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Provider pricing and uptime pages are useful inputs, not neutral head-to-head benchmarks. Your decision should be based on cost per successfully completed task, quality on your own examples, time-to-first-token and end-to-end latency, and the way each API behaves when it is throttled, overloaded, or unavailable.
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What you are actually comparing
“Cost,” “speed,” and “reliability” each contain several different measurements. Define them before testing so a cheap but inaccurate model, or a fast model that fails under load, cannot appear to win by using a narrower definition.
Cost
Cost is the total amount paid to complete a defined workload. It can include input tokens, cached input, cache writes, output tokens, context-related charges, tool or platform fees, service-mode premiums or discounts, geography effects, and retries. A headline input-token rate is only one line in that calculation. OpenAI lists multiple token categories and processing details in its pricing documentation. Google documents different prices for its inference modes, while Anthropic notes that endpoint and inference geography affect pricing; check the providers’ current pages immediately before committing budget.
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Speed
Measure at least two clocks: time to first token (TTFT) and end-to-end completion time. TTFT describes how quickly a streamed response begins; completion time describes when the full, usable result arrives. A third useful view is throughput, such as output tokens per second, but it should not replace the two user-facing timings. Report the median and tail latency, not only the fastest run.
Reliability
Reliability has a model dimension and a service dimension. Model reliability is whether the answer is correct, complete, policy-compliant, and valid in the format your application requires. Service reliability is whether requests are accepted, finish before timeout, survive rate limits, and recover through retries or fallbacks. A provider’s uptime commitment applies only to its named tier, scope, and measurement terms; it does not prove that generated answers are correct.
Define the workload and quality bar first
Write a one-page workload specification before you send a single comparison request. It prevents accidental advantages caused by giving one model shorter prompts, lower output limits, or a quieter traffic pattern.
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- Inputs: record the normal, minimum, maximum, and difficult input sizes. Include malformed data, ambiguous requests, long context, and edge cases that have caused incidents.
- Outputs: specify the required length, schema, citations, language, and refusal behaviour. Set the same output limit for every candidate unless a model cannot support the required format.
- Traffic: state requests per minute, daily volume, burst size, peak concurrency, and whether traffic is interactive or batch.
- Operating conditions: fix the region, network path, service tier, streaming setting, timeout, retry policy, tool availability, and model version.
- Acceptance rules: define what counts as a successful task before reviewing results. Separate objective checks, such as valid JSON or a correct database key, from human-scored criteria such as helpfulness.
Use a versioned test set that mirrors production rather than a collection of easy demonstration prompts. Keep a private holdout set for the final check so a prompt or routing change cannot be tuned to the visible examples.
Score quality consistently
For objective tasks, write automated assertions: exact match where appropriate, field-level accuracy, schema validation, citation presence, safety checks, and tool-call success. For subjective tasks, use a written rubric with fixed scales and examples. Blind evaluators to model identity when practical, randomise answer order, and record disagreements instead of silently averaging them away.
Official documentation explains how to evaluate external models, but it does not prescribe one quality rubric for every application. OpenAI’s external-model evaluation guidance is at developers.openai.com/api/docs/guides/external-models. Treat the rubric as an application decision, not a property supplied by a vendor.
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Calculate comparable cost
For each candidate configuration, estimate the bill for the same period and then divide it by useful work completed. A simple worksheet can use this structure:
period cost = input-token cost + cached-input cost + cache-write cost + output-token cost + context/tool/platform fees + retry cost
cost per accepted task = period cost / number of tasks that pass your quality and validity checks
Use measured token counts from representative requests rather than an average copied from a marketing example. Keep separate columns for uncached input, cached input, cache writes, and output. If a provider charges for context or tools, include those lines even when they are zero for another model.
Include service mode, geography, and failures
Run a separate cost scenario for every service condition you might deploy. A discounted asynchronous mode may be ideal for overnight processing but unsuitable for an interactive screen. A priority tier may add capacity charges or commitments. Anthropic’s pricing documentation describes endpoint and inference-geography effects; compare the same geography where the products allow it and record any unavoidable difference in the worksheet. The current page is platform.claude.com/docs/en/about-claude/pricing.
Count billable retries and failed requests. A timeout followed by a successful retry can consume twice the tokens, and a request that returns invalid JSON may still be chargeable even though your application rejects it. Model the retry policy you will actually use, including exponential backoff, maximum attempts, and whether a fallback model receives the request.
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Use a useful unit
Report at least three cost views:
- cost per request, for budgeting raw traffic;
- cost per accepted task, after quality and schema checks; and
- projected monthly cost at normal and peak volumes.
Cost per accepted task exposes hidden quality differences. If one model needs two attempts to produce valid output while another succeeds once, their effective prices are not their displayed token rates.
Handle changing prices carefully
Record the price-page URL, retrieval date, model identifier, region, and service mode beside every rate. Prices, promotional periods, model eligibility, and lifecycle dates change. For example, OpenAI’s pricing page states that promotional pricing for gpt-5.6-sol is available at least through November 21, 2026. That is a provider-specific statement about that offer, not a forecast for other models or a guarantee beyond the stated date.
