OpenAI’s current API ladder is led by GPT-6 Astra, followed by GPT-6 Sol and GPT-6 Luna. Astra is the maximum-capability route. Sol balances intelligence and cost for complex coding and agentic workflows, while Luna is the efficiency tier for focused, high-volume work.
The live chart, calculator, and pricing table above are generated from pricing.json at build time. For the complete provider table, use the OpenAI pricing page; for cross-provider shopping, open the AI API pricing comparison.
Calculate your OpenAI API bill: Enter your monthly input, cached-input, and output volumes in the token calculator to compare the GPT-6 tiers with current build-time rates.
How the GPT-6 tiers differ
| Tier | Start here when | Escalate when |
|---|---|---|
| GPT-6 Luna | The task is defined, repeatable, high-volume, or easy to verify | Quality misses create retries or human correction |
| GPT-6 Sol | Advanced reasoning, coding, computer use, or agent capability matters | Sol still misses on the hardest end-to-end work |
| GPT-6 Astra | The hardest research, coding, computer-use, or document workflows justify a premium | Keep Astra only where measured quality offsets the extra spend |
OpenAI launched Sol and Luna on September 22 with lower rates than their GPT-5.6 equivalents. Read the event-level analysis in GPT-6 Sol and Luna pricing: what it means.
All three GPT-6 tiers support text and image input, a 1.05-million-token context window, 128,000 maximum output tokens, and the Responses API tool stack. Do not treat a larger model as the automatic default: a cheaper tier can be the better route when the prompt is clear and the result is easy to check.
Hidden OpenAI API costs
Cache writes and reads are billed differently. GPT-6 cache reads receive a large discount, while writes cost more than normal input. Stable system instructions, tool schemas, and reference material should sit in a deterministic prefix so later calls reuse the cached region.
Long context changes the rate. When GPT-6 input crosses OpenAI’s long-context threshold, the full request receives higher input and output rates. Retrieval, summarization, and compaction can be cheaper than repeatedly sending an entire repository or document set.
Fast mode is a latency premium. It replaces Priority Processing and charges more than Standard without changing intelligence. Existing priority requests remain compatible. GPT-6 Fast mode is unavailable with EU data residency, so those requests must use Standard processing.
Reasoning and tool loops affect completed-task cost. A low per-token model can still be expensive if it retries, emits long reasoning traces, or makes unnecessary tool calls. Track total tokens, tool calls, retries, latency, and accepted results together.
Regional processing can add an uplift. Eligible data-residency endpoints may cost more than the global route. AWS-hosted OpenAI models can also differ from direct API pricing, so verify the billable platform rather than assuming one universal rate.
Best OpenAI model by use case
| Workload | First model to test | Why |
|---|---|---|
| Classification, extraction, routing | GPT-6 Luna | Efficient, easy-to-evaluate utility work |
| Customer support and RAG | GPT-6 Luna | Strong volume economics with escalation available |
| Data analysis and multi-step automation | GPT-6 Luna or Sol | Route by task difficulty and verification cost |
| Complex coding and agent planning | GPT-6 Sol | Strong capability without defaulting to Astra |
| Hardest end-to-end research and computer use | GPT-6 Astra | Maximum capability can reduce failed iterations |
| Offline bulk processing | GPT-6 Luna with Batch | Latency is less valuable than throughput savings |
| Latency-critical premium calls | Sol or Astra with Fast mode | Use only where response time has measurable value |
Do not pick a default from a benchmark alone. Build a small evaluation set from real production tasks, define an accepted-result rubric, and compare cost per accepted task. A router often wins: start cheap, detect uncertainty or failure, then escalate.
How to reduce OpenAI API spend
- Route by difficulty. Send routine calls to Luna or a nano model and reserve Sol for the hardest fraction of traffic.
- Use Batch or Flex for delay-tolerant work. Both can materially lower token rates when immediate responses are unnecessary.
- Keep prefixes cacheable. Avoid timestamps, random IDs, or reordered tool definitions inside otherwise stable context.
- Cap outputs. Output is usually the more expensive side of the request, and verbose agent loops compound quickly.
- Trim tool results. Return only the log lines, records, or document passages the next step needs.
- Measure accepted-task cost. Include retries, human correction, latency, and downstream failures—not only token volume.
For workload math, enter your actual prompt, cache, and response volumes in the AI token calculator.
Open-model quote to compare: Before standardizing on one provider, price the same evaluation set through Novita, an OpenAI-compatible managed route for DeepSeek, Llama, Qwen, image, and video models.
Disclosure: this article contains a sponsored affiliate link. It does not affect our pricing data or recommendations.
Free tier, subscriptions, and API billing
OpenAI does not advertise an ongoing free production API tier, and Astra is limited to paid usage tiers. Promotional credits may appear for new or eligible accounts, but applications should be budgeted as paid usage.
ChatGPT and Codex subscriptions do not replace API billing. Subscription prices and quota budgets are separate from the token-metered developer API. OpenAI launched Sol and Luna in ChatGPT Work and Codex for Plus, Pro, Business, Enterprise, and Edu users, with a gradual product rollout.
Bottom line
GPT-6 Luna is the first model to test for defined, high-volume work. GPT-6 Sol handles advanced coding and agent work, while Astra belongs on the hardest end-to-end calls. Fast mode belongs only where latency creates measurable value.
The best OpenAI architecture is usually a routing stack, not one model everywhere. Keep prompts cacheable, move delay-tolerant jobs to discounted processing, cap outputs, and judge every tier on cost per accepted result.
Sources: OpenAI’s official API pricing documentation, GPT-6 Astra model documentation, GPT-6 Sol and Luna announcement, and AI Pricing Guru’s live pricing dataset.