LLM Benchmarks & Performance

Compare the latest performance data from leading AI models across key benchmarks. Data sourced from official releases, research papers, and verified leaderboards.

4
Flagship Models
1
Open Source
1
Cost Efficient

Understanding the Benchmarks

MMLU

Massive Multitask Language Understanding - tests knowledge across 57 academic subjects

HumanEval

Coding benchmark measuring ability to generate correct Python functions

GPQA

Graduate-level Google-Proof Q&A in biology, physics, and chemistry

LMSys Arena

Real-world human preference ratings from head-to-head comparisons

flagship
Latest Release

Google Gemini 2.5 Pro

by Google DeepMindUpdated 15/01/2025

Benchmark Scores

LMSys Arena Elo
1466
#1
GPQA Diamond
86.4%
#1
MMLU
90.2%
#1
HumanEval
89.5%
#2
Strengths
  • Multimodal reasoning
  • Real-time processing
  • Agent capabilities
Limitations
  • Limited availability
  • API costs
flagship
Production Ready

OpenAI GPT-4o

by OpenAIUpdated 10/01/2025

Benchmark Scores

LMSys Arena Elo
1452
#2
MMLU
88.7%
#2
MedQA USMLE
89.4%
#1
HumanEval
90.2%
#1
Strengths
  • Code generation
  • Voice capabilities
  • Creative tasks
Limitations
  • Context length limitations
  • Cost for high usage
flagship
Computer Use Beta

Anthropic Claude 3.5 Sonnet

by AnthropicUpdated 05/01/2025

Benchmark Scores

LMSys Arena Elo
1438
#3
HumanEval
92%
#1
MMLU
88.7%
#2
GPQA
77.9%
#3
Strengths
  • Computer use
  • Long context
  • Safety focus
Limitations
  • Image generation limitations
  • Regional availability
flagship
Limited Access

xAI Grok 3

by xAIUpdated 20/12/2024

Benchmark Scores

AIME
93.3%
#1
GPQA
88.1%
#2
HumanEval
86.7%
#4
MMLU
85.6%
#5
Strengths
  • Real-time data
  • Mathematical reasoning
  • X integration
Limitations
  • Limited access
  • Newer model with less data
open source
Open Source

Meta Llama 3.3 70B

by MetaUpdated 28/12/2024

Benchmark Scores

IFEval
92.1%
#1
HumanEval
88.4%
#3
MMLU
86%
#4
MATH
68%
#3
Strengths
  • Open source
  • Cost effective
  • Fine-tuning friendly
Limitations
  • Requires infrastructure
  • Lower performance than closed models
cost efficient
Cost Leader

DeepSeek V3

by DeepSeekUpdated 15/12/2024

Benchmark Scores

Coding Tasks
87.2%
#2
MMLU
84.3%
#6
Math Problems
71.8%
#4
Reasoning
82.1%
#5
Strengths
  • Cost efficiency
  • Sparse attention
  • Strong coding
Limitations
  • Less multimodal capability
  • Limited brand recognition

Data Sources & Citations

Our benchmark data comes from verified sources and official publications:

Last updated: January 2025. Benchmark scores may vary based on evaluation methodology and test conditions.

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