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Items marked (preview) in this article are currently in public preview. This preview is provided without a service-level agreement, and we don't recommend it for production workloads. Certain features might not be supported or might have constrained capabilities. For more information, see Supplemental Terms of Use for Microsoft Azure Previews.
Model leaderboards (preview) in Azure AI Foundry portal allow you to streamline the model selection process in the Azure AI Foundry model catalog. The model leaderboards, backed by industry-standard benchmarks can help you to find the best model for your custom AI solution. From the model leaderboards section of the model catalog, you can browse leaderboards to compare available models as follows:
- Quality, cost, and performance leaderboards to quickly identify the model leaders along a single metric (quality, cost, or throughput);
- Trade-off charts to see how models perform on one metric versus another, such as quality versus cost;
- Leaderboards by scenario to find the best leaderboards that suite your scenario.
Whenever you find a model to your liking, you can select it and zoom into the Detailed benchmarking results of the model within the model catalog. If satisfied with the model, you can deploy it, try it in the playground, or evaluate it on your data. The leaderboards support benchmarking across text language models (large language models (LLMs) and small language models (SLMs)) and embedding models.
Model benchmarks assess LLMs and SLMs across the following categories: quality, performance, and cost. In addition, we assess the quality of embedding models using standard benchmarks. The leaderboards are updated regularly as better and more unsaturated benchmarks are onboarded, and as new models are added to the model catalog.
Quality benchmarks of language models
Azure AI assesses the quality of LLMs and SLMs using accuracy scores from standard, comprehensive benchmark datasets measuring model capabilities such as reasoning, knowledge, question answering, math, and coding.
| Index | Description |
|---|---|
| Quality index | Quality index is calculated by averaging applicable accuracy scores (exact_match, pass@1, arena_hard) over comprehensive, standard benchmark datasets. |
Quality index is provided on a scale of zero to one. Higher values of quality index are better. The datasets included in quality index are:
| Dataset Name | Leaderboard Category |
|---|---|
| arena_hard | QA |
| bigbench_hard | Reasoning |
| gpqa | QA |
| humanevalplus | Coding |
| ifeval | Reasoning |
| math | Math |
| mbppplus | Coding |
| mmlu_pro | General Knowledge |
See more details in accuracy scores:
| Metric | Description |
|---|---|
| Accuracy | Accuracy scores are available at the dataset and the model levels. At the dataset level, the score is the average value of an accuracy metric computed over all examples in the dataset. The accuracy metric used is exact-match in all cases, except for the HumanEval and MBPP datasets that use a pass@1 metric. Exact match compares model generated text with the correct answer according to the dataset, reporting one if the generated text matches the answer exactly and zero otherwise. The pass@1 metric measures the proportion of model solutions that pass a set of unit tests in a code generation task. At the model level, the accuracy score is the average of the dataset-level accuracies for each model. |
Accuracy scores are provided on a scale of zero to one. Higher values are better.
Performance benchmarks of language models
Performance metrics are calculated as an aggregate over 14 days, based on 24 trails (two requests per trail) sent daily with a one-hour interval between every trail. The following default parameters are used for each request to the model endpoint:
| Parameter | Value | Applicable For |
|---|---|---|
| Region | East US/East US2 | Standard deployments and Azure OpenAI |
| Tokens per minute (TPM) rate limit | 30k (180 RPM based on Azure OpenAI) for non-reasoning and 100k for reasoning models N/A (standard deployments) |
For Azure OpenAI models, selection is available for users with rate limit ranges based on deployment type (standard, global, global standard, and so on.) For standard deployments, this setting is abstracted. |
| Number of requests | Two requests in a trail for every hour (24 trails per day) | Standard deployments, Azure OpenAI |
| Number of trails/runs | 14 days with 24 trails per day for 336 runs | Standard deployments, Azure OpenAI |
| Prompt/Context length | Moderate length | Standard deployments, Azure OpenAI |
| Number of tokens processed (moderate) | 80:20 ratio for input to output tokens, that is, 800 input tokens to 200 output tokens. | Standard deployments, Azure OpenAI |
| Number of concurrent requests | One (requests are sent sequentially one after other) | Standard deployments, Azure OpenAI |
| Data | Synthetic (input prompts prepared from static text) | Standard deployments, Azure OpenAI |
| Region | East US/East US2 | Standard deployments and Azure OpenAI |
| Deployment type | Standard | Applicable only for Azure OpenAI |
| Streaming | True | Applies to standard deployments and Azure OpenAI. For models deployed via managed compute, or for endpoints when streaming is not supported TTFT is represented as P50 of latency metric. |
| SKU | Standard_NC24ads_A100_v4 (24 cores, 220GB RAM, 64GB storage) | Applicable only for Managed Compute (to estimate the cost and perf metrics) |
The performance of LLMs and SLMs is assessed across the following metrics:
| Metric | Description |
|---|---|
| Latency mean | Average time in seconds taken for processing a request, computed over multiple requests. To compute this metric, we send a request to the endpoint every hour, for two weeks, and compute the average. |
| Latency P50 | 50th percentile value (the median) of latency (the time taken between the request and when we receive the entire response with a successful code). For example, when we send a request to the endpoint, 50% of the requests are completed in 'x' seconds, with 'x' being the latency measurement. |
