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

Anthropic Claude Opus 4.8

by AnthropicUpdated 28/05/2026

Benchmark Scores

SWE-bench Verified
88.6%
#1
GPQA Diamond
92.3%
#2
Online-Mind2Web
84%
#1
Terminal-Bench 2.1
79.4%
#2
Strengths
  • Agentic judgment
  • Dynamic Workflows
  • Computer use
  • 1M token context
Limitations
  • Premium pricing
  • API rate limits at scale
flagship
Production Ready

OpenAI GPT-5.5

by OpenAIUpdated 23/04/2026

Benchmark Scores

Terminal-Bench 2.0
82.7%
#1
FrontierMath T1-3
51.7%
#1
ARC-AGI 2
78.5%
#1
AIME 2025
81.2%
#2
Strengths
  • Agentic coding
  • Context management
  • Creative reasoning
  • Cybersecurity
Limitations
  • Occasional hallucination issues
  • Cost for Pro tier
flagship
I/O 2026 Launch

Google Gemini 3.5 Flash

by Google DeepMindUpdated 20/05/2026

Benchmark Scores

Terminal-Bench 2.1
76.2%
#3
MCP Atlas
83.6%
#1
AIME 2025
100%
#1
GPQA Diamond
89.1%
#3
Strengths
  • Multimodal reasoning
  • 1M token context
  • Speed optimised
  • Gemini Spark agent
Limitations
  • Pro version pending release
  • Regional API availability
flagship
Rapid Growth

xAI Grok 4.3

by xAIUpdated 28/04/2026

Benchmark Scores

Continuous Reasoning
91.2%
#1
GPQA Diamond
87.5%
#4
Factual QA
89.8%
#1
Tool Use
85.3%
#2
Strengths
  • Aggressive pricing
  • Configurable reasoning levels
  • Real-time X data
  • 2M token context
Limitations
  • Ecosystem maturity
  • Enterprise support
open source
Open Source

Meta Llama 4 Scout

by MetaUpdated 15/03/2026

Benchmark Scores

IFEval
93.7%
#1
SWE-bench
72.4%
#3
MMLU-Pro
87.2%
#2
Throughput
89 tok/s
#1
Strengths
  • Open weights
  • 10M token context
  • High throughput
  • Fine-tuning friendly
Limitations
  • Infrastructure requirements
  • Lower agentic capability
cost efficient
Cost Leader

DeepSeek V3.2

by DeepSeekUpdated 10/04/2026

Benchmark Scores

Coding Tasks
88.5%
#2
MMLU-Pro
85.1%
#4
Math-500
82.6%
#3
Reasoning
84.9%
#3
Strengths
  • ~$0.35/M tokens
  • Rivals proprietary models
  • Strong coding
  • Self-hostable
Limitations
  • Limited multimodal support
  • Smaller community ecosystem

Data Sources & Citations

Our benchmark data comes from verified sources and official publications:

Last updated: June 2026. Benchmark scores may vary based on evaluation methodology and test conditions.

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