GPT-5.6 API Pricing: Sol, Terra, Luna Costs Compared

The decision: use Terra for cost-sensitive GPT-5.6 workloads; reserve Sol for cases where you have validated its value

If your application can use either GPT-5.6 Sol or GPT-5.6 Terra, the current OneMux catalogue prices make Terra the clear cost-first option. Terra is priced at $0.286 per million input tokens and $1.716 per million output tokens, compared with Sol at $0.65 input and $3.90 output.

That is not a small rounding difference: Sol costs roughly 2.27× Terra on both token types. The right reason to pay for Sol is therefore not its name or tiering alone, but a measured improvement on the prompts, outputs, tool flows, or operational requirements that matter to your product.

For teams also searching for DeepSeek API pricing, the more useful comparison is not merely the lowest headline rate. It is the cost of your actual input/output mix, plus the cost of switching models when a task does not need the higher-priced route.

GPT-5.6 Sol and Terra prices on OneMux

The following figures are the model-catalogue prices supplied for OneMux, stated in US dollars per million tokens.

Output pricing deserves special attention. Sol's output rate is six times its input rate, so long answers, generated drafts, support replies, and multi-step agent outputs can move the bill faster than prompt growth. Terra has the same input-to-output ratio, at a lower absolute rate.

What about GPT-5.6 Luna?

No GPT-5.6 Luna entry or price was included in the supplied OneMux model catalogue. There is no responsible way to calculate a Luna comparison from the available data.

A third-party pricing guide reports GPT-5.6 Sol promotional pricing of $4 per million input tokens and $20 per million output tokens through at least November 21, versus a $5/$30 list price. The supplied summary does not provide enough information to verify a Luna rate, establish which provider or billing context those prices apply to, or treat them as OneMux pricing. See the cited GPT-5.6 pricing guide.

Keep source-specific promotions separate from the live rate you will actually pay through your chosen provider. Before committing a pricing page, record the model identifier, token categories, currency, effective date, and whether cached, batch, reasoning, or tool-related tokens have separate treatment.

Workload math: where Sol becomes materially more expensive

A simple planning formula is:

monthly cost = (input tokens / 1,000,000 × input rate) + (output tokens / 1,000,000 × output rate)

Consider a monthly workload of 100 million input tokens and 25 million output tokens. This mix is common enough to be useful for planning: substantial context or retrieved material, with shorter generated responses.

Sol costs $91 more than Terra in this example. DeepSeek V4 Pro is also lower-cost than Sol here, despite its input price being closer to Sol's, because its output rate is much lower. DeepSeek V4 Flash establishes a different budget tier entirely.

These figures are token-cost estimates only. They are not forecasts of quality, latency, tool compatibility, or task success. Those dimensions need testing against your own workload.

A practical routing policy for product and operations teams

Do not make every request a Sol request by default. Classify work by consequence and output length.

Put low-consequence, high-volume work on the lower-cost route

For support teams, this can include intent classification, ticket tagging, suggested reply drafts subject to human review, and extracting fields from predictable messages. For marketers, it can include first-pass variations, taxonomy work, or structured content transformations. For international buyers, token accounting remains the same calculation, but finance teams should confirm invoicing and currency treatment with their provider rather than infer it from a model rate card.

Terra is the natural first comparison when you want to remain within the GPT-5.6 family while reducing spend. DeepSeek V4 Flash is worth testing when throughput economics are the primary constraint and the task is suitably bounded.

Escalate only when the request earns the higher rate

Keep Sol behind an explicit policy: a higher-stakes workflow, a task that fails on the lower-cost candidate during evaluation, or an escalation after a validation check. Founders can use this structure to control unit economics before a feature reaches broad usage; operators can attach separate caps and reporting to each route.

Measure outcomes that relate to the task: acceptance rate for a support draft, valid-schema rate for extraction, editor revision time for marketing copy, or successful completion of a defined workflow. A model that is cheaper per token but causes more retries is not necessarily cheaper per completed task.

Implement the comparison without multiplying integrations

OneMux provides access to leading AI models through one OpenAI-compatible API, with model access, routing, keys, spend visibility, and credit top-ups designed around pay-as-you-go usage. That lets a team retain one integration while selecting a model per request or per workload policy.

Start with the OneMux quickstart guide, then create a small evaluation set that reflects production traffic. Include short prompts, long-context requests, structured-output cases, and prompts likely to produce lengthy answers. Run the same set against Sol, Terra, and the DeepSeek candidate you are considering. Log input tokens, output tokens, retries, validation outcomes, and human acceptance where applicable.

Set a practical output ceiling as well. Because output tokens are the more expensive side of Sol and Terra, tighter response instructions, schema constraints where supported by your application, and sensible maximum output settings can protect spend without changing the selected model.

Review current account and usage options on the OneMux pricing page before deployment. Pricing, availability, and model catalogues can change, so production budgets should use the rate visible at the time of purchase rather than an old comparison article.

Conclusion

For a GPT-5.6 cost decision on the available OneMux rates, Terra is the default economic choice: $2.002 for a balanced million-input/million-output workload versus Sol's $4.55. Sol may still be the right route when your evaluation shows a worthwhile task-level advantage, but that advantage should be demonstrated rather than presumed.

DeepSeek V4 Pro offers a closer cost comparison for teams considering DeepSeek API pricing, while DeepSeek V4 Flash is the lower-cost option in this specific catalogue comparison. Luna remains a gap in the supplied pricing data, not a number to estimate.

Sources

• GPT-5.6 Pricing Guide — API Pulse — cited for the reported Sol promotional and list-price figures described in the supplied source summary.
• OneMux GPT-5.6 Sol model page — catalogue rate referenced for Sol.
• OneMux GPT-5.6 Terra model page — catalogue rate referenced for Terra.
• OneMux DeepSeek V4 Pro model page — catalogue rate referenced for the DeepSeek comparison.