Side-by-side model response tester
Choosing between models is a lot easier when you can see them answer the same prompt at the same time. This split-screen tool runs one prompt against two models — any mix of OpenAI, Anthropic, and Google — using your own API keys, and shows both responses next to each other along with latency and token usage. It turns model selection from guesswork into a direct, repeatable comparison.
When model selection actually matters
For most casual use, any of the leading frontier models will produce acceptable output. The model choice matters when you have a specific, high-volume, or high-stakes use case:
Cost at scale. If you’re making 10,000 API calls per day, a model that produces equally good output with 40% fewer tokens saves meaningfully. The only way to know which model is more concise on your prompts is to test it — general benchmarks don’t predict per-prompt token counts.
Quality on your specific task. Models have different strengths. Some are noticeably better at structured data extraction, others at nuanced creative writing, others at code. Benchmark rankings reflect averages across diverse tasks; your specific task may look quite different. A 5-minute direct comparison on a representative sample of your real prompts tells you more than any leaderboard.
Latency for real-time applications. If a model is slower to respond by 2 seconds on average, that is noticeable in a chat UI and potentially a product problem. Measuring time-to-first-token and total response time on real prompts is the only way to know how a model will feel to your users.
Safety and refusal behaviour. Different models have different thresholds for refusing edge-case requests. If your application touches topics that are occasionally sensitive, testing the specific prompts that matter to you is more useful than reading safety documentation.
How it works
You configure two sides independently: a provider and model for A and for B, plus the matching API key for each provider. You write a single system and user prompt; both sides receive it identically, with the same temperature and max-token settings, so the only variable is the model. When you run, the tool fires both requests in parallel, times each one, and renders the two responses side by side with their token counts. The two calls are independent — if one side errors on a bad key or rate limit, the other still returns. Nothing is stored; your keys live only in the page until you refresh.
What to look at in the comparison
Beyond “which answer is better,” the useful dimensions to compare:
- Completeness: Did one model answer all parts of the question and the other only part?
- Format fit: Which response format works better for your downstream use — structured or prose, headers or flowing text?
- Token count: For the same quality of answer, fewer tokens means lower cost per call at scale.
- Latency: Visible in the timing; for streaming use cases, time-to-first-token matters more than total time.
- Refusal behaviour: Did one model add unnecessary caveats or refuse a legitimate request? Did one take inappropriate action at the edges of policy?
Tips and notes
- Hold everything else constant. Same prompt, temperature, and max tokens — that is the whole point of a fair comparison.
- Run more than once. Latency fluctuates and higher-temperature outputs vary; a few runs give a truer picture than one.
- Compare cost, not just quality. A model that is marginally better but twice the tokens may lose on price at scale — watch the token counts.
- Mix providers freely. Pit GPT-4o against Claude or Gemini directly; just supply the key for each provider you pick.