Claude vs GPT, GPT vs Gemini, and the rest
Side-by-side pricing and capability notes for the models teams actually weigh against each other. No invented benchmark scores — just the facts and an honest next step.
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.
Compare →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.
Compare →GPT-4.1 vs Gemini 2.5 Pro
Comparing two large-context flagships for long-document processing, retrieval-augmented generation, and code-heavy development work.
Compare →Claude Haiku 4.5 vs GPT-5 mini
Comparing two fast, lower-cost models for high-volume classification, routing, and lightweight extraction where latency and unit price matter most.
Compare →DeepSeek V3 vs Claude Sonnet 4.5
Comparing a low-cost open-weight model with a premium managed flagship for general assistant and coding tasks where budget and reliability are both in scope.
Compare →Gemini 2.5 Flash vs Claude Haiku 4.5
Comparing two efficient models for latency-sensitive, high-volume tasks like tagging, routing, and short-form generation.
Compare →GPT-5 mini vs Llama 3.1 70B
Comparing a managed small model with a hosted open-weight model for teams weighing vendor-managed simplicity against self-hostable flexibility.
Compare →Mistral Large 3 vs Llama 3.1 70B
Comparing a managed European-hosted flagship with a popular open-weight model for teams balancing capability, data-residency, and deployment flexibility.
Compare →Pricing comes from official provider pages. Capability notes are general — the only way to know if a cheaper model is safe for your workload is to test it onModelSwitch.