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GPT-4o vs Llama 4 Scout

How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for GPT-4o and Llama 4 Scout.

OpenAI

GPT-4o

GPT-4o ("o" for "omni") is OpenAI's latest AI model, supporting both text and image inputs with text outputs. It maintains the intelligence level of [GPT-4 Turbo](/models/openai/gpt-4-turbo) while being twice as...

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Meta

Llama 4 Scout

Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input...

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Technical Specifications

SpecificationGPT-4oLlama 4 Scout
ProviderOpenAIMeta
Context Window128,000 tokens10,000,000 tokens
Agent Suitability90/10082/100
Time to First Token (TTFT)280 ms350 ms
Deployment Modelmanaged apiself hostable
Production Stabilitystablebeta
API AvailableYesYes
Released Date2024-05-132025-04-05

API Pricing Comparison

Input Price per Million Tokens

GPT-4o

$2.50

Llama 4 Scout

$0.10

Output Price per Million Tokens

GPT-4o

$10.00

Llama 4 Scout

$0.30

Want to test both models live?

Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.

Benchmark Performance Metrics

Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.

MMLUGeneral knowledge & multi-task understanding
8870.0%vs8720.0%
GPT-4o
Llama 4 Scout
HumanEvalPython coding & logic synthesis
9020.0%vs8950.0%
GPT-4o
Llama 4 Scout
MATHComplex mathematical problem solving
7660.0%vs8100.0%
GPT-4o
Llama 4 Scout
GPQAGraduate-level expert reasoning
5360.0%vs6680.0%
GPT-4o
Llama 4 Scout
HellaSwagCommonsense reasoning and inference
8870.0%vs9450.0%
GPT-4o
Llama 4 Scout
MT-BenchMulti-turn conversation flow quality
930.0%vs910.0%
GPT-4o
Llama 4 Scout

GPT-4o Quirks & Gotchas

  • โ–ธStrong multimodal performance โ€” best vision+tool calling combo
  • โ–ธLegacy model โ€” migrate to GPT-5 for latest improvements

Llama 4 Scout Quirks & Gotchas

  • โ–ธ10M context causes significant VRAM pressure โ€” recommend 4-bit quantization
  • โ–ธPrimarily designed for RAG, not agentic tool calling