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The only defensible way to compare LLM inference providers is to measure the workload you actually plan to run. There is no universal “fastest” or “cheapest” API: results change with prompt and completion lengths, model version, region, route, account tier, cache state, concurrency, and price schedule.
A useful benchmark produces a workload-specific performance and cost curve. For every load level, record time to first token (TTFT), streamed-token pacing, end-to-end latency, completed requests per second, aggregate output tokens per second, failures and throttling, and cost per successful task. Publish exactly what was sent, where it was measured, which route handled it, and how load was applied.
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What a provider benchmark should answer
Before collecting numbers, write down the decisions the benchmark must support. A production team usually needs answers to these questions:
- How responsive is the service when a user is waiting for the first streamed output?
- How quickly does output continue after that first token?
- What end-to-end latency should a request of a particular output length expect?
- How many requests and output tokens can the service complete at each concurrency or arrival rate?
- What error, timeout, throttling and retry rates occur under that load?
- What does a successful task cost after applying the actual input, output, cache and other billable usage?
- Which geography, endpoint, model version, account tier and data-handling conditions make a route eligible for your application?
The result is a set of operating points, not a league table. A service that has the lowest idle latency can become more expensive or less reliable when concurrency rises. A high aggregate token rate can coexist with an unacceptable wait for an individual request.
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Define the latency metrics before testing
Metric names are not perfectly consistent across benchmarking tools. Put the measurement point and formula in your report, then use the same definitions for every provider.
Time to first token (TTFT)
TTFT is client-side elapsed time from sending a request until the first streamed output arrives. The vLLM serving benchmark documentation uses this definition. Report at least p50 and p90 or p95; include p99 when the sample is large enough to make that tail meaningful. State the sample count and failures alongside each percentile.
TTFT captures queueing, admission, prompt processing, network travel and any provider-side setup before the first event. It is the most visible metric for chat interfaces, but it does not describe the pace of the rest of the completion.
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Inter-token latency (ITL)
ITL is the elapsed time between successive streamed output events. A streamed event is not guaranteed to represent exactly one token: batching, transport behavior and speculative decoding can produce events containing multiple tokens. Therefore, document how the client counts events and tokens rather than assuming one event equals one token.
Time per output token (TPOT)
vLLM calculates TPOT per request as:
(end-to-end latency − TTFT) / (output tokens − 1)
It then aggregates those request-level values. TPOT and ITL can describe related behavior but are not interchangeable unless their event and token-counting rules are reconciled. If a tool reports only one of them, preserve the tool’s definition in your comparison.
End-to-end latency
End-to-end latency is client-side elapsed time until the complete response is received. Always pair it with output length: a 200-token completion naturally takes longer than a 20-token completion even when generation speed is identical. Report a distribution, not only an arithmetic mean, and say whether failed or retried requests are included.
Measure throughput under a stated load
Throughput has meaning only with a load condition and a timing window. Report both completed requests per second and aggregate output tokens per second, together with concurrency or offered request rate.
NVIDIA defines total tokens per second as total output-token throughput across simultaneous requests; its measurement window runs from the first request through the last response. Other tools can use different windows, so copy the exact interval into your methodology. The NVIDIA benchmarking concepts guide, GuideLLM metrics reference and NVIDIA LLM metrics documentation provide useful definitions to reconcile.
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Never publish a bare “tokens per second” figure. Specify all of the following:
- Whether the figure is per request or aggregate across simultaneous requests.
- Whether it counts output tokens only or includes input tokens.
- The concurrency or arrival-rate target that produced it.
- The measurement interval and whether warm-up traffic is excluded.
- The model, version, route, region and account tier.
- Successful, failed, timed-out and retried request counts.
Goodput and reliability
Goodput is throughput that meets a declared service objective, such as a TTFT and end-to-end latency limit while succeeding without a retry. Define that objective before testing. Count errors, timeouts, throttles and retries; do not silently remove failed requests from the denominator. The reviewed tools do not establish one universal goodput definition, so your report must state its own rule.
Build a production-like workload
Provider comparisons are fair only when the requests represent the same intended use. Create a workload specification before choosing a service.
Prompt and completion distributions
Use a corpus or generator that reflects production prompt lengths, system-message sizes, conversation history, and expected completion lengths. Preserve the distribution rather than benchmarking one convenient prompt. If your application has several request classes, keep them as separate mixes and report each class as well as the aggregate.
