Compare Two LLM Providers — Token Price, Speed & Intelligence
Select any two providers to see token pricing, latency, throughput, context window, and strengths side-by-side.
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Click any provider to add it to the comparison, then click Compare. Hover for the link to view its full provider page.
What this comparison tool measures
The LLM provider comparison tool shows a side-by-side breakdown of input token price, output token price, context window size, median latency (TTFT in ms), throughput (tokens/sec), vision support, function calling support, and an intelligence score derived from MMLU, HumanEval, and MATH benchmarks. Prices are updated daily from provider pricing pages. Latency and throughput figures are measured at p50 load — real-world performance at high concurrency will differ. Use the tool to identify which provider offers the best cost-quality trade-off for your specific workload.
OpenAI vs Anthropic — the key differences
OpenAI and Anthropic are the two dominant frontier model providers. GPT-4o ($2.50/1M input, $10/1M output) and Claude 3.5 Sonnet ($3/1M input, $15/1M output) are closely matched on intelligence benchmarks, but differ in strengths: GPT-4o excels at code generation and tool use; Claude 3.5 Sonnet is preferred for long-document analysis and instruction following. OpenAI offers a broader model family (o1, o3, GPT-4o mini) and more mature API features (Assistants, fine-tuning, batch). Anthropic's prompt caching reduces costs by up to 90% for repeated context — critical for RAG and multi-turn applications.
Google vs OpenAI for high-volume workloads
Google's Gemini 1.5 Flash ($0.075/1M input tokens) is the most cost-effective frontier-adjacent model for high-volume workloads — 33× cheaper than GPT-4o at comparable quality for many tasks. Gemini 1.5 Pro offers a 1M-token context window, making it the only viable option for full-document analysis without chunking. For workloads exceeding 100M tokens/month, Google's pricing tiers and committed-use discounts through Vertex AI can reduce costs further. Compare Google vs OpenAI to see the full pricing and capability breakdown.
Open-weight inference providers: Groq, Together AI, Fireworks AI
For workloads that don't require frontier model quality, open-weight inference providers offer dramatically lower costs. Groq serves Llama 3.1 70B at $0.59/1M input tokens with sub-100ms TTFT — 4× cheaper than GPT-4o mini and 10× faster. Together AI and Fireworks AI offer a broader model selection (Llama, Mistral, Qwen, DeepSeek) with competitive pricing and higher rate limits. Cerebras's wafer-scale hardware delivers the highest throughput for Llama models — up to 2,100 tokens/sec — making it the best choice for latency-critical applications that can use open-weight models.
How to calculate true LLM API cost
True LLM API cost depends on your input/output token ratio, not just the headline price. A RAG pipeline with 2,000-token prompts and 200-token outputs has a 10:1 input/output ratio — optimise for input price. A code generation assistant with 500-token prompts and 2,000-token outputs has a 1:4 ratio — output price dominates. For Claude and OpenAI, prompt caching can reduce effective input cost by 50–90% for repeated system prompts. Batch APIs (OpenAI, Anthropic) offer 50% discounts for non-real-time workloads. Use the LLM ROI Calculator to model your exact workload and compare providers on total monthly cost.