GPT-5.6 Sol costs $5 per 1M input tokens and $30 per 1M output tokens, GPT-5.6 Terra costs $2.50 and $15, and GPT-5.6 Luna costs $1 and $6
More intelligence from every token, stronger performance per dollar, and more capability on demand for your hardest work.
Context & Ripple Effects
The preceding GPT-5.4 and GPT-5.5 coverage shows a familiar pair of API price points: $2.50/$15 per million input/output tokens for GPT-5.4, then $5/$30 for GPT-5.5. GPT-5.6 now places Terra at the former level and Sol at the latter, while adding the lower-priced Luna tier.
The GPT-5.6 family had already been discussed in the context of vulnerability identification, making the pricing structure relevant to how broadly developers can deploy different capability levels rather than treating the release as a single undifferentiated model.
First-order effects
- OpenAI offers three GPT-5.6 API cost bands: Luna at $1/$6, Terra at $2.50/$15, and Sol at $5/$30 per million input/output tokens.
- Customers can choose among named GPT-5.6 variants based on token economics, with output tokens remaining materially more expensive than input tokens across all three tiers.
Second-order effects
- Applications with output-heavy workloads have a clear incentive to route less demanding tasks toward Luna or Terra, while reserving Sol for work where its higher-priced tier is justified.
- By retaining the earlier GPT-5.4 and GPT-5.5 price points inside one generation, OpenAI makes version-to-version price comparison less central than model-tier selection; competing API providers will be measured against a broader pricing ladder rather than one flagship rate.
Third-order effects
- If this structure persists, frontier-model pricing may increasingly be organized as portfolios of capability and cost tiers, with routing across models becoming a core product and procurement decision.
- The continued input/output price spread means efficiency efforts will likely focus not only on prompt size but also on constraining generated output, especially in agentic or iterative workflows.
The trend: This is one data point in the shift from single-model API launches toward tiered model portfolios designed to let customers trade capability against inference cost within the same provider.