GPT-5.6 API Pricing: Sol vs Terra vs Luna

GPT-5.6 is available in three API models: gpt-5.6-sol, gpt-5.6-terra and gpt-5.6-luna. Sol is the highest-capability and highest-cost option, Terra is the balanced option, and Luna has the lowest price for high-frequency workloads.

All three GPT-5.6 models are live on RouterPlex with image input, 128K maximum output and a 258K input limit on the current master route.

GPT-5.6 API price comparison #

Cached input is one tenth of standard input for these models. Output remains the largest cost category, so concise completion limits and clear stopping conditions matter more than small prompt changes in many workloads.

GPT-5.6 Sol vs Terra vs Luna #

Choose GPT-5.6 Sol for the hardest work

Sol has the highest price and is positioned for complex coding, long-document reasoning, difficult analysis and image understanding. Use it when a better answer is worth more than token savings: architecture reviews, high-risk migrations, hard debugging sessions or multi-stage agents.

Live pricing: GPT-5.6 Sol API.

Choose GPT-5.6 Terra for a balanced default

Terra costs half as much as Sol on both standard input and output. That makes it the practical starting point for everyday coding assistance, document work, customer-facing AI features and agents whose tasks are important but not consistently frontier-hard.

Live pricing: GPT-5.6 Terra API.

Choose GPT-5.6 Luna for volume

Luna costs one fifth of Sol on input and output. It fits classification, extraction, straightforward Q&A, document triage and high-frequency automation where a lower per-request cost creates more value than maximum capability.

Live pricing: GPT-5.6 Luna API.

Use GPT-5.6 through one OpenAI-compatible endpoint #

The model string is the only part that changes:

curl https://api.routerplex.com/v1/chat/completions   -H "Authorization: Bearer $ROUTERPLEX_KEY"   -H "Content-Type: application/json"   -d '{
    "model": "gpt-5.6-terra",
    "messages": [
      {"role": "user", "content": "Find the failure modes in this deployment plan."}
    ]
  }'

To compare all three, run the same representative prompt set with a separate hard budget for each model. Record output quality, latency, uncached input, cached input and output cost instead of judging from one demo prompt.

Context and vision behavior #

OpenAI's direct GPT-5.6 model pages list a 1,050,000-token context window and 128,000 maximum output tokens. RouterPlex currently advertises 258K input because that is the limit supplied by its master route. The smaller published RouterPlex limit is intentional: customers should plan against the route they can actually call, not a larger direct-vendor specification.

Sol, Terra and Luna all accept images. That makes the same endpoint useful for screenshots, diagrams, scanned documents and mixed text-image tasks. The output remains text.

A simple selection rule #

Start with Terra. Move to Sol only when evaluation shows a meaningful quality improvement on the task that pays for the higher rate. Move to Luna when Terra succeeds reliably and throughput or unit cost matters more.

This approach is more reliable than choosing from benchmark headlines. Your prompt structure, tools, repository and error tolerance determine which price-quality point is best.

Start a $5 RouterPlex test, set a hard budget on the key, and compare GPT-5.6 with Kimi K3 and Claude Fable 5.