36 posts tagged “llm-pricing”
Posts about the pricing of various LLMs. See also my pricing calculator.
2024
Gemini 1.5 Flash price drop (via) Google Gemini 1.5 Flash was already one of the cheapest models, at 35c/million input tokens. Today they dropped that to just 7.5c/million (and 30c/million) for prompts below 128,000 tokens.
The pricing war for best value fast-and-cheap model is red hot right now. The current most significant offerings are:
- Google's Gemini 1.5 Flash: 7.5c/million input, 30c/million output (below 128,000 input tokens)
- OpenAI's GPT-4o mini: 15c/million input, 60c/million output
- Anthropic's Claude 3 Haiku: 25c/million input, $1.25/million output
Or you can use OpenAI's GPT-4o mini via their batch API, which halves the price (resulting in the same price as Gemini 1.5 Flash) in exchange for the results being delayed by up to 24 hours.
Worth noting that Gemini 1.5 Flash is more multi-modal than the other models: it can handle text, images, video and audio.
Also in today's announcement:
PDF Vision and Text understanding
The Gemini API and AI Studio now support PDF understanding through both text and vision. If your PDF includes graphs, images, or other non-text visual content, the model uses native multi-modal capabilities to process the PDF. You can try this out via Google AI Studio or in the Gemini API.
This is huge. Most models that accept PDFs do so by extracting text directly from the files (see previous notes), without using OCR. It sounds like Gemini can now handle PDFs as if they were a sequence of images, which should open up much more powerful general PDF workflows.
Update: it turns out Gemini also has a 50% off batch mode, so that’s 3.25c/million input tokens for batch mode 1.5 Flash!
GPT-4o mini. I've been complaining about how under-powered GPT 3.5 is for the price for a while now (I made fun of it in a keynote a few weeks ago).
GPT-4o mini is exactly what I've been looking forward to.
It supports 128,000 input tokens (both images and text) and an impressive 16,000 output tokens. Most other models are still ~4,000, and Claude 3.5 Sonnet got an upgrade to 8,192 just a few days ago. This makes it a good fit for translation and transformation tasks where the expected output more closely matches the size of the input.
OpenAI show benchmarks that have it out-performing Claude 3 Haiku and Gemini 1.5 Flash, the two previous cheapest-best models.
GPT-4o mini is 15 cents per million input tokens and 60 cents per million output tokens - a 60% discount on GPT-3.5, and cheaper than Claude 3 Haiku's 25c/125c and Gemini 1.5 Flash's 35c/70c. Or you can use the OpenAI batch API for 50% off again, in exchange for up-to-24-hours of delay in getting the results.
It's also worth comparing these prices with GPT-4o's: at $5/million input and $15/million output GPT-4o mini is 33x cheaper for input and 25x cheaper for output!
OpenAI point out that "the cost per token of GPT-4o mini has dropped by 99% since text-davinci-003, a less capable model introduced in 2022."
One catch: weirdly, the price for image inputs is the same for both GPT-4o and GPT-4o mini - Romain Huet says:
The dollar price per image is the same for GPT-4o and GPT-4o mini. To maintain this, GPT-4o mini uses more tokens per image.
Also notable:
GPT-4o mini in the API is the first model to apply our instruction hierarchy method, which helps to improve the model's ability to resist jailbreaks, prompt injections, and system prompt extractions.
My hunch is that this still won't 100% solve the security implications of prompt injection: I imagine creative enough attackers will still find ways to subvert system instructions, and the linked paper itself concludes "Finally, our current models are likely still vulnerable to powerful adversarial attacks". It could well help make accidental prompt injection a lot less common though, which is certainly a worthwhile improvement.
Context caching for Google Gemini (via) Another new Gemini feature announced today. Long context models enable answering questions against large chunks of text, but the price of those long prompts can be prohibitive - $3.50/million for Gemini Pro 1.5 up to 128,000 tokens and $7/million beyond that.
Context caching offers a price optimization, where the long prefix prompt can be reused between requests, halving the cost per prompt but at an additional cost of $4.50 / 1 million tokens per hour to keep that context cache warm.
Given that hourly extra charge this isn't a default optimization for all cases, but certain high traffic applications might be able to save quite a bit on their longer prompt systems.
It will be interesting to see if other vendors such as OpenAI and Anthropic offer a similar optimization in the future.
Update 14th August 2024: Anthropic's Claude now has its own version of prompt caching.
