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+Instruction,Input,Output
+,"Flagship Models
+GPT-4o (New)
+Description: Our fastest and most affordable flagship model
+Capabilities: Text and image input, text output
+Context Length: 128k tokens
+Pricing:  $5 | Output: $15* (per 1 million tokens)
+GPT-4 Turbo
+Description: Our previous high-intelligence model
+Capabilities: Text and image input, text output
+Context Length: 128k tokens
+Pricing:  $10 | Output: $30* (per 1 million tokens)
+GPT-3.5 Turbo
+Description: Our fast, inexpensive model for simple tasks
+Capabilities: Text input, text output
+Context Length: 16k tokens
+Pricing:  $0.50 | Output: $1.50* (per 1 million tokens)
+Detailed Models Description
+GPT-4o
+GPT-4o (“o” for “omni”) is our most advanced model. It is multimodal (accepting text or image inputs and outputting text), and it has the same high intelligence as GPT-4 Turbo but is much more efficient—it generates text 2x faster and is 50% cheaper. Additionally, GPT-4o has the best vision and performance across non-English languages of any of our models. GPT-4o is available in the OpenAI API to paying customers. Learn how to use GPT-4o in our text generation guide.","Reproducible Outputs (Beta)
+Chat Completions are non-deterministic by default (which means model outputs may differ from request to request). That being said, we offer some control towards deterministic outputs by giving you access to the seed parameter and the system_fingerprint response field."
+,"Flagship Models
+GPT-4o (New)
+Description: Our fastest and most affordable flagship model
+Capabilities: Text and image input, text output
+Context Length: 128k tokens
+Pricing:  $5 | Output: $15* (per 1 million tokens)
+GPT-4 Turbo
+Description: Our previous high-intelligence model
+Capabilities: Text and image input, text output
+Context Length: 128k tokens
+Pricing:  $10 | Output: $30* (per 1 million tokens)
+GPT-3.5 Turbo
+Description: Our fast, inexpensive model for simple tasks
+Capabilities: Text input, text output
+Context Length: 16k tokens
+Pricing:  $0.50 | Output: $1.50* (per 1 million tokens)
+*Prices per 1 million tokens","Deterministic Outputs
+To receive (mostly) deterministic outputs across API calls, you can:"
+,"def get_database_info(conn):
+    """"""Return a list of dicts containing the table name and columns for each table in the database.""""""
+    table_dicts = []
+    for table_name in get_table_names(conn):
+        columns_names = get_column_names(conn, table_name)
+        table_dicts.append({""table_name"": table_name, ""column_names"": columns_names})
+    return table_dicts
+Step 2: Extract Database Schema
+python
+Copy code
+database_schema_dict = get_database_info(conn)
+database_schema_string = ""\n"".join(
+    [
+        f""Table: {table['table_name']}\nColumns: {', '.join(table['column_names'])}""
+        for table in database_schema_dict
+    ]
+)
+Step 3: Define Function Specification
+python
+Copy code
+tools = [
+    {
+        ""type"": ""function"",
+        ""function"": {
+            ""name"": ""ask_database"",
+            ""description"": ""Use this function to answer user questions about music. Input should be a fully formed SQL query."",
+            ""parameters"": {
+                ""type"": ""object"",
+                ""properties"": {
+                    ""query"": {
+                        ""type"": ""string"",
+                        ""description"": f""""""
+                            SQL query extracting info to answer the user's question.
+                            SQL should be written using this database schema:
+                            {database_schema_string}
+                            The query should be returned in plain text, not in JSON.
+                        """""",
+                    }
+                },
+                ""required"": [""query""],
+            },
+        }
+    }
+]
+Step 4: Implement SQL Query Function
+python
+Copy code
+def ask_database(conn, query):
+    """"""Function to query SQLite database with a provided SQL query.""""""
+    try:
+        results = str(conn.execute(query).fetchall())
+    except Exception as e:
+        results = f""query failed with error: {e}""
+    return results
+Step 5: Invoke Function Call Using Chat Completions API
+python
+Copy code
+# Step 1: Prompt with content that may result in function call
+messages = [{""role"": ""user"", ""content"": ""What is the name of the album with the most tracks?""}]
+response = client.chat.completions.create(
+    model='gpt-4o', 
+    messages=messages, 
+    tools=tools, 
+    tool_choice=""auto""
+)
+response_message = response.choices[0].message 
+messages.append(response_message)
+pretty_print_conversation(messages)","Example Deterministic Output API Call
+Explore the new seed parameter in the OpenAI cookbook."
+,"python
+Copy code
+thread = client.beta.threads.create(
+  messages=[
+    {
+      ""role"": ""user"",
+      ""content"": ""Create 3 data visualizations based on the trends in this file."",
+      ""attachments"": [
+        {
+          ""file_id"": file.id,
+          ""tools"": [{""type"": ""code_interpreter""}]
+        }
+      ]
+    }
+  ]
+)
+Image Input Content
+Message content can contain either external image URLs or File IDs uploaded via the File API. Only models with Vision support can accept image input. Supported image content types include png, jpg, gif, and webp. When creating image files, pass purpose=""vision"" to allow you to later download and display the input content.","json
+Copy code
+{
+  ""id"": ""run_qJL1kI9xxWlfE0z1yfL0fGg9"",
+  ...
