LLM Performance Tracking

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:

Key Benchmarks

Model Families

The major LLM providers and their model series. Each family targets different use cases and price points.

GPT Series

OpenAI

active

GPT-5.4, GPT-5.3-Codex, GPT-5.2, GPT-5.1, o4-mini

Claude

Anthropic

active

Claude Opus 4.6, Claude Sonnet 4.6, Claude Haiku 4.5

Gemini

Google

active

Gemini 3.1 Pro, Gemini 3 Flash, Gemini 2.5 Pro, Gemini 2.5 Flash

DeepSeek

DeepSeek

active

DeepSeek V3.2, DeepSeek R1

Grok

xAI

active

Grok 4.1 Fast, Grok 4 Fast

MiniMax

MiniMax

active

MiniMax M2.5

Kimi

Moonshot AI

active

Kimi K2.5

GLM

Z.ai

active

GLM 5, GLM 5 Turbo

Mistral

Mistral AI

active

Mistral Small 4, Mistral Large 3, Codestral

Llama

Meta

active

Llama 4 Scout, Llama 4 Maverick, Llama 3.3 70B

Provider Market Share

Google
16.6%1.23T
Anthropic
14.3%1.05T
OpenRouter
12.3%912B
OpenAI
10.4%772B
MiniMax
8.4%618B
StepFun
7.2%531B
DeepSeek
7%520B
Z.ai
5.5%407B
Moonshot
3.3%243B
Others
14.9%1.1T

Quality Rankings

OpenRouter's composite quality score based on Arena Elo and multi-benchmark evaluation.

1

Gemini 3.1 Pro Preview

Google

57.2pts
2

GPT-5.4

OpenAI

57.2pts
3

GPT-5.3-Codex

OpenAI

54pts
4

Claude Opus 4.6

Anthropic

53pts
5

Claude Sonnet 4.6

Anthropic

51.7pts
6

GPT-5.2

OpenAI

51.3pts
7

GLM 5

Z.ai

49.8pts
8

Claude Opus 4.5

Anthropic

49.7pts
9

Gemini 3 Pro Preview

Google

48.4pts
10

GPT-5.1

OpenAI

47.7pts

Data from OpenRouter rankings, scraped March 17 2026. Scores are OpenRouter's composite quality metric.

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.

Metric: 5-way multiple choice accuracy. Random baseline: 20%. SOTA: ~92%.

GSM8K (Grade School Math 8K)

8,500 grade-school level math word problems requiring multi-step reasoning. Tests basic mathematical reasoning ability.

Metric: Exact match accuracy. SOTA: ~98% (with chain-of-thought reasoning).

HumanEval

164 Python programming problems. Model must generate a function that passes unit tests. The standard for measuring code generation ability.

Metric: Pass@1 (percentage that pass on first try). SOTA: ~94%.

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.

Metric: Multiple choice accuracy. Expert baseline: ~71%. SOTA: ~80%.

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.