Top 22 LLM Performance Benchmarks for Measuring Accuracy and Speed

Choosing an LLM can feel like buying a computer blind: inference speed, latency, memory use and accuracy trade off against cost and deployment constraints. Within LLM Inference Optimization Techniques, LLM Performance Benchmarks give you clear measures throughput, latency, accuracy, cost per token and reproducibility that turn claims into comparable results. Which model meets your latency targets or delivers the best accuracy on your workload? This article shows how to quickly identify which LLMs perform best by using trusted benchmarks, so you can make confident, data-driven decisions when choosing or comparing models.

To help you get there, Inference offers AI inference APIs that run standardized benchmark suites and real world tests so you can compare models on the metrics that matter without building your own evaluation setup.

What are The Main Parameters That Define LLM Performance Benchmarks?

Accuracy

Accuracy covers task correctness across benchmarks and includes task accuracy, exact match, F1, recall, precision, BLEU, ROUGE, and perplexity. Accuracy also captures calibration and confidence reliability.

Efficiency

Efficiency covers latency, throughput, resource usage, cost per token, GPU utilization, memory footprint, and energy. Measure tail latency and p99 as well as steady state throughput and batching behavior. Efficiency also includes system-level considerations such as model size, sequence length handling, quantization, pruning, mixed precision, and distributed execution strategies.

Robustness

Robustness measures behavior under distribution shift, adversarial prompts, corrupted inputs, and long context. Test with out-of-distribution examples, stress tests, and adversarial evaluations. Report robustness using accuracy drop, robustness score, and failure mode breakdown.

Fairness

Fairness and safety measure bias, toxicity, privacy leakage, and differential performance across demographic groups. Use subgroup accuracy, false positive and false negative rates by cohort, calibrated equalized odds, and toxicity and safety classifiers. Human review plays a major role here.

Adaptability

Adaptability captures fine-tuning, domain adaptation, continual learning, and instruction following. Measure sample efficiency, few-shot and zero-shot accuracy, and stability under updates. Track transfer learning performance and catastrophic forgetting.

How Standard Benchmarks Work: MMLU, HellaSwag, DROP and the Scoring Toolbox

Benchmarks like:

• MMLU
• HellaSwag
• DROP

Standardized tests are probe-specific skills. They provide input sets, gold labels, and scoring rules. Scorers range from simple exact match and accuracy to semantic matching using other LLMs or human grading.

Some use string matching, others use normalized numerical comparison or programmatic evaluation for generated code and reasoning traces. Benchmarks vary in size from a few dozen samples to thousands, and different tests stress different model capabilities such as knowledge recall, commonsense, reading comprehension, or multi-step reasoning.

Which Benchmarks Target Which Skills: Mapping Tasks to Tests

• Reasoning and commonsense: HellaSwag, BIG Bench Hard, targeted subsets of MMLU. These focus on multi-step inference, cause and effect, and everyday knowledge.
• Language understanding and QA: MMLU, DROP, SQuAD variants. These evaluate reading comprehension, numeric reasoning in text, and exact match style answers.
• Coding: HumanEval, CodeXGLUE. They test code generation correctness, unit test passing, and synthesis from natural language.
• Conversation and chatbots: Chatbot Arena, MT Bench. These measures conversational coherence, follow-up question handling, instruction following, and user satisfaction.
• Translation: BLEU and newer metrics applied to translation corpora. Models are tested on fidelity and fluency across languages.
• Math: Math datasets and arithmetic reasoning sets measure step-by-step reasoning and numeric accuracy.
• Logic: Logic-oriented prompts and synthetic reasoning tasks check inductive and deductive skills.
• Standardized tests: SAT, ACT, and professional exam-style sets assess broad knowledge, reasoning, and domain competence.

Eight Core Benchmarks Across Four Critical Domains

• TruthfulQA: Truthfulness and resistance to hallucination in factual answers.
• MMLU: Language understanding and multi-domain question answering.
• HellaSwag: Commonsense reasoning in finishing scenarios.
• BIG Bench Hard: High difficulty reasoning tasks and creative problem solving.
• HumanEval: Code generation evaluated by unit tests.
• CodeXGLUE: Broader programming tasks and code intelligence benchmarks.
• Chatbot Arena: Human ranked ELO style head to head comparisons of conversation quality.
• MT Bench: Multi-turn conversational ability with complex instruction and role handling.

