Side-by-side pricing for every major AI model. Sort by cost, filter by provider, adjust for your usage.
| Model | Input $/1M | Output $/1M | Your Cost (30d) | Relative |
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For high-volume production workloads, Gemini 1.5 Flash ($0.075/$0.30 per 1M) and Mistral Small ($0.10/$0.30) are the cheapest capable models. GPT-4o-mini ($0.15/$0.60) offers excellent quality for the price from OpenAI. Claude 3.5 Haiku ($0.80/$4.00) is the cheapest Anthropic option.
LLM prices have dropped ~80% per year since 2023. GPT-4-level quality that cost $100/1M tokens in 2023 is now available for $2-3/1M. Expect continued price cuts, especially as Gemini and open-source models (Llama) put competitive pressure on OpenAI and Anthropic.
Per-token price is not the only cost. Consider: latency (slower models increase infrastructure costs), accuracy (cheap models may need more retries), rate limits (higher tiers cost more), and network egress. A model that's 2× cheaper but requires 1.5× more requests nets you only 25% savings.
They're priced similarly ($2.50 vs $3.00 input, $10 vs $15 output). GPT-4o has a slight edge on coding and multimodal tasks; Claude 3.5 Sonnet is preferred for long-document analysis and nuanced writing. For most applications, performance is comparable — test on your specific task before committing.
The weights are free to download, but you pay for compute to run them. Via cloud providers (Together.ai, Fireworks, Groq), Llama 3.1 70B costs ~$0.59/$0.79 per 1M tokens. Running it yourself requires a multi-GPU server (~$1-3/hour on AWS). At <10M tokens/day, hosted APIs are almost always cheaper than self-hosting.
We show the standard API pricing without any discounts, caching, or batch pricing applied. Enterprise customers, high-volume users, and batch API users pay less. Always verify current prices on official provider pages as they change frequently.
When evaluating LLMs in 2026, don't just focus on training and inference costs. Factor in deployment expenses, including server requirements, scaling solutions, and ongoing maintenance. Many organizations overlook these costs, leading to budget overruns. Additionally, consider the geographical distribution of your user base, as latency can impact user experience and necessitate additional infrastructure investments. Always calculate total cost of ownership (TCO) for a more accurate comparison between models.