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Latest large language model updates with benchmark scores
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Browse all modelsLatest open source LLM releases and updates today
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Explore open-sourceOpen-source models have transformed the AI landscape, offering full control over infrastructure. Models like Llama 3, Mistral, and Qwen now rival proprietary alternatives on many benchmarks while providing flexibility to fine-tune, self-host, and customize for specific domains.
Key considerations include licensing terms (Apache 2.0, MIT, or custom licenses), parameter count (affecting inference costs), quantization support for efficient deployment, and the community ecosystem of fine-tuned variants and tooling.
Find the best AI model for your use case across coding, math, reasoning, and more
HumanEval, SWE-bench, MBPP
MATH, GSM8K, AIME
GPQA, ARC, HellaSwag
MMLU, TriviaQA, WinoGrande
Ranked by average benchmark score
Top performers on this benchmark
Highest quality per dollar spent
Based on benchmark score vs. API pricing ($/million tokens)
The large language model ecosystem has evolved dramatically, with over 500 models now available across commercial APIs and open-source releases. From OpenAI's GPT-4 series to Anthropic's Claude, Google's Gemini, and Meta's Llama family, developers now have unprecedented choice when selecting an AI model.
Benchmark evaluations like GPQA (graduate-level reasoning), HumanEval (code generation), and MMLU (multitask understanding) provide standardized ways to compare capabilities. However, real-world performance depends on your specific use case—a model that excels at coding may not be ideal for creative writing or domain-specific reasoning.
Comprehensive large language model evaluation, leaderboards, and research insights
A challenging dataset of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry. Questions are Google-proof and extremely difficult, with PhD experts reaching 65% accuracy.
Massive Multitask Language Understanding benchmark testing knowledge across 57 diverse subjects including STEM, humanities, social sciences, and professional domains
A more robust and challenging multi-task language understanding benchmark that extends MMLU by expanding multiple-choice options from 4 to 10, eliminating trivial questions, and focusing on reasoning-intensive tasks. Features over 12,000 curated questions across 14 domains and causes a 16-33% accuracy drop compared to original MMLU.
All 30 problems from the 2025 American Invitational Mathematics Examination (AIME I and AIME II), testing olympiad-level mathematical reasoning with integer answers from 000-999. Used as an AI benchmark to evaluate large language models' ability to solve complex mathematical problems requiring multi-step logical deductions and structured symbolic reasoning.
MATH dataset contains 12,500 challenging competition mathematics problems from AMC 10, AMC 12, AIME, and other mathematics competitions. Each problem includes full step-by-step solutions and spans multiple difficulty levels (1-5) across seven mathematical subjects including Prealgebra, Algebra, Number Theory, Counting and Probability, Geometry, Intermediate Algebra, and Precalculus.
A benchmark that measures functional correctness for synthesizing programs from docstrings, consisting of 164 original programming problems assessing language comprehension, algorithms, and simple mathematics
LiveCodeBench is a holistic and contamination-free evaluation benchmark for large language models for code. It continuously collects new problems from programming contests (LeetCode, AtCoder, CodeForces) and evaluates four different scenarios: code generation, self-repair, code execution, and test output prediction. Problems are annotated with release dates to enable evaluation on unseen problems released after a model's training cutoff.
MMMU (Massive Multi-discipline Multimodal Understanding) is a benchmark designed to evaluate multimodal models on college-level subject knowledge and deliberate reasoning. Contains 11.5K meticulously collected multimodal questions from college exams, quizzes, and textbooks, covering six core disciplines: Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering across 30 subjects and 183 subfields.
Live model battles across chat, coding, image, video, and audio modalities
A comprehensive look at Zhipu AI's GLM-4.7 — the flagship foundation model with 200K context window, 128K output capacity, MoE architecture, 'Vibe Coding' capabilities, and what it means for developers and enterprises.
Sebastian Crossa
A comprehensive look at NVIDIA's Nemotron 3 Nano — the hybrid Mamba-Transformer MoE model with 1M context window, 4x faster inference, open weights under NVIDIA Open Model License, and what it means for agentic AI.
Sebastian Crossa
In-depth comparison of Claude Opus 4.5 and Gemini 3 Pro across benchmarks, pricing, context windows, multimodal capabilities, and real-world performance. Discover which AI model best fits your needs.
Sebastian CrossaA practical guide to choosing the right LLM
Identify your primary task—code generation (HumanEval, SWE-bench), mathematical reasoning (MATH, GSM8K), or general knowledge (MMLU). Different benchmarks measure different capabilities.
API pricing ranges from $0.15/M tokens for lightweight models to $60+/M for frontier models. Use our comparison tool to find the best ratio.
Smaller models like GPT-4o-mini or Claude 3.5 Haiku offer faster responses. Reasoning models (o1, DeepSeek-R1) trade latency for accuracy on complex tasks.
Benchmarks provide signals, but real performance depends on your prompts. Create an evaluation set from actual use cases. Our AI Arena enables side-by-side comparison.
Common questions about LLM news, updates, and benchmarks
Dive deeper into large language model data, comparisons, and analysis
Compare 500+ models across benchmarks. Real-time rankings updated daily.
Top performers rankedApache, MIT & permissive licenses
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HumanEval, SWE-bench & more
MATH, GSM8K benchmarks
GPQA, MMLU, HumanEval, MATH, and 50+ more evaluations
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