LLM playground: compare Claude, GPT and DeepSeek before you code

Before you wire a model into your app, it helps to see how it actually responds to your prompt. The apillms Playground lets you do that in the browser: pick a model, set a system prompt, adjust temperature and output length, and watch the response stream in. When you're happy with the setup, the Code button turns it into a snippet you can drop straight into your project.

This post walks through each control and how to use it before you write a single line of integration code.

Where to find it

Log in at /login and open Playground from the console. If you haven't signed up yet, new accounts get free starter credits, so you can run a few tests without reaching for a card.

Choosing a model

The model dropdown lists everything available on apillms, including Claude, GPT, Grok, DeepSeek and Kimi models. Full details and filters by capability live on /models, but the Playground dropdown is faster when you already know roughly what you want to try.

If you're not sure which model fits your task, a few starting points:

• For coding and agent work, see Claude vs GPT vs DeepSeek vs Grok: which LLM to use for coding.
• For cost-sensitive, high-volume chat, deepseek-v4-flash is the cheapest option on the list.
• For a quick side-by-side on price, check /compare, which runs a live comparison against OpenRouter.

It's worth testing the same prompt across two or three models before committing. Response style, verbosity and how well a model follows your system prompt can differ enough to change which one you'd actually ship with.

System prompt and temperature

The system prompt field sets the model's role and constraints, same as the system parameter you'd pass through the API. This is the first thing worth iterating on: a vague system prompt gives vague answers, and testing it live in the Playground is much faster than redeploying code to check a wording change.

Temperature controls how deterministic the output is. Lower values (closer to 0) give more consistent, predictable answers, useful for structured output, extraction, or code generation. Higher values give more varied, creative phrasing, useful for brainstorming or copywriting. If you're not sure what value to use, start low and raise it only if the output feels too rigid or repetitive.

Max output tokens

This caps how long the response can be. It's not just a cosmetic setting: output tokens are usually the most expensive part of a request. For example, on $1.37 per 1M output tokens, a model that rambles for 2,000 tokens instead of 400 costs five times as much for the same answer.

Set max output tokens to roughly what you expect a real response to need. If your production use case is short JSON replies, cap it low in the Playground too, so you're testing the same conditions you'll actually run.

Streaming on or off

The stream toggle switches between getting the full response at once or watching it appear token by token, the same choice you'll make with the stream parameter in your own API calls. Streaming is useful for checking how a response "feels" in a chat UI, and for catching cases where a model front-loads filler before getting to the useful part of the answer. Turning streaming off is sometimes easier when you're mainly checking the final content, like testing a prompt that returns structured data.

From Playground to code with one click

Once your prompt, model, temperature, and token limit are dialed in, click Code. It generates a snippet using the same settings you just tested, so you don't have to manually transcribe values. From there you can:

• Copy a Python or Node snippet using the OpenAI-compatible endpoint at https://apillms.com/v1 — see the Python quickstart or Node.js quickstart for the surrounding setup.
• Use the Anthropic-compatible base URL (no /v1 suffix) if you're calling Claude models through the Anthropic SDK — details in Use the Anthropic SDK with apillms as a Custom Base URL.
• Grab a cURL command if you just want to test from a terminal or script.

A minimal cURL request looks like this once you have your key from /keys:

curl https://apillms.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-sonnet-5",
    "messages": [
      {"role": "system", "content": "You are a terse code reviewer."},
      {"role": "user", "content": "Review this function for bugs."}
    ],
    "temperature": 0.2,
    "max_tokens": 500,
    "stream": false
  }'

The Code button saves you from typing this out by hand, especially the parts you'd otherwise forget, like matching the exact model id or temperature value you landed on during testing.

A quick workflow for picking a model

A practical way to use the Playground before building anything:

1. Write the system prompt you plan to use in production.
2. Run it against two or three candidate models with the same user message.
3. Set max output tokens to match your real use case, not higher.
4. Compare the responses for quality, tone, and how closely they follow instructions.
5. Once you've picked a model, hit Code and paste the snippet into your project.

This matters more than it sounds. If you're building something with heavy repeated context, like a coding agent, the model and prompt structure you choose has a real effect on cost. The Claude Code token costs post and prompt caching guide both cover how that adds up once you're past the testing stage.

Takeaway

The Playground is the fastest way to compare models and settings before you touch code. Tune the system prompt, temperature, and max output tokens, toggle streaming to see how it behaves, and use the Code button to turn your tested setup into a working snippet. Check /pricing for per-pack token estimates once you're ready to move from testing to real usage.

FAQ

Does using the Playground cost credits?

Yes, Playground requests use the same model pricing as the API, so each test consumes credits just like a normal request would.

Can I test image or video models in the Playground?

The Playground is for chat/text models. Image and video generation have their own Image Studio and Video Studio with model, prompt and cost settings specific to those outputs.

What does the Code button actually generate?

It exports a code snippet (such as a cURL command or SDK call) that matches the model, system prompt, temperature, max output tokens and streaming setting you tested in the Playground.

Should I test with streaming on or off?

Either works for testing; streaming is closer to what a chat interface will show, while non-streaming is simpler when you only care about the final response content.