Measure speed under production-like load
Use the same prompt bytes, context, output cap, temperature or equivalent settings, streaming choice, region, and service tier for every candidate. Warm up connections before recording results, then repeat enough times to reveal normal and tail behaviour at the concurrency you expect in production.
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Record the right timestamps
- Capture request start at the client.
- Capture the first response byte and the first generated token separately when your protocol exposes both.
- Capture the timestamp of the final token and the time your application finishes validation or tool processing.
- Store status code, model version, service tier, region, token counts, retry count, and request identifier with each sample.
Calculate TTFT, generation time, and end-to-end time. Report p50 (median), p90, p95, and p99 when the sample is large enough; include the sample count and test window. A single mean can hide the slow requests that determine whether a chat interface feels responsive.
Streaming is a presentation choice, not proof of faster completion
Streaming can make an interface feel quicker because text appears before the answer is complete. It does not prove that the model finished sooner. Compare both streamed TTFT and final completion time, and keep streaming settings identical across candidates.
Vendor speed figures are claims tied to their own configuration. OpenAI says its Fast mode can provide up to 2.5x faster speeds for gpt-5.6-sol; retain the model and tier scope when citing that figure, and do not treat it as evidence that the service beats another provider on your workload. The product page is openai.com/api-fast-mode.
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Test tails and concurrency
Run a low-concurrency test to establish a baseline, then repeat at expected average and peak concurrency. Keep the request mix realistic: long-context requests, tool calls, and large outputs often have different latency from short prompts. Note queue time separately when the service mode exposes it. A mode that is fast at one request at a time may develop long tails under a burst.
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Application and answer reliability
For every completed response, record whether the task passed your acceptance rules. Useful fields include factual accuracy, required-field completeness, valid structured output, correct tool arguments, refusal correctness, and human rubric score. Publish the denominator: “92 of 100 test cases passed” is more informative than “92% reliable” without a test-set description.
API and service reliability
Track successful responses, HTTP errors, timeouts, connection failures, rate-limit responses, overload responses, malformed or truncated streams, and retries. Measure recovery separately: did the next attempt succeed, did a fallback succeed, or did the task fail permanently? Keep an incident log with timestamp, region, concurrency, tier, and provider status information.
Compare availability commitments only after reading their definitions. Anthropic’s service-tier documentation says Priority Tier “targets 99.5% uptime with prioritized computational resources.” The target applies to that Priority commitment, not automatically to Standard traffic or to answer quality. The documentation also says requests beyond committed Priority capacity fall back to Standard. See platform.claude.com/docs/en/api/service-tiers.
Define your own error budget from user impact. For example, you might allow a small percentage of background jobs to retry but require a much lower failure rate for an interactive payment step. Do not convert a provider’s contractual percentage into a prediction of your application’s observed success rate.
Understand service modes before comparing providers
Service modes can change price, queueing, latency, and capacity enough to reverse a model choice. Compare equivalent conditions where possible, and label non-equivalent results rather than placing them in one leaderboard.
| Provider or mode | Documented characteristic | How to use it in a comparison |
|---|---|---|
| Google Flex | Google’s 2026 documentation describes a 50% discount from listed full price and latency targets of 1–15 minutes. | Use for work that can tolerate asynchronous delay; measure queue and completion time separately and verify model eligibility. |
| Google Batch | Google documents a 50% discount from listed full price for Batch. | Compare total job completion time and failure recovery, not interactive TTFT. |
| Google Priority | Described as a synchronous option for production-facing use. | Test at your intended concurrency and record the current price and capacity terms. |
| Google Caching | Google documents a 90% discount for caching plus prorated token-storage charges. | Measure the proportion of eligible cached tokens and add storage charges; savings depend on reuse. |
| Google Standard | Standard is listed as an inference mode, but the cited optimization page does not establish one universal price or latency figure. | Use the current model-specific page for rates and measure your own latency distribution. |
| Anthropic Standard | Standard is the ordinary service path described alongside Priority. | Record its actual errors, queueing, and recovery under your workload rather than inferring performance from Priority commitments. |
| Anthropic Priority | Targets 99.5% uptime with prioritized resources; requests above committed capacity fall back to Standard. | Include commitment size, fallback frequency, and the resulting Standard latency in your reliability log. |
| OpenAI Fast mode | OpenAI claims up to 2.5x faster speeds for gpt-5.6-sol in the supported Fast-mode configuration. | Retest the claim with matched prompts, output limits, region, and concurrency; treat it as a vendor claim, not a cross-provider benchmark. |
Google describes its optimization choices this way: “The Gemini API offers a variety of optimization mechanisms to help you balance speed, cost, and reliability based on your specific workload needs.” The page was last updated October 9, 2026 UTC. Read the current Gemini optimization documentation for model-specific eligibility and terms.