Hello GPT-4o. OpenAI announced a new model today: GPT-4o, where the o stands for "omni".
It looks like this is the gpt2-chatbot we've been seeing in the Chat Arena the past few weeks.
GPT-4o doesn't seem to be a huge leap ahead of GPT-4 in terms of "intelligence" - whatever that might mean - but it has a bunch of interesting new characteristics.
First, it's multi-modal across text, images and audio as well. The audio demos from this morning's launch were extremely impressive.
ChatGPT's previous voice mode worked by passing audio through a speech-to-text model, then an LLM, then a text-to-speech for the output. GPT-4o does everything with the one model, reducing latency to the point where it can act as a live interpreter between people speaking in two different languages. It also has the ability to interpret tone of voice, and has much more control over the voice and intonation it uses in response.
It's very science fiction, and has hints of uncanny valley. I can't wait to try it out - it should be rolling out to the various OpenAI apps "in the coming weeks".
Meanwhile the new model itself is already available for text and image inputs via the API and in the Playground interface, as model ID "gpt-4o" or "gpt-4o-2024-05-13". My first impressions are that it feels notably faster than gpt-4-turbo.
This announcement post also includes examples of image output from the new model. It looks like they may have taken big steps forward in two key areas of image generation: output of text (the "Poetic typography" examples) and maintaining consistent characters across multiple prompts (the "Character design - Geary the robot" example).
The size of the vocabulary of the tokenizer - effectively the number of unique integers used to represent text - has increased to ~200,000 from ~100,000 for GPT-4 and GPT-3.5. Inputs in Gujarati use 4.4x fewer tokens, Japanese uses 1.4x fewer, Spanish uses 1.1x fewer. Previously languages other than English paid a material penalty in terms of how much text could fit into a prompt, it's good to see that effect being reduced.
Also notable: the price. OpenAI claim a 50% price reduction compared to GPT-4 Turbo. Conveniently, gpt-4o costs exactly 10x gpt-3.5: 4o is $5/million input tokens and $15/million output tokens. 3.5 is $0.50/million input tokens and $1.50/million output tokens.
(I was a little surprised not to see a price decrease there to better compete with the less expensive Claude 3 Haiku.)
The price drop is particularly notable because OpenAI are promising to make this model available to free ChatGPT users as well - the first time they've directly made their "best" model available to non-paying customers.
Tucked away right at the end of the post:
We plan to launch support for GPT-4o's new audio and video capabilities to a small group of trusted partners in the API in the coming weeks.
I'm looking forward to learning more about these video capabilities, which were hinted at by some of the live demos in this morning's presentation.
Gemini 1.5 Pro public preview (via) Huge release from Google: Gemini 1.5 Pro—the GPT-4 competitive model with the incredible 1 million token context length—is now available without a waitlist in 180+ countries (including the USA but not Europe or the UK as far as I can tell)... and the API is free for 50 requests/day (rate limited to 2/minute).
Beyond that you’ll need to pay—$7/million input tokens and $21/million output tokens, which is slightly less than GPT-4 Turbo and a little more than Claude 3 Sonnet.
They also announced audio input (up to 9.5 hours in a single prompt), system instruction support and a new JSON mod.
The new Claude 3 model family from Anthropic. Claude 3 is out, and comes in three sizes: Opus (the largest), Sonnet and Haiku.
Claude 3 Opus has self-reported benchmark scores that consistently beat GPT-4. This is a really big deal: in the 12+ months since the GPT-4 release no other model has consistently beat it in this way. It’s exciting to finally see that milestone reached by another research group.
The pricing model here is also really interesting. Prices here are per-million-input-tokens / per-million-output-tokens:
Claude 3 Opus: $15 / $75
Claude 3 Sonnet: $3 / $15
Claude 3 Haiku: $0.25 / $1.25
All three models have a 200,000 length context window and support image input in addition to text.
Compare with today’s OpenAI prices:
GPT-4 Turbo (128K): $10 / $30
GPT-4 8K: $30 / $60
GPT-4 32K: $60 / $120
GPT-3.5 Turbo: $0.50 / $1.50
So Opus pricing is comparable with GPT-4, more than GPT-4 Turbo and significantly cheaper than GPT-4 32K... Sonnet is cheaper than all of the GPT-4 models (including GPT-4 Turbo), and Haiku (which has not yet been released to the Claude API) will be cheaper even than GPT-3.5 Turbo.
It will be interesting to see if OpenAI respond with their own price reductions.