AI model comparison

Claude Sonnet 4.5 vs GPT-4.1

Comparing two widely-used large-context flagship models for production agent and assistant workloads that mix long documents with structured extraction and tool calls.

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 4.5GPT-4.1
ProviderAnthropicOpenAI
Input / 1M tokens$3$2
Output / 1M tokens$15$8
Cached input / 1M tokens$0.3$0.2
Batch discount50% off50% off
Context window200,000 tokens1,000,000 tokens
SourceAnthropic pricing ↗OpenAI pricing ↗
Claude Sonnet 4.5

Capabilities

Claude Sonnet 4.5 offers strong tool-calling and agentic reliability, well-regarded structured-output and long-document reasoning, with a 200k token context window and published prompt caching for repeat prefixes.

GPT-4.1

Capabilities

GPT-4.1 provides a 1M token context window, strong instruction-following and code generation, and prompt caching, making it a common choice for very long-context retrieval and code-heavy tasks.

Recommendation

Both are strong general-purpose flagships, so the right pick depends on your workload rather than a fixed quality ranking. If your tasks stretch past 200k tokens or lean heavily on code, GPT-4.1’s larger window is worth testing; for agentic and document-heavy flows, Claude Sonnet 4.5 is a frequent default. Because price does not predict which passes your real tasks, validate both on a representative sample before committing.

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/.

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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.