Request behavior
Record sampling temperature, top-p or other decoding settings, maximum output tokens, stop conditions, random-seed behavior where supported, streaming on or off, tool calls, structured-output requirements and multimodal inputs. These settings can change both latency and token counts.
Shared prefixes and cache state
If requests reuse a system prompt or other prefix, include that pattern deliberately. Run separate cold-cache and warm-cache conditions when both occur in production. vLLM warns that repeated runs against the same server can reuse prefix-cache prompts and inflate measured throughput; document how you reset, isolate or intentionally warm the cache.
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For a same-model comparison, record the exact model identifier, provider-specific variant and quantization, if any. “Model family” is not precise enough when revisions or serving configurations differ.
If the providers expose different models, call the exercise a service comparison rather than attributing every difference to infrastructure. Model architecture, tokenizer, safety layer, context handling and default decoding can all affect the result.
Capture the route details that can change performance or eligibility:
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- Test date and UTC time window.
- Client location and provider region.
- Endpoint, deployment or cluster route.
- API plan, quota and account tier.
- Client-library, runtime and benchmark-tool versions.
- Request IDs and response headers when the provider supplies them.
- Data-retention, residency, logging and other operational constraints.
A defensible benchmark protocol
- Write the workload contract. Specify prompt and output-length distributions, request mix, streaming mode, tools, shared-prefix behavior, sampling parameters and success criteria.
- Freeze the comparison unit. Pin model identifiers and versions. If a provider offers a variant or quantization, record it rather than treating it as an invisible implementation detail.
- Instrument the client clock. Timestamp request send, first streamed event, every subsequent event and final response. Record token counts from the response metadata or a documented tokenizer, plus status code, error type and retry history.
- Record the route and environment. Save geography, endpoint, account tier, software versions, network location and provider request IDs. Keep these values with every sample or an immutable run manifest.
- Warm up consistently. Use the same warm-up policy for every service. State whether model loading, connection establishment and cache priming are included or excluded. Do not mix cold and warm samples in one percentile.
- Run a baseline. Start at low concurrency or a low offered rate to expose intrinsic responsiveness without a queue. Keep the workload and parameters unchanged.
- Sweep load. Increase concurrency or arrival rate through several levels until latency, error rate or throttling becomes unacceptable. Hold each level long enough to observe steady behavior and use the same duration for each provider where practical.
- Repeat and randomize. Repeat each condition on separate runs. Randomize provider order when practical to reduce time-of-day and network bias. Document cache reset or cache-warm conditions for every repetition.
- Retain every observation. Keep attempted, successful, failed, timed-out and retried requests. Never publish only the successful subset without explaining the denominator.
- Check prices at test time. Save the official rate-card URL and price-check date. Record input, output, cache-read, cache-write, tool, long-context and other applicable rates before calculating cost.
- Plot the operating curve. At each load point, plot latency percentiles against achieved throughput, annotate reliability, and show cost per completed request or task. Select the region that meets your latency and reliability constraints at an acceptable cost.
Summarize samples without hiding variation
For each condition, publish sample count, attempted requests, successful requests, retries, failures and throttles. Show p50, p90 or p95 and p99 latency where sample size supports the tail. Include a confidence interval or run-to-run variation measure when you can justify one; at minimum, show the spread across repeated runs.
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Calculate cost per request and per successful task
Advertised token prices are inputs to a calculation, not a task cost. For an ordinary token-metered request, start with:
request cost = (input tokens × input rate) + (output tokens × output rate)
Use matching units for token counts and rates. Apply the actual measured counts for each request class, then aggregate to cost per completed request or business task. Include separately billed cache reads or writes, tools, images, audio, long-context tiers and other modalities when they apply.
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Caching changes both sides of the equation
Anthropic’s pricing documentation states: “Prompt caching reduces costs and latency by reusing previously processed portions of your prompt across API calls.” See its pricing documentation for the provider’s current cache treatment. Measure cache-hit and cache-miss conditions separately because a lower input charge can coincide with a different TTFT.
