GPT-4 vs Databricks Llama 3.1 405B

Performance benchmarks + pricing comparison — updated April 2026

GPT-4

OpenAI

Original GPT-4. Most expensive OpenAI model, largely superseded by newer options.

Input	$30.00/M
Output	$60.00/M
Context	8K tokens
Best For	Legacy applications requiring GPT-4 specifically
Benchmark	68/100

Databricks

Meta's 405B model hosted on Databricks. Largest open-weight model available for enterprise use.

Input	$5.00/M
Output	$15.00/M
Context	128K tokens
Best For	Enterprise AI, complex reasoning, custom fine-tuning

Estimated cost per project with 30% cache hit rate. Actual costs may vary based on usage patterns.

Scenario	GPT-4	Databricks Llama 3.1 405B	Savings
Small Script (1K lines)	$2.85	$0.63	Databricks Llama 3.1 405B saves $2.22 (78%)
Medium Feature (10K lines)	$22.50	$4.75	Databricks Llama 3.1 405B saves $17.75 (79%)
Large Project (50K lines)	$112.50	$23.75	Databricks Llama 3.1 405B saves $88.75 (79%)
Code Review (5K lines)	$6.75	$1.25	Databricks Llama 3.1 405B saves $5.50 (81%)

Databricks Llama 3.1 405B wins on both price and performance — $5.00/M input with a benchmark score of N/A/100.

For most developers, this is the clear choice between these two models.

Anthropic's balanced model for coding and general tasks. Best price-performance ratio in the Claude family.

Anthropic's most powerful model. Best for complex reasoning and challenging coding tasks.

Previous generation Sonnet. Still excellent for coding tasks at the same price point.

Fast, cost-effective model for high-volume tasks. Great for code review and simple queries.

First generation Opus. Highest reasoning capability in the Claude 3 family.

First generation Sonnet. Balanced performance for general tasks.