OpenAI: Migrating to GPT-5.6

Use this guide when you are preparing existing OpenAI traffic for GPT-5.6. GPT-5.6 is the current OpenAI GPT family for complex production workflows. In AI Stats, the fixed tier IDs map to the OpenAI model IDs gpt-5.6-sol, gpt-5.6-terra, and gpt-5.6-luna.

Choose the right GPT-5.6 model

Model	Use it for	Reasoning effort
`openai/gpt-5.6-sol`	Highest-capability reasoning, agentic coding, scientific analysis, and complex professional work	`none`, `low`, `medium`, `high`, `xhigh`, `max`
`openai/gpt-5.6-terra`	Balanced everyday work across reasoning, coding, and assistant workflows	`none`, `low`, `medium`, `high`, `xhigh`, `max`
`openai/gpt-5.6-luna`	Lower-latency and cost-sensitive GPT-5.6 workloads	`none`, `low`, `medium`, `high`, `xhigh`, `max`

OpenAI’s gpt-5.6 alias routes to gpt-5.6-sol. In AI Stats, use openai/gpt-5.6 or openai/gpt-sol-latest when you want that Sol-tier default, and use the fixed tier IDs for controlled routing. AI Stats also tracks tier aliases for the latest model in each tier: openai/gpt-sol-latest, openai/gpt-terra-latest, and openai/gpt-luna-latest. Use the fixed GPT-5.6 IDs for controlled migrations, and use the tier aliases only when you deliberately want future Sol, Terra, or Luna releases to roll forward through the same route.

What changed

GPT-5.6 adds the new Sol/Terra/Luna split instead of one default GPT route.
All three GPT-5.6 tiers support reasoning.effort: "max" for the highest reasoning budget.
GPT-5.6 supports reasoning.mode: "pro" without switching to a separate Pro model slug.
GPT-5.6 adds persisted reasoning controls through reasoning.context.
GPT-5.6 adds beta multi-agent support and Programmatic Tool Calling for eligible tool-heavy workflows.
Prompt caching is priced with separate uncached input, cache read, cache write, and output meters.
Explicit prompt caching is supported through prompt_cache_options; OpenAI currently recommends prompt_cache_options.ttl instead of prompt_cache_retention.

Update your request

Start by swapping only the model id and keeping the rest of the request stable. The first examples use the Responses API-style input shape. If you are migrating Chat Completions traffic, keep using messages and the flat reasoning_effort field where the route supports it.

{
  "model": "openai/gpt-5.6-terra",
  "input": "Summarize the rollout risks in this migration plan.",
  "reasoning": {
    "effort": "medium"
  }
}

Use max effort only for routes where the extra reasoning budget is worth the latency and cost.

{
  "model": "openai/gpt-5.6-sol",
  "input": "Review this multi-service incident report and propose a rollback plan.",
  "reasoning": {
    "effort": "max"
  }
}

If your integration still sends the flat OpenAI-compatible field, AI Stats also accepts reasoning_effort where the route supports it:

{
  "model": "openai/gpt-5.6-sol",
  "messages": [
    {
      "role": "user",
      "content": "Design a test plan for this agent workflow."
    }
  ],
  "reasoning_effort": "max"
}

Review pricing

GPT-5.6 pricing is tracked per 1M tokens in the catalog.

Model	Input	Cache read	Cache write	Output
Sol	$5.00	$0.50	$6.25	$30.00
Terra	$2.50	$0.25	$3.125	$15.00
Luna	$1.00	$0.10	$1.25	$6.00

Cache reads are priced separately from cache writes. In the current catalog, cache reads use a 90% discount from uncached input, while cache writes are priced at 1.25x uncached input.

Use prompt caching deliberately

For repeated context, keep the stable part of the prompt in cacheable blocks and leave request-specific text uncached.

{
  "model": "openai/gpt-5.6-sol",
  "input": [
    {
      "role": "user",
      "content": [
        {
          "type": "input_text",
          "text": "Stable policy document...",
          "cache_control": {
            "type": "ephemeral",
            "ttl": "1h"
          }
        },
        {
          "type": "input_text",
          "text": "Apply the policy to this new customer request."
        }
      ]
    }
  ],
  "prompt_cache_options": {
    "mode": "explicit",
    "ttl": "24h"
  }
}

Use cache_control when you want provider-neutral cache hints or explicit cache breakpoints. Use prompt_cache_options when you want to pass OpenAI cache mode and TTL options directly.

What to test

Reasoning and output quality

Sol, Terra, and Luna at the effort levels you expect to expose to users, including max where quality-first workflows justify it
standard mode versus reasoning.mode: "pro" on difficult tasks where quality matters more than latency
structured outputs and schema pass rate at each effort level
tool-call selection and argument quality

Cost and latency

latency at each reasoning effort
output token growth when moving from older GPT-5.x models
cache read/write mix on repeated prompts
cost per successful task, not just price per token

Rollback

keep your previous GPT-5.x route available as a fallback
keep max behind a config flag or preset until it is proven on production-like prompts
monitor cache write volume separately from cache read volume
move GPT-5.6 into default routing only after your own evals confirm task success, cost, and latency

​OpenAI: Migrating to GPT-5.6

​Choose the right GPT-5.6 model

​What changed

​Update your request

​Review pricing

​Use prompt caching deliberately

​What to test

​Reasoning and output quality

​Cost and latency

​Rollback

​Sources