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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.

Kernpunkte

  • 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.

Häufige Fragen

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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