COMPARE02

Compare two frontier models on coding, reasoning, and price

Structured head-to-head comparison for choosing between the newest LLMs.

The Prompt

Compare {model_a} and {model_b} for a real production use case.

Task type: {task_type}
Volume: {requests_per_day}
Latency requirement: {realtime_or_batch}

Compare on:
1. Coding accuracy (with a benchmark or citation)
2. Reasoning depth on multi-step problems
3. Cost per 1M input + output tokens
4. Latency at p50 and p95
5. Where each one visibly fails

End with one clear recommendation and the exact scenario I should switch to the other.

Try in Claude →Try in ChatGPT →

Example Output

Claude Opus 4.7 vs GPT-5.6 for a customer support agent (10k requests/day, realtime):

- Coding: Opus edges ahead on multi-file refactors; GPT is faster on isolated snippets.
- Reasoning: Opus wins on 3+ step problems; GPT hallucinates less on factual lookups.
- Cost: GPT-5.6 is ~30% cheaper per 1M tokens; Opus wins on cache hits.
- Latency p50: GPT 380ms, Opus 620ms. p95: GPT 900ms, Opus 1.4s.

Recommendation: GPT-5.6 for volume + speed. Switch to Opus when your escalation queue starts hitting complex multi-turn cases.

When to Use

Deciding between two LLMs for a new feature. Justifying a model switch to your team. Cost modeling for a client project.

Curated by Akash Rana, Editor