| Latency P90 | 90th percentile value of latency (the time taken between the request and when we receive the entire response with a successful code). For example, when we send a request to the endpoint, 90% of the requests are completed in 'x' seconds, with 'x' being the latency measurement. |
| Latency P95 | 95th percentile value of latency (the time taken between the request and when we receive the entire response with a successful code). For example, when we send a request to the endpoint, 95% of the requests are complete in 'x' seconds, with 'x' being the latency measurement. |
| Latency P99 | 99th percentile value of latency (the time taken between the request and when we receive the entire response with a successful code). For example, when we send a request to the endpoint, 99% of the requests are complete in 'x' seconds, with 'x' being the latency measurement. |
| Throughput GTPS | Generated tokens per second (GTPS) is the number of output tokens that are getting generated per second from the time the request is sent to the endpoint. |
| Throughput TTPS | Total tokens per second (TTPS) is the number of total tokens processed per second including both from the input prompt and generated output tokens. For models which do not support streaming, time to first token (ttft) represents the P50 value of latency (time taken to receive the response) |
| Latency TTFT | Total time to first token (TTFT) is the time taken for the first token in the response to be returned from the endpoint when streaming is enabled. |
| Time between tokens | This metric is the time between tokens received. |
Azure AI also displays performance indexes for latency and throughput as follows:
| Index | Description |
|---|---|
| Latency index | Mean time to first token. Lower values are better. |
| Throughput index | Mean generated tokens per second. Higher values are better. |
For performance metrics like latency or throughput, the time to first token and the generated tokens per second give a better overall sense of the typical performance and behavior of the model. We refresh our performance numbers on regular cadence.
Cost benchmarks of language models
Cost calculations are estimates for using an LLM or SLM model endpoint hosted on the Azure AI platform. Azure AI supports displaying the cost of standard deployments and Azure OpenAI models. Because these costs are subject to change, we refresh our cost calculations on a regular cadence.
The cost of LLMs and SLMs is assessed across the following metrics:
| Metric | Description |
|---|---|
| Cost per input tokens | Cost for standard deployment for 1 million input tokens |
| Cost per output tokens | Cost for standard deployment for 1 million output tokens |
| Estimated cost | Cost for the sum of cost per input tokens and cost per output tokens, with a ratio of 3:1. |
Azure AI also displays the cost index as follows:
| Index | Description |
|---|---|
| Cost index | Estimated cost. Lower values are better. |
Quality benchmarks of embedding models
The quality index of embedding models is defined as the averaged accuracy scores of a comprehensive set of standard benchmark datasets targeting Information Retrieval, Document Clustering, and Summarization tasks.
See more details in accuracy score definitions specific to each dataset:
| Metric | Description |
|---|---|
| Accuracy | Accuracy is the proportion of correct predictions among the total number of predictions processed. |
| F1 Score | F1 Score is the weighted mean of the precision and recall, where the best value is one (perfect precision and recall), and the worst is zero. |
| Mean average precision (MAP) | MAP evaluates the quality of ranking and recommender systems. It measures both the relevance of suggested items and how good the system is at placing more relevant items at the top. Values can range from zero to one, and the higher the MAP, the better the system can place relevant items high in the list. |
| Normalized discounted cumulative gain (NDCG) | NDCG evaluates a machine learning algorithm's ability to sort items based on relevance. It compares rankings to an ideal order where all relevant items are at the top of the list, where k is the list length while evaluating ranking quality. In our benchmarks, k=10, indicated by a metric of ndcg_at_10, meaning that we look at the top 10 items. |
| Precision | Precision measures the model's ability to identify instances of a particular class correctly. Precision shows how often a machine learning model is correct when predicting the target class. |
| Spearman correlation | Spearman correlation based on cosine similarity is calculated by first computing the cosine similarity between variables, then ranking these scores and using the ranks to compute the Spearman correlation. |
| V measure | V measure is a metric used to evaluate the quality of clustering. V measure is calculated as a harmonic mean of homogeneity and completeness, ensuring a balance between the two for a meaningful score. Possible scores lie between zero and one, with one being perfectly complete labeling. |
Calculation of scores
Individual scores
Benchmark results originate from public datasets that are commonly used for language model evaluation. In most cases, the data is hosted in GitHub repositories maintained by the creators or curators of the data. Azure AI evaluation pipelines download data from their original sources, extract prompts from each example row, generate model responses, and then compute relevant accuracy metrics.
Prompt construction follows best practices for each dataset, as specified by the paper introducing the dataset and industry standards. In most cases, each prompt contains several shots, that is, several examples of complete questions and answers to prime the model for the task. The evaluation pipelines create shots by sampling questions and answers from a portion of the data held out from evaluation.