Build a repeatable evaluation run
- Freeze the candidate list. Record exact model names, versions, endpoint, region, service mode, and date. Do not silently substitute a newer alias halfway through a run.
- Freeze prompts and settings. Store system and user messages, tool definitions, sampling settings, output limits, safety settings, and cache configuration in version control.
- Prepare a labelled test set. Include normal traffic, long inputs, difficult cases, known failures, and adversarial or malformed requests. Keep a holdout set unseen during tuning.
- Define scoring before execution. Create automated validators and a human rubric. Decide how partial credit, abstentions, refusals, and invalid outputs are treated.
- Run a pilot. Send a small sample to catch authentication, schema, quota, and logging errors. Exclude pilot timings from the main statistics.
- Run repeated trials. Use identical request order or a randomised order that is shared across candidates. Repeat at low, normal, and peak concurrency.
- Capture complete telemetry. Log token categories, price configuration, timestamps, status codes, retries, fallback path, response validity, and quality scores. Remove sensitive payloads or redact them according to your data policy.
- Calculate distributions and totals. Produce p50/p95/p99 TTFT and completion time, success and validity rates, cost per request, cost per accepted task, and projected spend.
- Review failures manually. Classify them as prompt ambiguity, model error, invalid format, timeout, rate limit, provider error, client bug, or evaluator error. Fix harness defects before ranking candidates.
- Repeat after changes. A prompt, model update, region change, tier change, or pricing change invalidates the old comparison. Keep prior results for trend analysis but label them with their original conditions.
Turn measurements into a decision
Use hard thresholds before weighted scores. Set the minimum task-pass rate, maximum p95 completion time, maximum acceptable service-error rate, data-handling requirements, and monthly budget. Eliminate any candidate that misses a non-negotiable threshold.
Among the remaining candidates, compare cost per accepted task, p95 and p99 latency, and failure recovery. A weighted score can help when trade-offs are unavoidable, but publish the weights and raw measurements so a different team can reach a different conclusion transparently.
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Routing can be sensible when validated rather than assumed. A lower-cost model may handle routine classification while a stronger model handles ambiguous or high-impact cases. Test the router itself: measure misroutes, extra latency, fallback frequency, and the combined cost per accepted task. Keep a safety fallback for outages and rate limits, and verify that the fallback preserves required output format and data controls.
Use a comparison worksheet
| Field | Value to record |
|---|---|
| Model and version | Exact identifier and release date |
| Endpoint and geography | URL or region used for every run |
| Service mode | Standard, priority, batch, flex, caching, fast, or other named mode |
| Prompt and output profile | Input-token range, cached-token share, output cap, tool use, streaming |
| Quality | Tasks passed, structured-output validity, rubric score, and holdout result |
| Speed | Sample count, TTFT p50/p95/p99, completion p50/p95/p99 |
| Service reliability | Success rate, status-code breakdown, timeout rate, rate limits, recovery rate |
| Economics | Cost per request, cost per accepted task, monthly projection, retry and fallback cost |
| Evidence date | Price and documentation retrieval date, plus test window |
Common comparison mistakes and fixes
- Using a public leaderboard as the decision. Leaderboards rarely match your prompts, tools, region, or quality bar. Fix this by running a private representative set.
- Comparing token prices without token counts. Long context, output length, caching, and retries can dominate the bill. Fix this with measured token-category columns.
- Reporting one latency number. A best-case or average hides queueing and tail behaviour. Fix this with repeated runs and percentile reporting.
- Calling uptime “reliability.” Availability does not establish answer correctness or schema validity. Keep service and application scorecards separate.
- Mixing service modes. Comparing an asynchronous discounted batch path with an interactive priority path is not an apples-to-apples speed test. Label the use case or rerun under equivalent conditions.
- Ignoring fallback behaviour. Capacity overflow, rate limits, and retries can change both cost and latency. Log every fallback and include it in the production estimate.
- Leaving versions implicit. Model aliases and terms change. Pin the version, date, and endpoint in every result.
- Trusting a stale price sheet. Recheck official pricing and lifecycle notices at decision time, especially when a promotion or model retirement is involved.
Keep the comparison current
Rates, model availability, geographic routing, service commitments, and performance change. Re-run a smoke test after a model-version change, pricing change, region change, or sustained incident, and schedule a full evaluation at an interval appropriate to your risk.
OpenAI’s external-model evaluation documentation currently says its Evals feature becomes read-only for existing users on October 31, 2026, and is scheduled to shut down on November 30, 2026. Those lifecycle dates are time-sensitive; confirm the current status before building a process that depends on that specific tool. The documentation is at developers.openai.com/api/docs/guides/external-models.
The practical outcome is a dated, reproducible decision record: the workload, quality bar, operating conditions, raw measurements, cost assumptions, failure classifications, and thresholds that produced the choice. That record is more durable than any universal ranking because it can be rerun when your traffic or the providers’ terms change.
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