Phind’s new model beats GPT-4 at coding with 5x speed

Phind Model is an open-source coding model fine-tuned on CodeLlama-34B, engineered to deliver top-tier code generation with GPT-3.5-level speed and a native 16k context window.

What it does

On the HumanEval benchmark, Phind Model V7 notched a 74.7% score, beating GPT-4 on code generation accuracy.

For complex technical programming prompts, it cuts turnaround times down from GPT-4's 50 seconds to around 10 seconds. It handles code completion, bug fixing, and deep documentation lookup smoothly.

The developers note that Phind might still take a few extra iterations of back-and-forth prompts compared to GPT-4 to nail the exact solution, but the core generation quality is remarkably sharp.

How it hits 5x speed

The speed boost comes down to serious engineering on top of Nvidia hardware.

Running on Nvidia H100 GPUs alongside Nvidia's TensorRT-LLM library, Phind clocks single-stream throughput at up to 100 tokens per second. By comparison, GPT-4 typically outputs around 20 tokens per second.

It also comes with a 16k context window, giving you enough room to paste full modules, documentation, and terminal stack traces without truncation.

The model weights are fully open-sourced on Hugging Face, meaning you can pull the model and run it on your own hardware stack.

How to use it

If you want to host it yourself, the weights are ready to download on Hugging Face.

If you prefer a hosted solution, Phind's web interface gives every user 10 free queries a day, avoiding the usual $20 monthly ChatGPT fee. Power users can opt for a $15 or $30 monthly tier or test out the conversational Pair Programmer feature.

There is also a free Visual Studio Code extension. It lets you highlight code to search documentation, trigger inline rewrites, and diagnose terminal build errors directly inside your IDE.

Why it matters

For programmers, latency kills flow. Waiting 50 seconds for GPT-4 to slowly generate a script breaks your focus, while getting an answer in 10 seconds feels almost interactive.

We think Phind proves that specialized open-source models, when paired with modern inference engines like TensorRT-LLM, can genuinely challenge closed-source commercial giants in targeted workflows. It is fast, capable, and well worth testing in your daily dev loop.