Breaking: Meta's Llama 3 is out. How are you planning to evaluate it vs GPT-4?

I work at a small software agency, and we use GPT-4 via the API for generating documentation and internal admin scripts.

My plan to evaluate Llama 3 is based on four practical things:

1. **Local cost control** - Running Llama 3 locally costs $0 in API fees. The tradeoff is needing your own hardware; a basic test with the 8B model needed about 10GB of RAM and a decent CPU on my M1 Mac.

2. **Task accuracy on internal docs** - For our use case, I'll test each model on five of our real, messy internal runbooks. I'll score them 1-5 on whether the instructions they generate would actually work for a junior dev without corrections.

3. **Speed for batch jobs** - When we process multiple documents, throughput matters. In my quick test, GPT-4 via API was faster for single questions, but running Llama locally could process a batch of 50 small tasks without rate limiting, which is a win for scheduled jobs.

4. **Setup and maintenance** - GPT-4 is just an API call. Getting Llama 3 running locally took me about 90 minutes of setup (downloading, picking a tool like Ollama, troubleshooting libraries). That's a real time cost.

I'd recommend sticking with GPT-4 for now if you're a beginner and your main use is learning and scripting. Its answers are generally more reliable out of the gate. If you want to pick Llama 3, you should tell us your budget for API fees and whether you have a spare machine to run it on consistently.

> GPT-4 is just an API call.

That's the core of it for a small team. Your 90-minute setup time is a real project cost, and it's a repeating one when you need to update or troubleshoot the local stack.

If you do proceed with the batch job testing, check how Llama 3 handles variable substitution in those admin scripts. I've seen models sometimes use the wrong syntax between `$VAR`, `${VAR}`, and `$(command)`, which silently breaks the script. That's a specific failure point GPT-4 usually gets right.

Your three test ideas are solid for a beginner. Start with those exact tasks and use the same prompt for both models. Scoring is the tricky part.

For the Terraform config, run `terraform plan` on each output and see which passes validation. For the bash script debugging, execute both suggested fixes in a sandbox and compare the error output.

Don't worry about HELM benchmarks yet. Your practical tests will give you a better feel for which model fits your workflow. The real question is whether Llama 3's output is good enough to justify the zero API cost for your learning projects.

Oh, running `terraform plan` on the outputs is a great concrete test! I wouldn't have thought of that. Makes scoring way more objective than me just guessing which config looks better.

For a beginner, setting up a safe sandbox for the bash script tests sounds a bit daunting though. What's the easiest way to do that without messing up my local machine? A throwaway Docker container maybe?

Great starting list! For the Dockerfile explanation task, I'd add a specific wrinkle to really test understanding. Ask both models to not just explain, but also suggest one improvement for a multi-stage build and explain why it's better. That pushes beyond surface-level description.

I actually just did a similar comparison for my team's internal knowledge base. We found GPT-4 was slightly more consistent on edge-case bash syntax, but the quality gap for straightforward DevOps explanations was way smaller than expected. Llama 3 70B held its own.

For scoring, I kept a simple spreadsheet with pass/fail on three criteria: was the answer factually correct, was it clear for a new hire, and did it include the requested specific element? No fancy rubric needed.

For a beginner, your test list is fine. The key is using the exact same prompts for both models.

Terraform plan validation is a good objective score. For bash, use a disposable container. `docker run --rm -it ubuntu bash` works. Execute the broken script and each model's fix suggestion there. It's isolated and you can just delete it.

Ignore the advanced benchmarks. Your practical tests will tell you if the zero API cost is worth the slight accuracy trade-off for your learning use case.

Good practical start. One thing I'd add to your scoring method is consistency.

Testing three times with the same prompt can be revealing. Does Llama 3 give you the same correct answer each time, or does it occasionally veer off? For learning, inconsistency can be more frustrating than a slightly less accurate but steady model.

The Terraform plan validation suggestion from user724 is a perfect example of an objective, binary score you can use.

Your test list is fine for a beginner. The most important part is using the same prompts and having binary, automated checks.

For Terraform validation, run a `terraform fmt -check` first, then `terraform validate`. For bash debugging, measure the time it takes to get a working script. Use `time` in your sandbox.

Oh, measuring the time in the sandbox is a smart addition. I've been stuck on just pass/fail, but timing could show if one model's fix takes way longer to debug in practice.

For someone like me just starting, would you run the `time` command on the whole debugging cycle? Or just on the script execution itself once it's fixed?

You're right that `terraform plan` validation gives a clean binary outcome, but I'd add a data quality check before that step. The model might output syntactically valid HCL that still contains logical errors, like referencing a non-existent variable or using an incompatible resource argument. Those issues only surface during apply.

I'd run `terraform validate` first as a syntax gate, then `terraform plan` to catch semantic problems. The difference in failure modes between those commands gives you more granular feedback on where each model struggles.

Great starting point for a hands-on evaluation! Your three test tasks are actually a perfect beginner framework - they're concrete, relevant to your work, and you can define clear success criteria.

You've gotten excellent advice on binary scoring with `terraform validate/plan` and using disposable Docker containers. Let me add one more dimension for the Dockerfile explanation task, since it's more subjective. Ask each model to explain the same file, but target the explanation to two different audiences: a complete beginner, and a senior engineer looking for best practices. Then, judge which model better adapts its tone and depth. This tests a subtle but important skill beyond just factual recall.

I'll echo what others said about skipping the heavy academic benchmarks for now. Your personal workflow is the benchmark that matters. The real test is which model you instinctively reach for after a week of side-by-side use.

Adapting the explanation for different audiences is a sharp metric, it gets at the model's practical usefulness beyond just raw correctness. For the beginner, does it avoid jargon and introduce the core concept of a layer? For the senior engineer, does it skip the basics and point out the inefficient `apt-get update` pattern or the missing `.dockerignore`?

That said, I've found this kind of test can sometimes reveal more about our prompting than the model. You need to be very explicit with the persona in the prompt. "Explain this Dockerfile to a beginner who has never used Docker before" versus "Explain this Dockerfile to a senior infra engineer focused on image security and build performance" will get you better results.

Your last point about the instinctive reach is the real answer. After running these tests, just see which one you grab for the next quick question. That's the model that fits your brain.

That prompting point is crucial, and I think it exposes a larger evaluation challenge. When we ask for different audience adaptations, we're really testing two things at once - the model's ability and our ability to write distinct prompts. If the results are poor, is it the model or my prompt that needs work?

Your 'instinctive reach' metric cuts through that nicely. After a week of testing, which tool do you reflexively open for a quick question? That practical workflow fit often outweighs small accuracy differences on contrived tests.

It's a good reminder that our evaluation should include our own habits, not just the model's output.

You've got a solid plan for practical tests. The key is moving from subjective impressions to quantifiable metrics, even with simple tasks. For scoring, I'd recommend creating a small rubric spreadsheet. Each task gets a row, and you score outputs across a few columns.

For your Terraform config test, you can capture more than just a plan passing. Add columns for: `terraform validate` (pass/fail), `terraform plan` (pass/fail), and a count of resource arguments used that are deprecated in the current AWS provider version (you can check this with `terraform providers schema -json`). This gives you a numerical score for code quality, not just functionality.

The same principle applies to bash debugging. Beyond timing the fixed script, log the number of iteration cycles it takes. Prompt once, execute the suggestion. If it fails, feed the error back to the model. Count how many loops each model needs to reach a working state. That's a measurable efficiency metric for your actual workflow.