Self-hosted economics
For self-hosted inference, add accelerator rental or amortization, utilization, idle time, storage, networking, power where relevant, and operator overhead. Divide those costs by the completed workload, not by theoretical peak tokens. A June 2026 preprint, “Beyond Per-Token Pricing: A Concurrency-Aware Methodology for LLM Infrastructure Cost Estimation,” reports H100 infrastructure costs from $0.21 to $15.25 per million output tokens across its studied scenarios and an underutilization penalty of 2.5–24× across stated low-to-moderate enterprise loads. Those are scenario-specific findings, not provider API prices or universal estimates; your utilization and concurrency assumptions must be shown.
Compare providers on a single decision table
Build one row for every provider, model and load condition, or create a separate table for each workload class. A useful schema is:
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| Field | What to report |
|---|---|
| Identity | Provider route, exact model/version, region, account tier and test date |
| Workload | Prompt and output distributions, streaming mode, sampling and cache condition |
| Load | Concurrency or offered rate, run duration and measurement window |
| Latency | TTFT p50/p95/p99, ITL or TPOT definition, end-to-end percentiles and output-length context |
| Throughput | Completed requests per second and aggregate output tokens per second |
| Reliability | Attempts, successes, failures, timeouts, throttles and retries; goodput rule |
| Cost | Input/output/cache/tool rates, measured token mix, cost per request and cost per successful task |
| Eligibility | Residency, retention, compliance and operational constraints |
Use the same load points across providers so the curves are comparable. If one service cannot sustain a target rate, report the achieved rate and the reason instead of filling the cell with a peak number from a different condition.
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Find the sustainable operating region
At low load, compare TTFT and end-to-end percentiles. As load rises, look for the point where p95 or p99 latency bends upward, throttles appear or goodput falls. That knee is often more useful for capacity planning than the maximum token rate.
Separate interactive and batch decisions
An interactive assistant may prioritize TTFT and tail latency, accepting a lower aggregate rate. A batch summarization pipeline may accept higher TTFT if aggregate output tokens per second and cost per task improve. Use the same raw observations but choose different constraints for each workload.
Make cost comparable to delivered work
Plot cost per successful task at the same achieved throughput and reliability target. A lower output-token rate can still win if it returns shorter, sufficient answers or has a cheaper cache-hit mix; conversely, a low token price can lose once retries, long contexts or idle self-hosted capacity are included.
Common benchmark failures and fixes
Publishing one tokens-per-second number
Why it fails: It omits whether the figure is per request or aggregate, output-only or input-plus-output, and the load and timing window.
Fix: Publish both request and output-token throughput with concurrency or offered rate, interval, model, route and success counts.
Mixing incompatible metric definitions
Why it fails: One tool’s TTFT, ITL, TPOT or throughput window may not match another’s.
Fix: Copy each tool’s formula into the methods section, recompute from raw timestamps where possible, and label any metric that cannot be reconciled.
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Why it fails: Different prompt lengths, output caps, sampling settings, regions or model revisions can dominate the result.
Fix: Use one workload manifest and disclose every unavoidable difference. Call the result a service comparison when models differ.
Letting cache reuse inflate a run
Why it fails: Repeated prompts can hit a prefix cache and make later runs look faster or more productive.
Fix: Separate cold and warm conditions, reset or randomize prefixes when measuring cold behavior, and state the cache policy.
Dropping failures and retries
Why it fails: Removing bad requests makes latency and cost look better while hiding capacity limits.
Fix: Keep attempted and completed denominators visible, count billed retries, and define goodput before the run.
Treating a rate card as a task estimate
Why it fails: Token mix, cache treatment, tools, long-context pricing and retries determine actual spend.
Fix: Apply contemporaneous rates to measured token counts and publish the formula, assumptions and price-check date.
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Readers should be able to reproduce the comparison from the published methods. Include:
Quick Recap
- A workload manifest with representative prompts, length distributions and decoding parameters.
- A run manifest containing date, UTC window, client location, route, model/version, account tier and software versions.
- Load levels, warm-up policy, cache condition, duration and measurement window.
- Metric definitions for TTFT, ITL, TPOT, end-to-end latency, throughput and goodput.
- Per-request fields for timestamps, token counts, status, error, throttle and retry.
- Percentiles, sample counts and variation for every condition.
- Price sources, rates, cache and tool assumptions, and cost per request and successful task.
- Plots or tables showing latency versus achieved throughput and cost at the same operating points.
- A conclusion tied to explicit constraints, such as “meets p95 TTFT below X at Y requests per second,” rather than a provider-wide winner.
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