Key Quantitative Metrics for Benchmarking LLMs

• Accuracy or precision: Percent correct predictions on a labeled set.
• Recall: true positives divided by actual positives, useful in retrieval and detection.
• F1 score: Harmonic mean of precision and recall to balance false positives and false negatives.
• Exact match: Proportion of outputs that exactly match the gold answer, critical for QA and translation when form matters.
• Perplexity: Measures how well a model predicts tokens. Lower perplexity means better token prediction on the test distribution.
• BLEU: N-gram overlap metric for translation, comparing machine output to reference translations.
• ROUGE: Summary-oriented overlap metrics such as ROUGE N and ROUGE L for the longest common subsequence.
• Latency and throughput: Average and tail latency, tokens per second, and cost per token.
• Resource usage: GPU memory, CPU usage, network IO, FLOPs and energy consumption.
• Robustness metrics: Accuracy under distribution shift, adversarial success rate, and degradation curve.
• Fairness metrics: Subgroup accuracies, disparate impact ratios, and false positive disparities.
• Human evaluation scores: Coherence, relevance, factuality, helpfulness, and safety were graded by raters.

Practical Steps When Choosing Benchmarks for Your Project

• Align with objectives: Pick benchmarks that reflect the tasks your system must do. Ask which skills matter more: knowledge recall or multi-step reasoning.
• Embrace task diversity: Include multiple benchmarks spanning reasoning, QA, safety, and efficiency to avoid over-optimizing to one metric.
• Stay domain relevant: Add domain-specific tests or in-house datasets that reflect real user prompts and failure modes.
• Mix quantitative and qualitative: Combine automated scoring with periodic human evaluation for subtle qualities like tone and ethics.
• Track system-level metrics: Measure latency, cost, scaling behavior, memory, and throughput alongside accuracy so you know operational trade-offs.
• Run ablation studies: Measure the impact of prompt design, context length, quantization, and model size on both quality and efficiency.
• Use continuous evaluation: Add new test cases as user behavior evolves and monitor model drift and degradation over time.

How Benchmarks Differ From System-Level Tests

Benchmarks test model capabilities on curated tasks. System-level benchmarks measure integrated performance in production, including:

• Latency under load
• Multi-user concurrency
• Pipeline bottlenecks
• Caching
• Pre and post-processing
• User experience

Treat LLM benchmark scores as indicators, not guarantees of system readiness.

Questions to Guide Your Next Steps

• Which user tasks matter most right now: factual Q A, coding, or multi-turn chat?
• Do you need tight latency and cost targets or maximum quality regardless of resource use?
• How will you measure fairness and safety in production, and at what thresholds will you block output?
• Would you like a custom benchmark suite mapped to a specific application, or a template for combining automated metrics and human evaluation for continuous monitoring?

Related Reading

• LLM Inference Optimization
• Model Context Protocol
• Speculative Decoding
• Lora Fine Tuning
• Gradient Checkpointing
• LLM Quantization
• LLM Use Cases
• Post Training Quantization
• vLLM Continuous Batching

LLM Performance Benchmarks

1. ARC

The AI2 Reasoning Challenge evaluates question answering and basic reasoning with more than 7,000 grade school natural science questions split into an easy set and a challenge set.

Scoring is direct:

One point for a correct answer, and 1/N points when the model lists multiple answers and one is correct.

ARC focuses on factual recall and short-form reasoning in science, and researchers use it to measure knowledge coverage, answer accuracy, and robustness under few-shot and zero-shot prompts.

2. Chatbot Arena

Chatbot Arena gathers pairwise human preferences by having users talk to two anonymous chatbots and vote for the preferred response. Those votes feed statistical ranking methods and sampling algorithms to estimate relative model quality on conversational fluency, instruction following, and safety.

This benchmark evaluates:

• Real-world interaction
• User preference
• Comparative model ranking rather than single metric accuracy.

3. GSM8K

GSM8K contains roughly 8,500 grade school math word problems that require between two and eight elementary calculation steps. Solutions are written in natural language, and models are judged on correct final answers.

Researchers apply AI verifiers or human checks to validate reasoning chains and use metrics like accuracy and verifier agreement to measure mathematical reasoning and mitigation of hallucinated intermediate steps.

4. HellaSwag

HellaSwag asks a model to complete situations by choosing among plausible endings that include adversarially generated wrong answers. It measures commonsense reasoning and natural language inference in zero-shot and few-shot settings and evaluates accuracy after adversarial filtering, which inflates the difficulty.

HellaSwag emphasizes robustness to deceptive distractors and helps test calibration and contextual understanding.

5. HumanEval

HumanEval focuses on code generation and functional correctness. Each prompt includes a programming problem and unit tests; models get credit when their generated code passes those tests.

The pass@k metric estimates the probability that at least one of k samples passes. HumanEval captures model capability on synthesis, testable correctness, and sample diversity for code generation benchmarks.

6. MMLU

Massive Multitask Language Understanding spans over 15,000 multiple-choice questions across 57 subjects. MMLU tests general knowledge, reading comprehension, and the ability to apply learned information in few-shot and zero-shot scenarios.

Evaluators compute accuracy per subject and then average across subjects to produce a single comparative score for knowledge breadth and domain generalization.

