GPT-6 Astra vs Sol vs Luna: API comparison

The GPT-6 Astra vs Sol vs Luna choice is a workload decision for TTAPI developers. OpenAI positions Astra as its most capable model for difficult, multi-step work; Sol as a strong model for complex coding and agentic tasks; and Luna as its most efficient option for focused, high-volume requests. All three model IDs are listed in the TTAPI catalog.

Astra is the candidate when failure is expensive and the task needs broad judgment across code, research, and professional artifacts. Sol is a practical first test for everyday engineering and work that still needs reasoning. Luna fits extraction, triage, short transformations, and frequent automations when the result can be checked against a clear rule. These are starting points, not rankings measured on TTAPI.

OpenAI lists a 1,050,000-token context window and a 128,000-token maximum output for Astra, Sol, and Luna. All three accept text and image input and produce text. The official model catalog lists streaming and structured outputs across the family. Their available tools and controls depend on the API surface used.

TTAPI currently presents the three IDs through its OpenAI-compatible chat completions route. Do not infer that every capability of OpenAI's Responses API, Codex, or ChatGPT is available through this route. For a request with tool use or advanced reasoning controls, check the current TTAPI reference for the exact fields supported on the gateway.

• Astra: low through max reasoning effort; no none setting in OpenAI's model guide.
• Sol and Luna: none through max reasoning effort in OpenAI's model guide.
• Keep model-specific tool and reasoning settings out of a generic chat request until the TTAPI reference confirms them.

OpenAI describes Astra as the highest-capability GPT-6 model, Sol as the balanced option for demanding work, and Luna as the efficient model for repetition at scale. That positioning does not provide a controlled TTAPI latency or accuracy result for your application. A stronger model may also use fewer retries or less human correction, so price per million tokens is only one part of cost.

Run the same prompt and input on all three model IDs. For coding, require tests to pass and review unrelated edits. For document analysis, score fact coverage and unsupported claims. For extraction, validate the output against a schema. For each model, record accepted results, failures, elapsed time, input and output tokens, and the amount of human repair. Repeat each workload at least three times before drawing a conclusion.

• Hold the task, source material, output requirements, and acceptance checks constant.
• Compare the same reasoning setting where the gateway supports it.
• Report median latency and cost per accepted result, including retries.
• Separate provider-published capability claims from results you measured on TTAPI.

The TTAPI price table above follows the site's current OpenAI LLM channel data. Cached input is a separate rate: Astra is $0.8, Sol is $0.16, and Luna is $0.008 per million cached text tokens in the current TTAPI catalog. The price gap is large, but the right unit for product decisions is the cost of a result your team accepts.

OpenAI's direct API also has separate processing tiers and higher long-context rates above its published input threshold. Those provider terms should not be copied onto a TTAPI invoice without checking the live TTAPI billing rules. Estimate a workload using its observed usage and the current TTAPI pricing page, then revisit the estimate when prompts, context length, or model routing change.

The cURL example starts with gpt-6-sol on TTAPI's documented chat completions endpoint. Keep TTAPI_KEY on the server. To compare Astra and Luna, send the same request with only model changed to gpt-6-astra or gpt-6-luna. Save each response and usage record with the model ID so the comparison remains auditable.

Start with the simplest non-streaming call and one evaluation task. Add model-specific reasoning controls, streaming, or tool calls only after confirming those fields in the TTAPI reference. If the output does not meet the acceptance bar, test a stronger model before broadening the prompt or adding more workflow steps.

curl --request POST \
  --url 'https://api.ttapi.io/v1/chat/completions' \
  --header "Authorization: Bearer $TTAPI_KEY" \
  --header 'Content-Type: application/json' \
  --data '{
  "model": "gpt-6-sol",
  "messages": [
    {
      "role": "user",
      "content": "Review this API migration plan. List the three highest risks and one verification step for each."
    }
  ],
  "stream": false
}'