ProviderBench

This site compares LLM inference providers running the same models, giving teams a way to evaluate where a model runs rather than looking only at model-level performance. It is designed for developers and teams choosing between inference providers, GPU clouds, or specific routes who want a practical view of speed, cost, reliability, and model coverage before putting their own traffic through an endpoint.

The rankings bring performance, pricing, uptime, and coverage into the same view. By separating response start time from token generation speed and requiring shared-model measurements for comparable scores, the benchmark helps distinguish genuinely comparable provider performance from entries where the available data is thinner.

Key Features

Same-Model Benchmarking

Compare providers running identical models.

Using the same model across providers makes it easier to evaluate differences attributable to the inference provider rather than differences between model capabilities.

This provides a practical basis for comparing:

• Inference speed
• Provider routes
• GPU infrastructure
• Performance consistency

Multiple Speed Metrics

The benchmark separates different parts of the response experience rather than reducing performance to one speed number.

It tracks:

• Response time
• First-token speed
• Output token speed

This distinction matters because different workloads optimize for different things. For interactive chat, response start time can have a major impact on perceived responsiveness, while longer generations may make sustained output speed more important.

Pricing Context

Compare provider performance alongside published pricing.

The benchmark includes:

• Published catalog price
• Blended token pricing
• Cost context alongside performance

This makes it easier to identify cases where a low advertised rate may come with slower inference or weaker overall performance.

Reliability Signals

Uptime is shown alongside speed and pricing.

Rather than evaluating an endpoint purely on how quickly it responds, teams can also consider whether the provider has demonstrated reliable availability.

This gives the leaderboard a broader operational perspective for teams deciding where production inference should run.

Provider And Route Coverage

Review what each provider actually supports.

Coverage information includes:

• Available models
• Context size
• Exact OpenRouter routes

This helps teams determine not only which provider ranks well, but whether the provider actually supports the model and context requirements of their workload.

Ranking Rules

The rankings distinguish between directly comparable measurements and thinner datasets.

For comparable scores, the benchmark requires shared-model measurements across providers. This prevents providers from being ranked as though they had equivalent evidence when there is no common model data.

Entries with limited data are treated more cautiously.

This makes the leaderboard useful as a comparative benchmark without implying that every provider row has the same level of measurement coverage.

Provider-Specific Models

The benchmark also keeps providers that primarily offer their own models in the broader view.

When direct shared-model comparison is not possible, the site uses the provider's published catalog speed.

This allows the leaderboard to remain useful as a broader provider directory while making the distinction between directly benchmarked results and provider-published performance visible.

Built For LLM Infrastructure Decisions

AI Engineering Teams

Compare inference providers before deciding where production workloads should run.

Developers

Evaluate routes based on response speed, generation speed, price, and availability.

AI Infrastructure Teams

Compare GPU-backed inference options without relying on a single performance metric.

Teams Evaluating Providers

Shortlist providers based on the combination of model coverage, cost, speed, and uptime before conducting their own traffic tests.

Common Use Cases

Choosing An Inference Provider

Compare multiple providers running the same model to identify meaningful performance differences.

Optimizing Chat Responsiveness

Focus on response start time and first-token speed when interactive latency matters most.

Evaluating Generation Throughput

Use output token speed when longer completions make sustained generation performance more important.

Comparing Cost And Performance

Look at pricing alongside speed to avoid selecting an endpoint solely because its published token rate is lower.

Checking Provider Reliability

Use uptime alongside performance and cost when evaluating potential production infrastructure.

Comparing OpenRouter Routes

Review exact routes, supported models, and context sizes when deciding between available inference paths.

Broader Provider Coverage

The leaderboard is not limited to providers that can be directly compared on every model. Providers with proprietary or otherwise unique models can still appear using published catalog speed where direct benchmarking is unavailable.

The distinction is important: not every ranking represents the same measurement basis. Shared-model results provide the stronger basis for direct provider comparisons, while provider-specific entries provide broader coverage with less directly comparable evidence.

Why It Matters

Choosing an inference provider is not simply a matter of finding the fastest model. The same model can behave differently depending on where it is hosted, which route serves it, and how reliable that endpoint is.

This benchmark brings those provider-level variables into one comparison. Response latency, first-token speed, output throughput, pricing, uptime, model coverage, context size, and routing information can be evaluated together instead of being gathered from separate provider pages.

The result is a more practical way to shortlist inference infrastructure while keeping the limitations of the underlying measurements visible.

Compare Where Your Models Run

Evaluate LLM inference providers using the same models and compare response time, first-token speed, output speed, pricing, uptime, model coverage, context size, and exact routes. With shared-model ranking rules and more cautious treatment of thin data, the benchmark provides a practical starting point for choosing inference infrastructure before testing with your own traffic.