GPT-6 Luna is the lower-price starting point; Claude Sonnet 5.5 is the challenger when task quality matters more than equal-volume token cost. Neither vendor’s price alone establishes the cheaper completed workflow.
The chart, calculator, and pricing table above read our maintained dataset at build time. This refresh replaces the older GPT-5.6 Luna/Sonnet 5 comparison without changing its URL. It also removes obsolete guidance about a scheduled Sonnet price increase.
What changed in this comparison
The buying decision now uses current GPT-6 Luna and Claude Sonnet 5.5 rows rather than treating July’s models and introductory terms as today’s recommendation. GPT-6.1 Sol is the balanced OpenAI escalation control, while Opus 5.5 provides a higher-cost Claude control.
On standard short-context pricing, Sonnet 5.5’s fresh-input and output rates are twenty times Luna’s; its cache-read rate is ten times Luna’s. Those are equal-token comparisons, not measured task savings. Each model’s tokenizer, output length, reasoning, and retries can change the bill.
See OpenAI pricing and Anthropic pricing for full catalogs. The OpenAI API guide explains the broader routing ladder.
Features and workload fit
| Factor | GPT-6 Luna | Claude Sonnet 5.5 |
|---|---|---|
| Standard token price | Lower-cost route | Premium over Luna |
| Context window | 1,050,000 tokens | 1,000,000 tokens |
| Output limit | 128,000 tokens | 128,000 tokens |
| First workload to evaluate | Extraction, classification, structured summaries | Coding and complex agent workflows |
| Provider ecosystem | OpenAI integrations | Claude integrations |
| Escalation control | GPT-6.1 Sol | Claude Opus 5.5 |
These are shortlist roles, not a neutral capability ranking. Give both models the same documents, tools, output requirements, and acceptance checks before assigning a production winner.
Choose Luna when tasks are repetitive, errors are inexpensive to detect, and most requests pass automated validation. Lower input and output rates matter most when traffic is large and correction costs stay small.
Choose Sonnet 5.5 when your evaluation shows it returns acceptable code or analysis with fewer failed attempts or less review. Do not assume a more expensive model is better: require task-level evidence that recovers the premium.
Test GPT-6.1 Sol when Luna fails quality checks but you want to remain in the OpenAI stack. Compare it directly with Sonnet rather than moving every request to a flagship.
Model the bill before switching
Use the token calculator with the article’s preselected models. Run these scenarios with the same volumes, then replace estimates with usage logs:
| Scenario | Monthly uncached input | Monthly output | Next adjustment |
|---|---|---|---|
| Structured extraction | 10M tokens | 1M tokens | Add validation failures |
| Support drafts | 5M tokens | 2M tokens | Add reviewer time |
| Coding pilot | 2M tokens | 1M tokens | Add tool loops and retries |
The calculator supplies the current dollar estimate; the prose does not freeze a price that can expire. Count cached reads separately from uncached input and budget cache creation rather than treating every repeated token as a free hit.
GPT-6 Luna’s higher long-context rates apply above 272,000 input tokens to the entire request, not just the excess. A monthly token total does not tell you how many requests crossed that threshold. Compare short and long requests separately, including cached reads, writes, and output.
Sonnet also distinguishes cache writes from reads, with different cache durations. A large advertised context window is a capacity limit, not a promise that sending the entire repository is the cheapest strategy.
Practical cost controls
Keep stable instructions and tool schemas before changing task details to improve cache reuse. Retrieve relevant passages rather than resending every document. Bound output length, tool rounds, and retry counts.
For each model, record accepted results, uncached input, cached input, cache writes, output, tool fees, latency, and reviewer minutes. Compare total spend divided by accepted results, including failed attempts.
Start with a shadow pilot and keep a rollback route. Check model-specific request and tool behavior before treating a successor as a drop-in replacement. The cached-token guide explains why a warm-cache demo can understate cold-start costs.
If routine tasks still cost too much, compare an open-model route on Novita under the same acceptance rubric. Verify its exact model, rate, and caching policy separately; a hosted alternative is not the same route as either first-party API.
Affiliate disclosure: AI Pricing Guru may earn a commission from the sponsored link at no extra cost to you. It does not affect this comparison.
FAQ
Is GPT-6 Luna cheaper than Claude Sonnet 5.5?
Yes at equal standard short-context token volumes. Task-level savings still depend on output length, retries, tools, and acceptance rate.
Does Sonnet’s higher price make it a poor choice for coding?
Not necessarily. It can be the cheaper completed workflow if your own tests show sufficiently fewer failures or less human correction.
Is the old Sonnet introductory-price warning still useful?
No: that dated warning is not a current buying baseline. Use the live table and current provider documentation instead.
Can I compare bills using only monthly token totals?
Not reliably when request size or cache state differs. Separate long-context requests, cache reads and writes, outputs, and tool charges.
Does a chat subscription include these API tokens?
Do not assume so. Subscription access, quotas, and API metering are separate buying routes whose terms must be checked independently.
Bottom line
Start with Luna for cheaply verified volume work, test Sonnet 5.5 where correctness is harder to achieve, and keep GPT-6.1 Sol as an escalation control. Promote the route that lowers cost per accepted outcome, not the one with the most impressive anecdote.
Official sources checked October 15, 2026: OpenAI’s API pricing and Anthropic’s Sonnet 5.5 model overview. Standard price rows were checked against the maintained dataset; this refresh does not announce a same-day price cut or an independent performance benchmark.