See what switching AI models does to your API bill. Compare the same workload across two models and estimate monthly and annual cost impact. Optionally compare cost per successful task, save this workload in your browser, and reopen it later to recalculate with currently available pricing.
Compare in three steps
1
Choose both modelsCurrent route first, target route second.
2
Use one workloadThe same tokens and volume price both sides.
3
Review the bill changeSee monthly impact beside verified sources.
Verified pricing
Compare the same token workload
USD · independently selected verified modes · same workload on both models
Verified prices only
Both selected models have verified cached-input read pricing for every eligible tier. Cache-write tokens are priced separately when the selected tier exposes verified cache-write pricing.
Comparison workload
Current model
OpenAI GPT-6.1 SolModel ID: openai/gpt-6.1-sol
Target model
Anthropic Claude Haiku 5.5Model ID: anthropic/claude-haiku-5-5
Monthly workload
1,000,000 requestsRequests per month; each request is one paid attempt in the basic comparison.
Input
1,000 tokens per requestInput-token assumption used for both selected models.
Output
500 tokens per requestOutput-token assumption used for both selected models.
Cached input
0% of input tokens0 cache-write tokens per request; cache read and write quantities cannot exceed total input.
Processing modes
Standard → StandardGlobal base pricing requested.
Current monthly cost
OpenAI GPT-6.1 Sol
Cost per request
$0.0070per request
Monthly cost
$7,000.00per month
Regular input$0.0020 / request
Cached inputNot used
Cache writeNot used
Output$0.0050 / request
Standard rates / 1M: input $2 · cached not used · cache write not used · output $10
Target monthly cost
Anthropic Claude Haiku 5.5
Cost per request
$0.00035per request
Monthly cost
$350.00per month
Regular input$0.0001 / request
Cached inputNot used
Cache writeNot used
Output$0.00025 / request
Standard rates / 1M: input $0.1 · cached not used · cache write not used · output $0.5
Monthly savings$6,650.00per monthThe target costs less for this workload.
Savings
95.00%
Annual savings
$79,800.00per year
For this workload, switching from OpenAI GPT-6.1 Sol (Model ID: openai/gpt-6.1-sol) to Anthropic Claude Haiku 5.5 (Model ID: anthropic/claude-haiku-5-5) changes estimated monthly API cost from $7,000.00 to $350.00, a savings difference of $6,650.00 per month and $79,800.00 per year.
Save this workload
Save your workload in this browser. Reopen it later to compare your saved estimate with currently available pricing. Clearing browser storage will remove saved workloads.
Saved workloads
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Verified prices used in this result
Model identity, processing mode, pricing tier, verification date, and official provider source.
Same token counts do not guarantee the same text volume, quality, latency, tokenizer behavior, or task success rate. This compares API billing, not the full business benefit or migration cost.
What this comparison includes
Each side uses one verified price record for its selected Standard or supported Ultrafast mode: the matching text input and output prices and the model's applicable context tier. The decision delta is Current cost − Target cost: positive means savings, negative means additional cost, and zero means no cost difference.
Compare cost per successful task
Open the optional outcome-aware section to add your current and alternative success-rate assumptions. Verified model prices do not verify those rates. It keeps the verified per-attempt API costs above, then compares cost per successful task, calculates the alternative success rate required to break even, and estimates equal-outcome monthly and annual impact. Token price alone does not determine the cheaper route. See how success rate can make the cheapest LLM cost more per successful task.
Cost per successful task = cost per attempt ÷ success rate.
Break-even alternative success rate = current success rate × alternative cost per attempt ÷ current cost per attempt.
Use success rates as decimal values in these formulas: 90% = 0.90.
What it deliberately leaves out
Cached-input reads are included only when both selected tiers expose reliable verified rates. Cache-write costs are included only when the selected pricing records expose reliable verified cache-write rates; otherwise no cache-write estimate is produced. Token assumptions stay fixed per call; growing multi-step context, external tool fees, and infrastructure are excluded. This version does not estimate Batch, Flex, Priority, Fast processing, quality, latency, tokenizer differences, contract discounts, or migration engineering. For a dedicated view of extra paid attempts, use the LLM Retry & Fallback Cost Calculator. If you are comparing the new OpenAI routes, read the September 2026 GPT-6 Sol and Luna pricing update. Review the source-linked AI API pricing dataset before changing production routing.
How does the AI model switch cost calculator work?
Choose a current model and a target model, enter the same input tokens, output tokens, and monthly request volume, and the calculator applies each model's verified rates for its selected Standard or supported Ultrafast mode to estimate the bill difference.
Does switching AI models always save money?
No. The result shows monthly and annual savings when the target is cheaper, or the additional monthly and annual cost when it is more expensive for the same workload.
How is cached input handled?
Cached input share uses each selected model's own verified cache-read price. If either selected pricing tier lacks a reliable cache-read rate, a non-zero cache scenario is blocked instead of assuming a discount.
Does this calculator measure model quality or migration effort?
No. The optional outcome-aware mode applies success rates that you provide, but it does not benchmark quality, latency, retries, or one-time migration engineering.
How do success rates change the model switch comparison?
Success rates convert each model's per-attempt API cost into cost per successful task. The calculator then estimates how many alternative attempts are needed to match the current monthly successful-task volume.
What is the break-even alternative success rate?
It is the minimum alternative success rate needed to match the current model's cost per successful task, based on the two per-attempt costs and the current success rate.
What does a break-even rate above 100% mean?
A required rate above 100% is not feasible under the entered assumptions. The alternative cannot match the current cost per successful task through success-rate improvement alone.
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