A decision framework, not a winner
Both providers ship multiple models across a range of sizes and prices, and both update frequently. Instead of asking "which is best?", score each option against the dimensions your workload actually cares about. Weight the dimensions, then decide.
- Quality and reasoning on your specific task
- Latency and throughput at your traffic pattern
- Context-window needs (document size, conversation length)
- Cost at your expected monthly volume
- Governance, data-handling, and deployment constraints
- Vendor-concentration risk
Quality and reasoning
Public benchmarks rarely match your workload. The reliable approach is to build a small evaluation set of real examples from your domain and run the same prompts through comparable models from each provider. Judge on the outcomes you care about — correctness, tone, format adherence, refusal behaviour — not on a single leaderboard score. We do not publish our own benchmark scores; run your own on data that represents your use case.
Context window and latency
If you routinely process long documents or maintain long conversations, compare each provider's available context windows and how price scales as prompts grow. If you serve interactive users, measure real end-to-end latency for your prompt sizes rather than trusting headline numbers.
Tip: use the model comparison tool to line up context windows and current rates from both providers in one view.
Cost at your expected volume
A model that is cheaper per token can still cost more overall if it produces longer outputs or needs bigger prompts to hit your quality bar. Estimate cost using your real input/output ratio and volume, not the raw per-token rate. Both providers offer caching and batch discounts — factor those into the comparison for workloads that qualify.
Governance and vendor risk
- Data handling and compliance: confirm each provider's data-retention and regional options meet your requirements.
- Deployment surface: consider whether you need a specific cloud, region, or enterprise agreement.
- Concentration risk: designing prompts and code to be portable across providers reduces lock-in and gives you leverage if pricing or availability changes.
Scenario-based guidance (illustrative)
These are reasoning patterns, not verdicts: your evaluation on real data should always override a general pattern.
- High-volume classification: the deciding factor is usually cost-per-outcome at scale — test the cheapest adequate model from each provider.
- Long-document analysis: context-window capacity, long-prompt pricing, and caching support tend to dominate the decision.
- Complex coding or reasoning: quality on your own eval set matters most, since fewer retries and corrections often outweigh a lower per-token rate.
- Regulated or region-specific deployments: governance and deployment options may decide it before cost or quality do.
Whichever way you lean, confirm current rates and capabilities on each provider's official pricing page, and revisit the decision periodically — both providers move quickly.
Pricing and capabilities change. Provider rates, model line-ups, and features are updated by the providers over time. Any figures on this page are clearly labelled illustrative planning assumptions, not quotes. Official sources are checked on a recurring schedule. Unusual or structural changes are held for human review. See the Pricing Sources & Data Status page for verification dates, and always confirm current rates on the official provider pages below.
Official sources
Confirm current pricing and capabilities directly with each provider: