We can reasonably expect that pricing for 1.5 Pro should be similar to 1.0 Pro. Pricing for 1.0 Pro is $0.000125 / 1K characters.
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Compare that to $0.01 / 1K tokens for GPT4-Turbo. Rule of thumb is about 4 characters / token, so that’s $0.0005 for 1.5 Pro vs $0.01 for GPT-4, or a 20x difference in Gemini’s favor.
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So Google will be providing a model that is arguably superior to GPT4 overall at a price similar to GPT-3.5.
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If OpenAI isn’t able to respond with a better and/or more efficient model soon Google will own the API market, and that is OpenAI’s main revenue stream.
$ ./src/jose
Parameters:
-train <file> (mandatory argument)
Use text data from <file> to train the model
-word-output <file>
Use <file> to save the resulting word vectors
-context-output <file>
Use <file> to save the resulting word context vectors
-doc-output <file>
Use <file> to save the resulting document vectors
-size <int>
Set size of word vectors; default is 100
-window <int>
Set max skip length between words; default is 5
-sample <float>
Set threshold for occurrence of words. Those that appear with higher frequency in the
training data will be randomly down-sampled; default is 1e-3, useful range is (0, 1e-3)
-negative <int>
Number of negative examples; default is 2
-threads <int>
Use <int> threads; default is 20
-margin <float>
Margin used in loss function to separate positive samples from negative samples; default is 0.15
-iter <int>
Run more training iterations; default is 10
-min-count <int>
This will discard words that appear less than <int> times; default is 5
-alpha <float>
Set the starting learning rate; default is 0.04
-debug <int>
Set the debug mode (default = 2 = more info during training)
-save-vocab <file>
The vocabulary will be saved to <file>
-read-vocab <file>
The vocabulary will be read from <file>, not constructed from the training data
-load-emb <file>
The pretrained embeddings will be read from <file>
Examples:
./jose -train text.txt -word-output jose.txt -size 100 -margin 0.15 -window 5 -sample 1e-3 -negative 2 -iter 10