A practical guide to evaluating GPT, Claude, Gemini, and other large language models with repeatable, data-driven comparisons.
Get started freeStart by listing the dimensions that matter for your use case: output quality, latency, cost per token, context-window size, and instruction-following accuracy. Weight each criterion so you can score models objectively rather than relying on anecdotal impressions.
Choose at least three models that span different providers and price tiers. For example, pair a frontier model like GPT-5.2 against a cost-efficient option like DeepSeek V3 and a balanced choice like Claude Sonnet 4.5. LLMWise gives you access to nine models through a single API, making selection painless.
Send the same prompts to every model under identical settings (temperature, max tokens, system prompt). Use LLMWise Compare mode to run prompts against multiple models in parallel and collect structured output in a single request, eliminating the need to juggle separate API keys and SDKs.
Review latency, time-to-first-token, token throughput, and total cost alongside qualitative output quality. Look for patterns: one model may excel at code while another handles creative writing better. LLMWise logs every request with these metrics automatically so you can query historical data.
Use the results to build a routing strategy: assign the best model per task category and set up fallback chains for reliability. Re-run comparisons periodically as providers release updates. LLMWise Optimization policies can automate this cycle by analyzing your request history and recommending model changes.
500 free credits. One API key. Nine models. No credit card required.