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Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →A low price per token does not guarantee a low price for useful work. A cheaper model can consume more tokens, need extra attempts, fail the quality bar more often, or require more human review and rework. The useful comparison is the full cost of the workload divided by the number of original tasks that meet your agreed standard.
Why token price is not the same as task cost
Token pricing tells you what a provider charges for model input and output; it does not tell you how much it costs to finish a job successfully. A model that charges less per token may use more tokens to reach an answer, require several retries, or produce work that a person must correct. Failed attempts can still consume billed tokens and staff time.
OpenAI summarizes the model-level relationship this way: “At the model level, cost per successful task depends on price, the amount of compute used, and the likelihood of reaching the right result.” Its article also identifies employee time, review, retries, and rework as relevant to business cost. This is OpenAI’s framing, not an independent benchmark: A scorecard for the AI age.
How to calculate cost per completed task
First define what “completed” means for the work at hand. Set an acceptance bar for correctness and completeness, and include a deadline if timeliness matters. Then use:
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Cost per completed task = total cost attributable to the evaluation workload ÷ number of original tasks that pass the agreed quality bar
Count all billed attempts and retries in the total, including attempts that time out or fail. Add tool or retrieval charges, human review, and rework when they are part of the process being evaluated. State what is included so that one model’s result is not compared with another’s on a narrower cost basis. If the deadline is part of the acceptance bar, count only tasks that pass within it.
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Report the pass rate and latency alongside the cost ratio. A low cost per accepted task can still conceal a poor overall result if the model passes very few tasks, and latency matters when work must finish quickly. BEP Research’s benchmark page poses the practical question, “What does a completed AI task actually cost?” Its starter implementation does not publish hardware performance results, so it should not be treated as an empirical model comparison: Cost per Successful AI Task — BEP Benchmark Starter.
How to compare models fairly
Run each candidate on the same task set, with the same instructions, tools, grading rules, quality floor, deadline, and cost accounting. Record enough detail to explain the result and reproduce it:
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- Model and version, plus configuration and reasoning-effort settings.
- Input and output token counts, tool use, and billed attempts or retries.
- Pass or fail against the agreed quality bar, with the grading method.
- Cost per successful task, pass rate, and latency.
- Review and rework time or cost, if these are included in the business case.
Include routine work as well as difficult cases. Averages can hide expensive outliers: Anthropic documents an example in which two problems in a 20-problem research run accounted for 43% of spend. That is a result from that particular benchmark, not a general ratio. Anthropic recommends measuring cost per completed task on a team’s own traffic: Optimizing for cost and intelligence.
Re-run the evaluation when prices, model versions, or the mix of tasks changes. Where the sample size supports it, report uncertainty rather than presenting a small test as a precise forecast. A benchmark result cannot guarantee performance on a different organization’s prompts, tools, workload, or grading standard.
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What published comparisons show—and what they do not
Vendor and publisher benchmarks can illustrate why price and success rate belong together, but their figures are tied to particular tasks and configurations. Anthropic reports that, on a 478-problem SWE-bench Pro subset, Claude Fable 5.1 at low effort solved 88.6% of tasks at $0.54 per solved task, while Claude Sonnet 5 at default effort solved 77.4% at $0.84 per solved task. In another comparison on that subset, Claude Opus 5.5 at default effort and Claude Fable 5.1 at default effort scored 92.8% and 92.3%, respectively—described as within run-to-run noise—at $0.22 and $1.19 per solved task. These are Anthropic-published, configuration-specific results, not an independent purchasing recommendation; details are in its benchmark documentation.
InferOps’ May 2026 benchmark snapshot provides a different example: it reports canonical quality of 0.881 at $0.000557 per task for its gpt-5.4-mini plus batch configuration, versus 0.935 at $0.004220 per task for its gpt-5.4 baseline. The publisher says the snapshot covered 1,280 scored responses and cautions that prices and capabilities change. Treat these numbers as one dated publisher benchmark, not an expected result for another workload: LLM Cost-Optimisation Benchmark v1.
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Microsoft’s documentation says its cost benchmarks measure actual input, reasoning, and output token consumption during benchmark execution rather than estimating cost from token prices alone. That is a benchmark accounting method, not a promise about a user’s own costs: Model benchmarks and leaderboards in Microsoft Foundry.
Falling token prices do not settle the economics
Stanford HAI’s 2025 AI Index reports that the price for a model reaching GPT-3.5-equivalent MMLU performance fell from $20.00 per million tokens in November 2022 to $0.07 per million tokens by October 2024, a greater-than-280-fold reduction over about 18 months. The report’s price series used data from Artificial Analysis and Epoch AI. This is a historical, fixed-performance token-price comparison—not a current quote or a cost-per-completed-task measure: Artificial Intelligence Index Report 2025, Chapter 1.
A September 2, 2026 paper assembled posted-price observations across providers and models and joined them to benchmark scores. Its authors report that matched-model and quality-adjusted price indices show different trends, and that their measured buyer price per completed task stopped falling as reasoning-token consumption rose faster than token prices declined. These are findings specific to the authors’ data and index methods, not universal settled facts: The Price of Intelligence: A Quality-Adjusted Price Index for AI Services.
How to choose for your workload
There is no universal cheapest or best model in these comparisons. Choose based on the work you need done, not a headline token rate or a result from another organization’s benchmark. For each candidate, compare cost per accepted task with its pass rate, retries, latency, and any review or rework your process incurs. Check difficult and high-cost cases as well as the average, then make the choice against your actual quality and deadline requirements.
Quick Recap
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