Performance

AI Model Performance Benchmarks

📊 Overview: Compare frontier models across standardized benchmarks. Data from official sources and independent evaluations.

Reasoning & Knowledge (MMLU)

MMLU (Massive Multitask Language Understanding): Tests knowledge across 57 domains. Higher is better.

Model MMLU Score Training Cutoff
GPT-4o 92.3% April 2024
Claude 3.5 Sonnet 88.3% Late 2024
Mistral Large 2 84.4% Q2 2024
Deepseek-V3 88.5% Q2 2024
Llama 3.1 70B 85.2% April 2024
Gemini 2.0 Flash 85.9% April 2024

Mathematics (GSM-8K)

GSM-8K: Grade school math problems. Tests mathematical reasoning.

Model GSM-8K Score Advanced Math (AIME)
GPT-4o 97.1% 80.7%
Claude 3.5 Sonnet 96.5% 71.3%
Deepseek-V3 96.0% 79.4%
Llama 3.1 70B 93.0% 65.7%
Mistral Large 2 89.0% N/A

Code Generation (HUMANEVAL)

HUMANEVAL: Python function implementation. Gold standard for code generation.

Model HUMANEVAL Score Best Use Case
Deepseek-V3 97.3% Code generation specialist
GPT-4o 92.3% Reliable production code
Claude 3.5 Sonnet 92.1% Complex refactoring, architecture
Mistral Large 2 91.0% Efficient code generation
Llama 3.1 70B 85.9% Open-source option

Long Context Reasoning (NEEDLE-IN-HAYSTACK)

Needle-in-Haystack: How well models find relevant info in long documents. Tests context window usage.

Model Context Window Performance at 80% Practical Best Practice
Gemini 2.0 Flash 1M tokens Excellent Analyze entire books/codebases
Claude 3.5 Sonnet 200K tokens Excellent Long documents, full conversations
Llama 3.1 70B 128K tokens Very Good Extended docs, moderate context
GPT-4o 128K tokens Very Good Production with large context
Mistral Large 2 32K tokens Good Smaller documents, conversations

Latency & Speed Comparison

Time to First Token (TTFT): How fast responses start appearing.

Tokens Per Second (TPS): How fast the model generates output.

Model TTFT Tokens/Sec Rating
Gemini 2.0 Flash <300ms >100 t/s ⭐⭐⭐⭐⭐
GPT-4o <500ms >80 t/s ⭐⭐⭐⭐⭐
Mistral Large 2 <800ms >60 t/s ⭐⭐⭐⭐
Claude 3.5 Sonnet <1s >50 t/s ⭐⭐⭐⭐
Llama 3.1 70B Variable >40 t/s ⭐⭐⭐

Multimodal Performance

Image Understanding: How well models understand visual content.

Model Vision OCR Chart Reading Visual Reasoning
GPT-4o Native Excellent Excellent Excellent
Claude 3.5 Sonnet Native Excellent Very Good Excellent
Gemini 2.0 Flash Native Good Good Very Good
Grok-2 Native Good Good Very Good
Deepseek-V3 Native Good Good Good
Llama 3.1 (base) No No No No

Real-World Benchmark Summary

⭐ Best Overall (Balanced)

GPT-4o: Wins on MMLU (92.3%), HUMANEVAL (92.3%), Math (97.1%). Fastest. Best multimodal.

⭐ Best for Reasoning

Claude 3.5 Sonnet: 88.3% MMLU, 96.5% GSM-8K. Excellent at nuance and complex analysis. 200K context.

⭐ Best for Code

Deepseek-V3: 97.3% HUMANEVAL (highest). Perfect for code generation tasks.

⭐ Best for Large Context

Gemini 2.0 Flash: 1M tokens. 75% cheaper than alternatives. Handles massive documents.

⭐ Best for Speed

Gemini 2.0 Flash or GPT-4o: Both sub-500ms TTFT. Gemini fastest overall.

Benchmark Methodology

Important Notes:

✓ Benchmarks are snapshots in time. Models improve frequently.
✓ Different sources may show variations (±2-3%)
✓ Benchmarks don't always correlate with real-world performance
✓ Your specific use case may have different winner than average
✓ Test with your actual data to validate model choice

When to Trust These Numbers

Benchmark Useful For Not Useful For
MMLU General knowledge, factual accuracy Creative writing, specific domains
HUMANEVAL Code generation quality Code understanding, debugging, architecture
GSM-8K Mathematical reasoning Real-world problem solving with ambiguity
Latency Relative speed comparison Actual deployment performance (varies with load)
Context Maximum document size Practical retrieval accuracy (quality matters more)

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