7. MBPP

Mostly Basic Programming Problems contains about 974 short Python tasks aimed at entry-level programmers. Each problem includes examples and test cases, and the benchmark judges functional correctness by running tests.

MBPP reports two key metrics:

• Percent of tasks solved by any sample and percent of samples that solve their tasks
• Helping quantify practical code synthesis and sample efficiency.

8. MT Bench

MT Bench contains open-ended, multi-turn questions across coding, extraction, STEM knowledge, humanities, math, reasoning, role play, and writing. It uses GPT 4 as a judge to score responses and compares instruction following, conversational coherence, and factuality across models.

This framework emphasizes instruction adherence, multi-turn context handling, and human-like dialogue quality.

9. SWE bench

SWE bench challenges models to fix bugs or implement feature requests inside factual code bases. The metric is the percentage of resolved task instances after generating a patch and running tests or applying human review.

This benchmark evaluates long context reasoning, codebase navigation, and integration with execution environments that reflect production software engineering demands.

10. TruthfulQA

TruthfulQA contains over 800 questions across 38 topics that are specifically designed to expose model hallucination. It combines human evaluation and auxiliary models fine-tuned on BLEU and ROUGE to predict judgments of informativeness and truthfulness.

The benchmark measures factuality, propensity to invent facts, and the model’s ability to resist leading prompts that encourage false confident answers.

11. Winogrande

Winogrande expands on the Winograd Schema Challenge with 44,000 crowdsourced problems using adversarial filtering. Models select the correct referent in carefully constructed pronoun resolution tasks.

The benchmark measures commonsense inference and contextual disambiguation through standard accuracy scoring and pushes models to reason about implicit world knowledge.

12. BIG Bench Hard

BIG Bench Hard extracts 23 of the most challenging tasks from BIG Bench, which initially had over 200 tasks spanning reasoning, code, math, language, and more. Outputs vary in format, so scoring often uses exact match or tailored metrics.

Chain of thought significantly improves performance on many BBH tasks by guiding multi-step reasoning, and practitioners use BBH to probe model limits and cross-task generalization. Example usage can instantiate a BBH benchmark with configurable tasks, shots, and a chain of thought enabled before evaluating a model and reporting an overall score.

13. CodeXGLUE

CodeXGLUE covers 14 datasets across 10 tasks, such as:

• Code completion
• Code translation
• Summarization
• Code search

Evaluation uses task-appropriate metrics from exact match to BLEU and task-specific measures like CodeBLEU. It provides a standardized evaluation suite and leaderboard for comparing models on engineering-oriented benchmarks and developer workflows.

14. GSM8K

GSM8K requires models to perform sequential arithmetic operations and to articulate reasoning steps in natural language. Researchers use accuracy and verifier-assisted checks to measure whether models can chain operations without inventing intermediate facts.

This dataset highlights gaps in numerical reasoning, stepwise verification, and the need for a verifiable chain of thought for reliable math outputs.

15. MATH

MATH collects 12,500 problems from advanced mathematics competitions requiring algebra, geometry, calculus, and problem-solving strategies beyond routine high school math. Evaluation demands multi-step reasoning, symbolic manipulation, and creative heuristics.

Models are judged on final correctness and their ability to produce stepwise solutions that align with expected derivations.

16. Mostly Basic Programming Problems

MBPP tests program synthesis with natural language prompts and example solutions. Each problem ships with test cases, so benchmarking focuses on practical correctness.

Researchers report solved task rates and sample success rates to track improvements in code generation models, sample diversity, and reliability under few-shot or fine-tuned settings.

17. SWE bench

SWE bench assembles over 2,200 GitHub issues and corresponding pull requests across popular Python projects. Models must generate patches that compile and resolve the issue, often requiring context windows spanning many files.

This benchmark measures model competence in code comprehension, debugging, and producing executable fixes judged by run-time tests and human review.

18. AgentHarm

AgentHarm contains 110 explicitly malicious agent tasks across 11 harm categories like fraud, cybercrime, and harassment. It evaluates whether models refuse harmful agentic requests and maintain safe behavior even when adversarially prompted to complete a multi-step attack.

Metrics track refusal rates and the model’s ability to preserve non-harmful behavior under adversarial conditioning.

19. SafetyBench

SafetyBench includes over 11,000 multiple-choice questions across categories such as:

• Offensive content
• Bias
• Illegal activities
• Mental health

It provides bilingual data for safety evaluation across user demographics and measures a model’s compliance, bias mitigation, and safe response generation using standard classification metrics.

20. MultiMedQA

MultiMedQA merges six medical datasets spanning professional, research, and consumer questions, and adds a new set of medically searched questions. It evaluates:

• Factuality
• Comprehension
• Reasoning
• potential harm
• Bias

The benchmark measures clinical knowledge encoding, safety in medical advice, and domain-specific generalization that affects downstream deployment decisions.

