GPT-6 Sol vs Luna
Start with the task's verification cost, then decide whether Sol's higher rate is worthwhile.
GPT-6 Sol vs Luna is primarily a workload decision. Sol is positioned for complex coding and agent workflows; Luna for focused, high-volume tasks. For identical billed tokens in the same processing tier, Sol's listed prices are 20x Luna's. That is a price ratio, not a measure of intelligence or completed-task cost.
Checked September 23, 2026. This launch review evaluates OpenAI's published specifications and reproducible cost calculations. We have not run a controlled benchmark of these models. Workload recommendations below are starting points for evaluation, not measured accuracy or speed results.
GPT-6 Sol vs Luna: the key differences
Direct OpenAI prices in USD per one million tokens, using Standard processing. These are not OmniaKey gateway quotes.
| Specification | GPT-6 Sol | GPT-6 Luna |
|---|---|---|
| API model ID | gpt-6-sol | gpt-6-luna |
| OpenAI's positioning | Complex coding and agent workflows | Focused, high-volume tasks |
| Uncached input | $2.00 | $0.10 |
| Cached input | $0.20 | $0.01 |
| Cache write | $2.50 | $0.125 |
| Output | $10.00 | $0.50 |
| Context window (tokens) | 1,050,000 | 1,050,000 |
| Maximum input (tokens) | 922,000 | 922,000 |
| Maximum output (tokens) | 128,000 | 128,000 |
| Knowledge cutoff | 2026-04-20 | 2026-05-18 |
Both were released on September 22, 2026, accept text and images, and produce text. Both support none, low, medium (default), high, xhigh and max reasoning. Their matching feature lists make switching technically approachable, but do not establish equal reliability, speed or reasoning-token usage. Luna's later knowledge cutoff is not evidence of stronger general reasoning.
Sol vs Luna pricing on the same workload
| Illustrative workload | GPT-6 Sol | GPT-6 Luna |
|---|---|---|
| 100K uncached input + 10K billed output | $0.30 | $0.015 |
| 300K uncached input + 10K billed output | $1.35 | $0.0675 |
| 1,000 jobs of 10K input + 1K billed output each | $30.00 | $1.50 |
Above 272K input tokens, the entire request uses 2x input/cache rates and 1.5x output rates. The multiplier is not limited to the excess tokens. Exactly 272K remains in the lower bracket under the current wording.
These are calculations, not observed bills. Output includes billed reasoning tokens as well as visible text. The examples assume one attempt, no cache writes or hits, and exclude tool fees, storage, taxes and human review. Use actual usage fields when reconciling a run.
Which is better for coding, extraction and agents?
Evaluate Sol first for changes with connected decisions: debugging across modules, implementing a feature under compatibility constraints or interpreting several tool results. The reason is its documented positioning; keep the recommendation conditional on your own acceptance tests.
Evaluate Luna first for bounded, repeatable work: extracting fields, applying a known category set or making a local transformation. Cheap verification is the useful advantage here. A large context window does not make an unchecked output trustworthy.
An agent can use both. A planning or difficult repair step may justify Sol, while validated extraction runs on Luna. Keep the acceptance standard constant across routes, so a lower bill is not purchased by silently accepting worse results.
Can Luna-first routing reduce the bill?
Consider the 100K-input, 10K-output example. A Luna attempt costs $0.015 and a complete Sol rerun costs $0.30. If a validator escalates a hypothetical 20% of jobs, the average token budget is:
$0.015 + 0.20 × $0.30 = $0.075 per job
That is 75% below sending every job directly to Sol at $0.30, under these assumptions. The 20% is an illustration, not a measured failure rate. Validation, extra context, tools and human review can reduce or eliminate the saving. A validator that misses wrong answers invalidates the quality comparison.
For this simplified model, 0.015 + p × 0.30 < 0.30 requires p < 0.95. The 95% threshold is only a token-cost break-even, not an acceptable operational failure rate. For a real decision, divide total attempt, tool and review cost by the number of accepted tasks and inspect the residual error rate.
API compatibility and tool calling
Both models support Responses and Chat Completions. For reasoning together with tools, use Responses API. Their model pages allow Chat Completions function calling only when reasoning_effort is none; endpoint support does not imply identical tool behavior.
On Responses, the documented reasoning.effort values are none, low, medium, high, xhigh, and max. Start with a fixed setting and raise it only when an acceptance test justifies the extra work. Function calling, structured outputs and image input are supported. Native output is text; using an image-generation tool is a separate operation.
The request example and prices on this page refer to OpenAI directly. For OmniaKey, check the live model catalog for the exact route, supported features and current quote. OpenAI's release does not by itself verify a gateway route, a Codex picker or a ChatGPT plan entitlement.
How to compare Sol and Luna fairly
Use 20–50 representative tasks, including known difficult cases. Freeze the input, repository revision, tools, permissions, reasoning setting and timeout. Equal effort names are a reproducible configuration choice, not proof of equal compute.
Record first-attempt acceptance, acceptance after retry, failures, input/cache/reasoning/output tokens, tool charges, end-to-end p50/p95 latency and manual repair time. Assess accepted outputs for missed errors. Repeat quality-sensitive cases and keep both routes until the results are stable.
This review supplies an evaluation method, not benchmark results. Do not infer that Luna is faster from its lower price, or that Sol is 20 times better from its higher price. Choose the route that meets your quality and latency requirements at the lowest verified cost per accepted task.
Frequently asked questions
Is GPT-6 Sol 20 times more expensive than Luna?
Yes for equal billed tokens, the same processing mode and corresponding price brackets. That includes input, cache reads, cache writes and output. Real jobs can use different token volumes, tools and retries.
Should I use Sol or Luna for a coding agent?
Start by evaluating Sol for connected engineering tasks and Luna for bounded steps with executable checks. No matched quality benchmark was run for this article; use your own acceptance results to decide.
Is GPT-6 Terra the middle option?
The current public GPT-6 catalog lists Astra, Sol and Luna. It does not list gpt-6-terra. Do not confuse GPT-5.6 Terra with a confirmed GPT-6 release.