benchr
How to use the comparison
Use this page when two models look close on a single metric but imply different production trade-offs. Price, context window, named benchmark variants, and editorial capability profiles sit together; missing values remain blank instead of being guessed.
A cheaper model can win if your workload is high-volume and failure is easy to review. A more expensive model can still be the rational choice when one additional successful code repair, research answer, or agent step saves more than the token premium. The comparison is meant to make that decision explicit.
What the data includes
The table uses the same model index as the pricing pages: official token prices, provider-published context limits, public benchmark figures where available, and benchr editorial estimates where providers do not publish directly comparable scores. Estimated fields are described on the methodology page.
For cost modeling, treat the listed input and output prices as a starting point. Real bills depend on your input/output ratio, cache hit rate, batch usage, retries, and routing strategy. For a workload-specific estimate, use the Cost Calculator after narrowing the shortlist here.
Ready-made comparisons
Open a stable, shareable comparison when one of these common shortlists matches your decision:
- GPT-5 vs Claude Opus 4.8 — general capability, price, and context.
- GPT-5 vs Gemini 3.5 Flash — quality-first versus high-throughput API work.
- Claude Sonnet 4.6 vs GPT-5 — daily production work and long-context trade-offs.
- DeepSeek V4-Pro vs GPT-5 — hosted open-weight economics versus a closed model.
- DeepSeek V4-Pro vs Claude Sonnet 4.6 — coding cost and provider trade-offs.