21. FinBen

FinBen covers 24 tasks in seven financial domains, such as:

• Information extraction
• Question answering
• Text generation
• Risk management
• Forecasting
• Decision making

It includes stock trading evaluation and highlights where models excel or fail in finance. Metrics span exact match, F score, forecasting error, and domain-specific risk measures to evaluate reliability, calibration, and economic reasoning.

22. LegalBench

LegalBench contains 162 tasks across six legal reasoning types, including:

• Issue spotting
• Rule recall
• Rule application
• Rule conclusion
• Interpretation
• Rhetorical understanding

Experts crowdsource tasks and judge outputs, creating a focused benchmark for statutory reasoning, precedent application, and precise legal language generation.

Evaluation emphasizes the correctness of legal argument, citation accuracy, and the applicability of rules to factual scenarios.

Related Reading

• KV Cache Explained
• LLM Performance Metrics
• LLM Serving
• Serving ML Models
• LLM Performance Benchmarks
• Pytorch Inference
• Inference Latency
• Inference Optimization

Start Building with $10 in Free API Credits Today!

Inference exposes OpenAI-style endpoints so you can swap models without rewriting your call patterns. Use GPT style completions, streaming tokens, and the same auth model you already know. The platform runs popular open source LLMs and handles cold start control, warm pools, autoscaling, and request routing.

Expect typical metrics you watch in LLM performance benchmarks, like p95 and p99 latency, throughput per GPU, and memory footprint, to be surfaced in dashboards so you can profile model latency and token throughput.

How Performance Shows Up In Real Work: Latency, Throughput, And Cost Per Token

Performance is measured by latency, throughput, and cost efficiency. Look at tokens per second, end-to-end latency, and latency tail when you benchmark a model. Track GPU utilization, memory bandwidth, and throughput per GPU.

Run microbenchmarks for single request latency and macrobenchmarks for sustained throughput under concurrency. Test float16 and int8 quantized variants to trade off perplexity and inference speed. Which metric matters most for your app, p95 latency or throughput per dollar?

Batch Processing For High Volume Async Ai Jobs That Scale

Batch APIs optimize throughput for asynchronous workloads by grouping requests into larger compute units. Dynamic batching reduces GPU idle time and raises tokens per second.

The service supports job queues, backpressure control, retries, and idempotent jobs so that large-scale embedding generation or extraction jobs run without manual orchestration. Measure batch throughput, batch latency distribution, and effective cost per token when you tune batch size and concurrency.

Document Extraction Built For Retrieval Augmented Generation Workflows

Extraction pipelines produce clean text, chunking, embeddings, and structured fields for RAG indexes. The toolkit handles OCR, chunk overlap, metadata tagging, and vectorization so your vector store receives consistent passages.

Evaluate extraction quality with precision and recall, and measure embedding speed and index build throughput. Tune chunk size, overlap, and embedding dimensionality to balance retrieval recall and token budget when you assemble context windows for prompts.

How $10 Free Api Credits And Model Choice Speed Your First Tests

Free credits let you run initial LLM performance benchmarks without committing budget. Try multiple models and quantization settings to compare perplexity, token generation speed, and cost per token.

Use warm worker pools, caching of common prompts, and response caching to reduce p95 latency and cost. Want to validate throughput per dollar? Run the same prompt across models while collecting p95, p99, tokens per second, and GPU utilization.

Practical Tips To Squeeze Inference Efficiency From Open Source Llms

Use mixed precision and int8 quantization to lower the memory footprint and increase throughput per GPU. Profile with tensor core-aware tools to reduce compute stalls and improve throughput.

Apply prompt compression and token pruning to reduce average tokens per request. Favor batching for high-throughput endpoints and reserve small concurrency pools for low-latency interactive traffic. Which of these levers will move your metrics most quickly during tests?

Observability And Benchmark Hygiene You Should Follow

Log raw latency distributions, not just averages, capture throughput curves as concurrency scales, and track cost per token per model version. Run benchmarks that include tokenization and network overhead so your end-to-end latency reflects production behavior. Store reproducible workloads and seed inputs so results are comparable across runs and model upgrades.

Operational Patterns For Production Inference

Use autoscaling with warm workers to avoid cold start penalties and maintain p95 latency. Separate asynchronous batch pipelines from interactive endpoints to optimize throughput and tail latency independently. Implement circuit breakers and graceful degradation when model latency spikes, and instrument backpressure so upstream services remain healthy while you troubleshoot model performance.

Related Reading

• Continuous Batching LLM
• Inference Solutions
• vLLM Multi-GPU
• Distributed Inference
• KV Caching
• Inference Acceleration
• Memory-Efficient Attention