Compare model tiers, not provider averages. GPT-6 Luna and Claude Haiku 5.5 are useful routine-work controls; GPT-6.1 Sol and Claude Sonnet 5.5 are a more demanding-work comparison. Their standard fresh-input and output prices match within each pair in the current dataset.
The chart, calculator, and table above read maintained pricing data at build time. This refresh removes the frozen GPT-5.6-era table and the expired Sonnet launch-pricing recommendation without changing the article URL.
Which provider should you choose?
| Buying need | First comparison | What decides |
|---|---|---|
| Routine extraction and classification | Luna versus Haiku 5.5 | Validation success and request size |
| Coding and complex tool use | Sol versus Sonnet 5.5 | Accepted changes, failed loops, and review time |
| Claude-native escalation | Sonnet versus Opus 5.5 | Whether fewer failures repay the premium |
| Existing OpenAI integrations | Keep an OpenAI control | Migration work and required tool behavior |
| Existing Claude integrations | Keep a Claude control | Workflow fit, permissions, and billing |
Choose OpenAI when its supported tools, modalities, or existing integrations are the requirement. Choose Anthropic when your application depends on Claude-native workflows or your tests favor its outputs. These are buying criteria, not an independent capability ranking.
For the complete catalogs, open OpenAI pricing and Anthropic pricing. The ChatGPT versus Claude comparison separates chat subscriptions from API spending.
Caching and long context can break the tie
Track fresh input, cached reads, cache creation, and output separately. A warm-cache result is not a cold-start budget, and cache reads do not establish the cost of creating a cache.
Haiku 5.5 has higher rates above 100,000 input tokens per request. Luna and Sol have a different boundary above 272,000 input tokens. These are request-level thresholds, not monthly allowances. Do not apply Haiku’s rule to every Claude model or assume that a large context window means flat pricing.
Use the cached-token guide to plan repeatable prefixes. Retrieve relevant passages and cap tool histories before paying to resend entire documents.
Compare cost per accepted result
Run the same held-out prompts with the same tools, output requirements, retry cap, and acceptance checks. Start the token calculator with these pilot volumes, then replace them with usage logs:
| Monthly workload | Fresh input | Output |
|---|---|---|
| Extraction | 10M tokens | 1M tokens |
| Support drafts | 5M tokens | 2M tokens |
| Coding review | 2M tokens | 1M tokens |
| Pricing data: How we verify → | ||
The calculator supplies current token-only dollar estimates. Add cache writes, paid tools, retries, failed runs, and reviewer time before dividing total spend by accepted results. Split long-context requests into their own scenario.
Practical routing recommendation
Start routine work on Luna or Haiku and escalate only the requests that fail a defined acceptance check. Compare Sol with Sonnet for the harder tail. Promote Opus only when the measured improvement offsets its added spend.
Do not migrate every request because a provider wins one demo. Log model IDs, token categories, latency, acceptance, and escalation reasons, and retain a rollback route.
For a third control on low-risk tasks, test a hosted open-model endpoint on Novita. Verify the exact host, model, rate, and data-handling terms independently.
Affiliate disclosure: sponsored links may earn us a commission at no extra cost to you. They do not affect the comparison.
FAQ
Is OpenAI cheaper than Anthropic?
Not across every model tier. The selected budget and balanced pairs match on standard fresh-input and output rates, while context tiers, cache behavior, and task outcomes can change total spending.
When is a premium Claude model worth it?
When your evaluation shows fewer failures, retries, or review minutes worth more than the extra model spend. A higher token rate alone is not evidence of better results.
Should I use one provider for everything?
Keep the simplest setup that meets quality, availability, and governance requirements. Add routing when measured savings justify the extra operational work.
Does prompt caching change the comparison?
Yes, when requests produce actual cache hits. Account for cache creation and request-length rules rather than pricing all input as cached.
Do ChatGPT or Claude subscriptions cover these calls?
Do not assume so. Subscription access and metered API billing are separate buying routes whose terms must be checked independently.