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GPT-5.3-Codex vs Llama 3.1 8B

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-5.3-Codex and Llama 3.1 8B.

OpenAI

GPT-5.3-Codex

GPT-5.3-Codex is OpenAI’s most advanced agentic coding model, combining the frontier software engineering performance of GPT-5.2-Codex with the broader reasoning and professional knowledge capabilities of GPT-5.2. It achieves state-of-the-art results...

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Meta

Llama 3.1 8B

Llama 3.1 8B is Meta's lightweight open-weight model from the Llama 3.1 generation, optimized for efficient deployment on consumer hardware and edge devices. Despite its compact 8-billion-parameter size, it delivers strong performance on instruction following, text summarization, and lightweight coding tasks. Lllama 3.1 8B is the most downloaded model in the Llama family and runs efficiently on laptops, single GPUs, and CPU via quantization β€” making it the default choice for on-device AI applications and local prototyping.

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Technical Specifications

SpecificationGPT-5.3-CodexLlama 3.1 8B
ProviderOpenAIMeta
Context Window400,000 tokens131,072 tokens
Agent SuitabilityN/A74/100
Time to First Token (TTFT)N/A80 ms
Deployment Modelmanaged apiself hostable
Production Stabilitystablestable
API AvailableYesYes
Released Date2026-02-242024-07-23

API Pricing Comparison

Input Price per Million Tokens

GPT-5.3-Codex

$1.75

Llama 3.1 8B

$0.04

Output Price per Million Tokens

GPT-5.3-Codex

$14.00

Llama 3.1 8B

$0.04

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.

GPT-5.3-Codex Quirks & Gotchas

No developer gotchas reported.

Llama 3.1 8B Quirks & Gotchas

  • β–ΈPerfect for CPU/edge deployment β€” runs on Raspberry Pi with quantization
  • β–ΈLimited tool calling vs larger models β€” best for simple classification and chat