Luna: $0.05 / $0.25. Low rates for capable models.

What Model Should You Use?