Frontier AI, Frontier Engine Math, & New Models
Gemini vs Claude vs GPT-5: Benchmarks and Real Use
Frontier models differ less in headline scores than in behavior: Gemini leans into multimodal and long-context work, Claude into careful reasoning and safety, and GPT-5 into broad tool use and agentic tasks. Benchmarks are a starting filter, not a decision; the real test is your prompts, data, and constraints.
Pontos principais
- Separate capability benchmarks (reasoning, coding, math) from product benchmarks (latency, cost, tool reliability).
- Run the same prompt suite across all three with fixed temperature and decoding settings.
- Measure on your domain data, not just public datasets like MMLU or HumanEval.
- Compare context handling: effective use of long inputs, not just window size.
- Check tool calling: argument accuracy, parallel calls, and recovery from errors.
- Evaluate safety and refusal behavior against your policy, not generic red-team suites.
- Track cost per successful task, not per token, including retries and tool calls.
- Re-run evals on every model update; rankings change with each release.
Perguntas frequentes
Do benchmark leaderboards predict production performance?
Rarely. They measure curated tasks; your data, prompts, and latency budget dominate real outcomes.
Which model is best for coding?
It depends on language and repo size; test on your own codebase with hidden tests before committing.
How often should I re-evaluate?
On every provider model update and quarterly at minimum, since behavior drifts even without version changes.
Should I use one model for everything?
No. Route by task: cheap models for classification and extraction, frontier models for reasoning and agents.
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