n the rapidly evolving world of artificial intelligence, the cost of inference has seen a remarkable decline over the past year, making these cutting-edge technologies more accessible than ever before. As industry competition heats up and efficiency in both software and hardware improves, we are witnessing a democratization of AI that is opening doors for innovators and creators across the globe.
The measure of cost per million tokens has become the yardstick by which we gauge the affordability of AI models. This metric not only reflects the direct cost of utilizing AI but also embodies the intricate dance between computational power and the economic scales that drive technological advancement. It’s a testament to the relentless pursuit of optimization and cost-effectiveness that characterizes the AI field.
I recall a conversation with a colleague from the early days of natural language processing. We marveled at the sheer computational requirements and the associated costs needed to produce even the simplest of language models. Back then, the idea of generating human-like text was a distant dream, ridden with financial and technical roadblocks. Fast forward to today, and it’s astounding how far we’ve come. We’ve transitioned from those days of prohibitive costs and exclusive access to a time where startups and established enterprises alike can harness the power of advanced language models without breaking the bank.
In light of these changes, it’s exciting to present a snapshot of the present AI costs as of May 2024. The following table doesn’t just list numbers—it narrates the story of progress, of barriers broken, and of a future that’s being written with the ink of innovation. Here’s a comprehensive look at the pricing landscape for some of the most prominent language models available in the market today.
| MODEL NAME | PROVIDER | INPUT PRICE / 1M TOKENS | OUTPUT PRICE / 1M TOKENS |
|---|---|---|---|
| gpt-3.5-turbo-0125 | OpenAI | $0.50 | $1.50 |
| gpt-3.5-turbo-instruct | OpenAI | $1.50 | $2.00 |
| gpt-4-32k-context | Azure | $0.25 | $1.25 |
| claude-3-sonnet | Anthropic | $3.00 | $15.00 |
| claude-3-haiku | Anthropic | $15.00 | $75.00 |
| claude-3-opus | Anthropic | $15.00 | $75.00 |
| claude-2.0/2.1 | Anthropic | $8.00 | $24.00 |
| mistral-large | Mistral | $8.00 | $24.00 |
| mistral-7b | Mistral | $0.25 | $0.25 |
| claude-2.0/2.1 | AWS | $8.00 | $24.00 |
| jurassic-2-ultra | AWS | $18.80 | $18.80 |
| imagen-image-editing | $20.00 | $20.00 | |
| claude-3-sonnet | $3.00 | $15.00 | |
| Llama 3 70B (8K Context Length) | Groq | $0.59 | $0.79 |
| Mixtral 8x7B SMoE (32K Context Length) | Groq | $0.27 | $0.27 |
| Llama 3 8B (8K Context Length) | Groq | $0.05 | $0.10 |
| Gemma 7B (8K Context Length) | Groq | $0.10 | $0.10 |
As we peruse this table, let’s appreciate the strides made in AI affordability and the opportunities it has unfurled. Whether you’re a developer looking to integrate the latest AI into your app, a researcher pushing the boundaries of what’s possible, or a business aiming to leverage AI for a competitive edge, the current pricing landscape is a fertile ground for growth and exploration.