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Meta: Llama 3.1 405B (base) vs OpenAI: GPT-5.2-Codex

Head-to-head API cost, context, and performance comparison. Synced at 11:16:52 AM.

Executive Summary

When evaluating Meta: Llama 3.1 405B (base) against OpenAI: GPT-5.2-Codex, the pricing structure is a key differentiator. Meta: Llama 3.1 405B (base) is approximately 49% more cost-effective per 1 million tokens overall.

However, when looking at raw reasoning capabilities, OpenAI: GPT-5.2-Codex leads with a statistical ELO score of 1300. For tasks involving complex logic, coding, or instruction-following, developers might prefer OpenAI: GPT-5.2-Codex, provided their budget allows for the API burn rate.

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Raw Technical comparison

Metric
Meta: Llama 3.1 405B (base)
OpenAI: GPT-5.2-Codex
Performance (ELO)
1300
1300
Input Cost / 1M
$4.00
$1.75
Output Cost / 1M
$4.00
$14.00
Context Window
32,768 tokens
400,000 tokens

Verdict

If you are looking for pure performance and capability, Tie is statistically superior. However, if API burn rate is the primary concern, Meta: Llama 3.1 405B (base) wins out aggressively in pricing.

People Also Ask

Is Meta: Llama 3.1 405B (base) cheaper than OpenAI: GPT-5.2-Codex?

Yes. Meta: Llama 3.1 405B (base) is cheaper for both input and output generation compared to OpenAI: GPT-5.2-Codex. Exploring alternatives often yields cost reductions.

Which model has the larger context window?

The OpenAI: GPT-5.2-Codex model has the advantage in memory, offering a massive 400,000 token limit for document ingestion.

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