# Phind
### Phind Model beats GPT-4 at coding, with GPT-3.5-like speed and 16k context
Phind now defaults to our own model that matches and exceeds GPT-4's coding abilities while running 5x faster. You can now get high quality answers for technical questions in 10 seconds instead of 50.
The current 7th-generation Phind Model is built on top of our open-source CodeLlama-34B fine-tunes that were the first models to beat GPT-4's score on HumanEval and are still the best open source coding models overall by a [wide margin](https://huggingface.co/spaces/bigcode/bigcode-models-leaderboard).
### The Phind Model V7 achieves 74.7% pass\@1 on HumanEval
This new model has been fine-tuned on an additional 70B+ tokens of high quality code and reasoning problems and exhibits a HumanEval score of 74.7%. However, we've found that HumanEval is a poor indicator of real-world helpfulness. After deploying previous iterations of the Phind Model on our service, we've collected detailed feedback and noticed that our model matches or exceeds GPT-4's helpfulness most of the time on real-world questions. Many in our Discord community have begun using Phind exclusively with the Phind Model despite also having unlimited access to GPT-4.
One of the Phind Model's key advantages is that's very fast. We've been able to achieve a 5x speedup over GPT-4 by running our model on H100s using the new TensorRT-LLM library from NVIDIA, reaching 100 tokens per second single-stream.
Another key advantage of the Phind Model is context – it supports up to 16k tokens. We currently allow inputs of up to 12k tokens on the website and reserve the remaining 4k for web results.
There are still some rough edges with the Phind Model and we'll continue improving it constantly. One area where it still suffers is consistency — on certain challenging questions where it is capable of getting the right answer, the Phind Model might take more generations to get to the right answer than GPT-4.
[Try the Phind Model](https://www.phind.com/)\
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# Agent Instructions: Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the `ask` query parameter:
```
GET https://metaverse-imagen.gitbook.io/ai-tools-research/ai-tools-main-categories/programming-and-code/code-generators/phind.md?ask=
```
The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