+  ""status"": ""requires_action"",
+  ""required_action"": {
+    ""submit_tool_outputs"": {
+      ""tool_calls"": [
+        {
+          ""id"": ""call_FthC9qRpsL5kBpwwyw6c7j4k"",
+          ""function"": {
+            ""arguments"": ""{\""location\"": \""San Francisco, CA\""}"",
+            ""name"": ""get_rain_probability""
+          },
+          ""type"": ""function""
+        },
+        {
+          ""id"": ""call_RpEDoB8O0FTL9JoKTuCVFOyR"",
+          ""function"": {
+            ""arguments"": ""{\""location\"": \""San Francisco, CA\"", \""unit\"": \""Fahrenheit\""}"",
+            ""name"": ""get_current_temperature""
+          },
+          ""type"": ""function""
+        }
+      ]
+    },
+    ...
+    ""type"": ""submit_tool_outputs""
+  }
+}
+Step 4: Handle Tool Calls and Submit Outputs
+How you initiate a Run and submit tool_calls will differ depending on whether you are using streaming or not, although in both cases all tool_calls need to be submitted at the same time. You can then complete the Run by submitting the tool outputs from the functions you called. Pass each tool_call_id referenced in the required_action object to match outputs to each function call."
+,"json
+Copy code
+{
+  ""id"": ""msg_abc123"",
+  ""object"": ""thread.message"",
+  ""created_at"": 1699073585,
+  ""thread_id"": ""thread_abc123"",
+  ""role"": ""assistant"",
+  ""content"": [
+    {
+      ""type"": ""text"",
+      ""text"": {
+        ""value"": ""The rows of the CSV file have been shuffled and saved to a new CSV file. You can download the shuffled CSV file from the following link:\n\n[Download Shuffled CSV File](sandbox:/mnt/data/shuffled_file.csv)"",
+        ""annotations"": [
+          {
+            ""type"": ""file_path"",
+            ""text"": ""sandbox:/mnt/data/shuffled_file.csv"",
+            ""start_index"": 167,
+            ""end_index"": 202,
+            ""file_path"": {
+              ""file_id"": ""file-abc123""
+            }
+          }
+        ]
+      }
+    }
+  ]
+}
+Input and Output Logs of Code Interpreter
+By listing the steps of a Run that called Code Interpreter, you can inspect the code input and output logs of Code Interpreter:","# The thread now has a vector store with that file in its tool resources.
+print(thread.tool_resources.file_search)
+Step 5: Create a Run and Check the Output
+Now, create a Run and observe that the model uses the File Search tool to provide a response to the user’s question."
+,"$5.00 / 1M tokens
+Output: $15.00 / 1M tokens
+gpt-4o-2024-05-13",
+,"$5.00 / 1M tokens
+Output: $15.00 / 1M tokens
+Vision Pricing Calculator
+Resolution: 150px x 150px
+Price: $0.001275
+GPT-3.5 Turbo
+GPT-3.5 Turbo is optimized for dialog, fast, and inexpensive for simple tasks.",
+,"$0.50 / 1M tokens
+Output: $1.50 / 1M tokens
+gpt-3.5-turbo-instruct",
+,"$1.50 / 1M tokens
+Output: $2.00 / 1M tokens
+Embedding Models
+Build advanced search, clustering, topic modeling, and classification functionality.",
+,"Training: $8.00 / 1M tokens
+Input Usage: $3.00 / 1M tokens
+Output Usage: $6.00 / 1M tokens
+davinci-002",
+,"Training: $6.00 / 1M tokens
+Input Usage: $12.00 / 1M tokens
+Output Usage: $12.00 / 1M tokens
+babbage-002",
+,"Training: $0.40 / 1M tokens
+Input Usage: $1.60 / 1M tokens
+Output Usage: $1.60 / 1M tokens
+Assistants API
+The Assistants API and its tools are billed at the chosen language model's per-token input/output rates. Additional fees for tool usage:",
+,"$10.00 / 1M tokens
+Output: $30.00 / 1M tokens
+gpt-4-turbo-2024-04-09",
+,"$10.00 / 1M tokens
+Output: $30.00 / 1M tokens
+gpt-4",
+,"$30.00 / 1M tokens
+Output: $60.00 / 1M tokens
+gpt-4-32k",
+,"$60.00 / 1M tokens
+Output: $120.00 / 1M tokens
+gpt-4-0125-preview",
+,"$10.00 / 1M tokens
+Output: $30.00 / 1M tokens
+gpt-4-1106-preview",
+,"$10.00 / 1M tokens
+Output: $30.00 / 1M tokens
+gpt-4-vision-preview",
+,"$10.00 / 1M tokens
+Output: $30.00 / 1M tokens
+gpt-3.5-turbo-1106",
+,"$1.00 / 1M tokens
+Output: $2.00 / 1M tokens
+gpt-3.5-turbo-0613",
+,"$1.50 / 1M tokens
+Output: $2.00 / 1M tokens
+gpt-3.5-turbo-16k-0613",
+,"$3.00 / 1M tokens
+Output: $4.00 / 1M tokens
+gpt-3.5-turbo-0301",
+,"$1.50 / 1M tokens
+Output: $2.00 / 1M tokens
+davinci-002",
+,"$2.00 / 1M tokens
+Output: $2.00 / 1M tokens
+babbage-002",
+,"$0.40 / 1M tokens
+Output: $0.40 / 1M tokens
+FAQ
+What’s a token?
+Tokens are pieces of words used for natural language processing. For English text, 1 token is approximately 4 characters or 0.75 words. As a reference, the collected works of Shakespeare are about 900,000 words or 1.2M tokens.",