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GPT-4 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-4 and Llama 4 Scout.

OpenAI

GPT-4

OpenAI's flagship model, GPT-4 is a large-scale multimodal language model capable of solving difficult problems with greater accuracy than previous models due to its broader general knowledge and advanced reasoning...

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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-4Llama 4 Scout
ProviderOpenAIMeta
Context Window8,191 tokens10,000,000 tokens
Agent SuitabilityN/A82/100
Time to First Token (TTFT)N/A350 ms
Deployment Modelmanaged apiself hostable
Production Stabilitystablebeta
API AvailableYesYes
Released Date2023-05-282025-04-05

API Pricing Comparison

Input Price per Million Tokens

GPT-4

$30.00

Llama 4 Scout

$0.10

Output Price per Million Tokens

GPT-4

$60.00

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
N/Avs8720.0%
GPT-4
Llama 4 Scout
HumanEvalPython coding & logic synthesis
N/Avs8950.0%
GPT-4
Llama 4 Scout
MATHComplex mathematical problem solving
N/Avs8100.0%
GPT-4
Llama 4 Scout
GPQAGraduate-level expert reasoning
N/Avs6680.0%
GPT-4
Llama 4 Scout
HellaSwagCommonsense reasoning and inference
N/Avs9450.0%
GPT-4
Llama 4 Scout
MT-BenchMulti-turn conversation flow quality
N/Avs910.0%
GPT-4
Llama 4 Scout

GPT-4 Quirks & Gotchas

No developer gotchas reported.

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