Home / GPT-6 Sol vs Luna Cost
Sol and Luna are two tiers of the same GPT-6 API. Sol costs exactly 20× Luna on every published rate — input, cached input, and output. Enter your own workload below to see what that 20× means in dollars per month.
✓ Last verified: September 26, 2026 · Manually verifiedPick a model, set your workload, and watch both models update side by side. Prices come from the same data layer as every calculator on this site — never hard-coded per page.
Source: OpenAI official pricing. Per 1M tokens, USD, short-context Standard tier — the rates the calculator above uses.
| Model | Input / 1M | Cached input / 1M | Output / 1M |
|---|---|---|---|
| GPT-6 Luna | $0.10 | $0.01 | $0.50 |
| GPT-6 Sol | $2.00 | $0.20 | $10.00 |
| Sol ÷ Luna | 20× | 20× | 20× |
Long-context rates exist for both models (Luna $0.20 / $0.02 / $0.75, Sol $4.00 / $0.40 / $15.00 per 1M) and keep exactly the same 20× ratio. Details on the methodology page.
Take a concrete workload — 30,000 input + 8,000 output tokens per request, 20,000 requests/month, 40% cached input, Standard tier. This is a typical coding-agent profile: long retrieved context in, long code completions out.
| Per request | Per day | Per month | Per year | |
|---|---|---|---|---|
| GPT-6 Luna | $0.00592 | $3.95 | $118.40 | $1,420.80 |
| GPT-6 Sol | $0.11840 | $78.93 | $2,368.00 | $28,416.00 |
| Difference | $0.11248 | $74.99 | $2,249.60 | $26,995.20 |
Sol costs $2,249.60 more per month — the 20× ratio, in dollars. Note the ratio is identical at every time scale (per request, per day, per year) because both models' prices scale linearly with tokens. What changes with volume is the absolute gap: at 200,000 requests/month the difference would be $22,496/month. Small per-request differences compound fast.
Both models discount cached input to 0.1× the standard rate — the same ratio. That has one important consequence: caching never changes which model is cheaper.
On the example workload (40% cached input): Luna drops from $140.00 to $118.40/month (saves $21.60, −15.4%); Sol drops from $2,800.00 to $2,368.00/month (saves $432.00, −15.4%). Identical percentage, very different dollars — because the saving per cached token scales with the base price. If you cache aggressively, Sol's absolute savings are larger, but Luna stays 20× cheaper regardless.
For the example workload, the split is identical on both models — 67.6% output, 32.4% input — because the 20× scaling is uniform. The takeaway is about workload shape, not model choice: output tokens dominate this bill. Every extra 1,000 output tokens costs $0.0005 on Luna vs $0.01 on Sol. Output-heavy workloads (coding agents, long generations, chain-of-thought) therefore produce the largest absolute gaps; input-heavy workloads (RAG with huge retrieved contexts, tiny answers) shrink the dollar difference while the 20× ratio stays put.
Math only, no verdicts. This page compares cost arithmetic. It does not claim Sol or Luna is "better" — quality, latency, and context limits are separate decisions. The calculator exists so you can price your actual token mix instead of guessing from per-1M rates.
Exactly 20× on every published rate: input $2.00 vs $0.10, cached input $0.20 vs $0.01, output $10.00 vs $0.50 per 1M tokens (short-context Standard rates, verified September 26, 2026). Because the ratio is uniform, the 20× holds for any workload mix.
No. Both models discount cached input to 0.1× the standard rate, so caching cuts both bills by the same percentage (15.4% in our example workload). It changes absolute dollars — Sol saves $432/month versus Luna's $21.60 at 40% cache — but it never flips the ranking.
No. Both tiers multiply the whole bill by 0.5× regardless of model, so the 20× ratio is preserved; only the absolute gap halves — $1,124.80/month on the example workload instead of $2,249.60.
Long-context rates are higher for both models (Luna $0.20/$0.02/$0.75, Sol $4.00/$0.40/$15.00 per 1M), but the Sol-to-Luna ratio stays exactly 20×. Our calculator uses short-context rates; treat its estimate as a lower bound if your prompts routinely exceed roughly 200K tokens.
The ratio is constant, but the dollar gap grows with output volume: each extra 1,000 output tokens adds $0.0005 on Luna versus $0.01 on Sol. Output-heavy workloads like coding agents therefore show the largest absolute differences.
No — this page compares cost arithmetic only. Capability, latency, and quality differences are outside its scope. Run both models on your own evaluations, then plug the token counts into the calculator above.
Independence disclosure: this is an independent cost-estimation tool. It is not affiliated with, sponsored by, or endorsed by OpenAI, Anthropic, or Google. Prices are a snapshot last verified September 26, 2026 and may be outdated — always confirm on the provider's official pricing page before making decisions.