AI model comparison

Claude Sonnet 4.5 vs Gemini 2.5 Pro

Comparing two high-capability models for knowledge-worker tasks such as summarization, multi-step reasoning, and working across very long inputs.

Pricing & context

All figures below are list prices pulled directly from the Harpd pricing registry (last verified 2026-08-18). Prices change often — open each model’s source link to confirm before budgeting.

MetricClaude Sonnet 4.5Gemini 2.5 Pro
ProviderAnthropicGoogle
Input / 1M tokens$3$1
Output / 1M tokens$15$10
Cached input / 1M tokens$0.3$0.13
Batch discount50% off50% off
Context window200,000 tokens1,000,000 tokens
SourceAnthropic pricing ↗Google pricing ↗
Claude Sonnet 4.5

Capabilities

Claude Sonnet 4.5 emphasizes agentic reliability and careful structured output, with a 200k context window and prompt caching for stable prefixes.

Gemini 2.5 Pro

Capabilities

Gemini 2.5 Pro offers a 1M token context window, native multimodal input, and strong reasoning, with built-in prompt caching for long contexts.

Recommendation

Neither model dominates the other on every axis, so the decision should follow your data and latency needs. Gemini 2.5 Pro’s 1M-token window is compelling for massive-context tasks, while Claude Sonnet 4.5 is often preferred for agentic and structured-output reliability. Run both against your real tasks on ModelSwitch to see which actually passes before switching.

Updated 2026-08-18.

Answer

Price tells you what a model costs. It does not tell you whether it can replace your current model on your real tasks.

Run your real tasks
Evidence
  • Benchmarks measured on Harpd are planned — see /benchmarks/.

Explore related tools

Model Compare

Side-by-side pricing and capability explorer for every model in the registry.

LLM Pricing

Full list-price table across providers, updated from official sources.

Cost per Successful Task

Why raw token price hides the real cost of failed or retried calls.

ModelSwitch

Find the cheapest model that passes your real tasks, measured on your workload.

Frequently asked questions

How do I know if a cheaper model is safe to use?
Price only tells you cost, not whether a model preserves your quality bar on real work. The safe path is to run a representative sample of your actual tasks through both models and compare outputs against your acceptance criteria. Harpd’s ModelSwitch does exactly this — it switches only once a cheaper model passes your tasks, not on price alone.
Is price a good proxy for quality?
No. List price reflects positioning and context length, not how a model performs on your specific workload. A cheaper model can be better for narrow tasks and worse for others. Treat price as one input to a cost-vs-capability decision, and validate capability with real-task testing rather than assuming a ranking.
What is cost per successful task?
Cost per successful task divides your total spend by the tasks that actually succeeded, folding in retries and failures that still cost tokens. A model with a lower per-token price can end up more expensive per successful task if it fails or retries more often. See /cost-per-successful-task/ for the full method.
How does ModelSwitch test a replacement model?
ModelSwitch runs your real tasks against a candidate model and compares results to your current one using your defined acceptance checks. It switches only when the cheaper model passes, and reports the measured savings. It does not rely on published benchmarks or price as a proxy for quality.