Picking a model from a leaderboard is a bad way to pick a model. Leaderboards measure benchmarks; you need to measure *your* prompts. Here's the workflow.
1. Open the playground
The NewToken playground puts 230+ models behind one chat interface. No per-vendor accounts, no API keys to rotate for testing — just pick a model and talk to it.
2. Fix your prompt, sweep the models
Write your real task — the exact prompt your product will send. Then run it across a ladder of models: a free one, a cheap flash-tier one, a mid-tier, and the frontier model. For most product tasks, the mid-tier wins on cost-quality tradeoff.
3. Tune the knobs that matter
- Temperature — lower for extraction and classification, higher for ideation.
- Reasoning effort — on reasoning models, this is the biggest cost lever. Low effort often passes your bar at a fraction of the tokens.
- Max tokens — cap it to your real output length; runaway generations are a silent bill multiplier.
4. Read the cost badge
Every playground message shows its real cost. Multiply by your expected volume — that's your monthly bill for this model. Repeat for the next model and pick the cheapest one that passes your quality bar.
5. Ship behind the same endpoint
Because the playground uses the same OpenAI-compatible API you'll use in production, the model you picked is the model you call — one baseURL, one key, no SDK rewrites when you swap models later.
Selected models in the playground are completely free, so step 1 costs nothing.