GPT-4 32K
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Built to handle vast amounts of data, GPT-4 32K excels at delivering accurate and detailed outputs. With a 32,000 token capacity, it surpasses its predecessors in managing complex information, making it perfect for generating comprehensive responses.
| License | openai |
|---|---|
| Context window(in thousands) | 128000 |
Use cases for GPT-4 32K
- Scientific Research Analysis: Ideal for handling extensive research papers and datasets to identify trends and correlations.
- Customer Support Automation: Analyzes large volumes of customer interactions to improve automated support systems.
- Supply Chain Management: Optimizes logistics by processing large datasets from various supply chain components.
| Arena Elo | N/A |
|---|---|
| MMLU | N/A |
| MT Bench | N/A |
GPT-4 32K is not currently ranked on the Chatbot Leaderboard.
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| Throughput(output tokens per second) | N/A |
|---|---|
| Latency(seconds to first tokens chunk received) | N/A |
| Total Response Time(seconds to output 100 tokens) | N/A |
No performance metrics are available at this time.
What's Twitter saying?
- Silent Removal of GPT-4 32K Mention: OpenAI quietly removed mentions of GPT-4 32K, and it was never fully released. Azure plans to deprecate GPT-4 32K in September. Models like GPT-4 Turbo and GPT-4o have a 128 input context but only a 4K output context, similar to other models like Claude. (Source: @headinthebox)
- Analyzing Congressional Hearings with GPT-4 32K: GPT-4 32K impressively analyzed a 23-page congressional hearing document, providing detailed insights. (Source: @SullyOmarr)
- Significance of GPT-4-32K Access: Widespread access to GPT-4-32K would be a major leap, even more significant than the upgrade from GPT-3.5 to GPT-4. The model’s larger context window and power would enable complex workflows and use cases. (Source: @mckaywrigley)
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What is GPT-4-32k and how does it differ from GPT-4?
GPT-4-32k refers to a version of the GPT-4 model developed by OpenAI that can process up to 32,000 tokens in a single prompt, offering significantly more context space than the standard GPT-4 model, which supports up to 8,000 tokens. This extended token limit allows for more complex and detailed inputs and outputs, enhancing the model's ability to understand and generate longer pieces of text.
How can I get access to GPT-4-32k?
As of the last update in December 2023, GPT-4-32k access has been limited and not generally available to the public. OpenAI has initiated a selective rollout, primarily targeting select partners and developers. To stay updated on availability, check OpenAI's official announcements and consider applying for access through their Enterprise program.
Is there a GPT-4-16k version available?
No, there is no official 16k token version of GPT-4 mentioned in the discussions. The conversation mainly revolves around the standard GPT-4 model, which supports up to 8k tokens, and the GPT-4-32k version. For more information on token limits and model capabilities, refer to the OpenAI documentation.
Why is GPT-4's token limit still at 8k despite GPT-3.5 having a 16k version?
The decision on token limits involves balancing various factors, including computational resources, model performance, and user needs. While GPT-3.5 offers a version with a 16k token limit, GPT-4's initial focus has been on enhancing model sophistication and output quality within an 8k token framework. OpenAI continuously evaluates user feedback and technological capabilities, suggesting the possibility of future updates to token limits. For the latest model specifications, visit the API reference guide.
How does OpenAI decide who gets access to limited release models like GPT-4-32k?
OpenAI selects participants for limited release models based on several criteria, including the potential impact of the use case, the technical capacity to support advanced models, and the contribution to broader research and development goals. Interested parties are encouraged to apply through official channels, such as the OpenAI Enterprise program, and to provide detailed information about their intended use cases.