Which Language Model
Should You Use?
Compare GPT-5, Claude Opus 4.6, Llama 4, Gemini 2.5, and other 2026 LLMs across standard benchmarks. From language understanding (MMLU) to mathematical reasoning (GSM8K, MATH) to code generation (HumanEval, SWE-bench).
What Makes a Good Language Model?
LLMs are evaluated across multiple dimensions. No single benchmark tells the full story. Here's what we track:
General Knowledge
How well does the model understand the world? Tested via MMLU (57 academic subjects), ARC (science), and HellaSwag (common sense).
Reasoning Ability
Can the model think through complex problems? Measured via GSM8K (grade-school math), MATH (competition problems), and GPQA (expert reasoning).
Code Generation
Programming proficiency via HumanEval (function synthesis), MBPP (Python basics), and SWE-bench (real-world debugging).
Multimodal Understanding
Vision capabilities tested via MMMU (college-level reasoning), MathVista (visual math), and ChartQA (data interpretation).
Key Benchmarks
Language Understanding
General knowledge, reading comprehension, and language tasks
MMLU
SOTA: 92.3%57 subjects from STEM to humanities
HellaSwag
SOTA: 96.2%Commonsense reasoning
ARC
SOTA: 97.1%Grade-school science questions
TruthfulQA
SOTA: 91.0%Factual accuracy and truthfulness
Reasoning & Math
Mathematical problem solving and logical reasoning
GSM8K
SOTA: 97.8%Grade-school math word problems
MATH
SOTA: 96.4%Competition mathematics
GPQA
SOTA: 79.8%Graduate-level science questions
BBH
SOTA: 94.1%Big-Bench Hard reasoning tasks
Code Generation
Programming ability and software engineering
HumanEval
SOTA: 94.1%Python function synthesis
MBPP
SOTA: 91.2%Basic Python programming
SWE-bench
SOTA: 80.9%Real GitHub issue resolution
LiveCodeBench
SOTA: 62.3%Recent coding problems
Multimodal
Vision, image understanding, and cross-modal tasks
MMMU
SOTA: 74.2%College-level multimodal understanding
MathVista
SOTA: 72.8%Visual mathematical reasoning
AI2D
SOTA: 95.1%Diagram understanding
ChartQA
SOTA: 87.6%Chart and graph comprehension
Model Families
The major LLM providers and their model series. Each family targets different use cases and price points.
GPT Series
OpenAI
GPT-4o, GPT-5, o1, o3, o4-mini
Claude
Anthropic
Claude Sonnet 4.6, Claude Opus 4.6, Claude Haiku 4.5
Llama
Meta
Llama 3.3 70B, Llama 4 Scout, Llama 4 Maverick
Gemini
Gemini 2.5 Pro, Gemini 2.5 Flash, Gemini 3 Flash
Mistral
Mistral AI
Mistral Large 3, Mistral Small 3.1, Codestral 25.01
DeepSeek
DeepSeek
DeepSeek V3, DeepSeek R1
Qwen
Alibaba
Qwen 2.5, Qwen 3, QwQ-32B
Grok
xAI
Grok 2, Grok 3, Grok 3 Mini
Understanding the Metrics
MMLU (Massive Multitask Language Understanding)
Multiple-choice questions across 57 subjects from elementary math to professional law. The most comprehensive test of general knowledge.
GSM8K (Grade School Math 8K)
8,500 grade-school level math word problems requiring multi-step reasoning. Tests basic mathematical reasoning ability.
HumanEval
164 Python programming problems. Model must generate a function that passes unit tests. The standard for measuring code generation ability.
GPQA (Graduate-Level Google-Proof Q&A)
Expert-written questions in biology, physics, and chemistry designed to be difficult even for domain experts. Tests deep reasoning.
Explore Related Benchmarks
Why Our LLM Benchmarks Are Different
Verified Results Only
No marketing claims. We cite published papers, official leaderboards, and third-party evaluations. Every number is traceable.
Consistent Methodology
Same evaluation protocol for all models. We note when vendors use different prompting strategies or few-shot examples.
Regular Updates
New models release weekly. We track the latest results as papers publish and maintain historical trends.
Frequently Asked Questions
What's the difference between GPT-4 and GPT-4o?
GPT-4o is the "omni" version with native multimodal capabilities (vision, audio). It's faster and cheaper than GPT-4 while maintaining similar benchmark scores on text tasks. GPT-4o is now the default model at OpenAI.
Are open-source LLMs competitive with GPT-4?
In 2026, open-source models have largely closed the gap. Llama 4 Maverick, Qwen 3, and DeepSeek R1 achieve competitive performance across MMLU, HumanEval, and reasoning tasks. The trade-off is now primarily about hosting complexity vs API convenience.
Why do benchmark scores vary across sources?
Different evaluation setups: 0-shot vs few-shot prompting, exact wording of system prompts, temperature settings, and whether chain-of-thought is used. We document these differences in our benchmark methodology notes.
Which benchmark best predicts real-world performance?
Depends on your use case. For coding assistants, check SWE-bench. For tutoring/QA, MMLU matters. For math/analysis, GSM8K and MATH. No single metric captures everything. That's why we track multiple benchmarks.
Explore LLM Benchmark Data Tracked Across 50+ Models
Compare MMLU, GSM8K, HumanEval, SWE-bench, and more across every major model family updated for 2026.