AI API pricing in 2026 is both competitive and confusing. Every provider lists per-token rates, but the real cost depends on output volume, context size, model tier, and how much you can optimize prompts. This guide summarizes pricing across eight major providers using the data in our calculator and explains how to compare them in a way that reflects real usage.
The pricing basics (quick refresher)
Most providers charge per million tokens, split into input (prompt) and output (completion). Output is usually more expensive. Total cost is driven by three things:
- How many tokens you send and receive per request
- How many requests you make
- Which model tier you choose
That sounds simple, but a small change in output length can dwarf the input cost. Always estimate total output tokens when comparing models.
Provider snapshots and model tiers
Below is a concise pricing view for each provider with typical flagship and efficient tiers from our data. Use this to anchor your comparisons.
OpenAI
OpenAI's lineup is broad, with multiple flagship and efficient models. GPT-5 is priced at $1.25 input / $10 output per 1M tokens. For budget workloads, GPT-5 nano is $0.05 / $0.40, and GPT-5 mini is $0.25 / $2.00. OpenAI also offers reasoning-focused o-series models; for example, o3 is $2.00 / $8.00 and o3-pro is $20.00 / $80.00.
Anthropic
Anthropic's Claude family has clear tiering. Claude Opus 4.6 is $5.00 / $25.00, Claude Sonnet 4.5 is $3.00 / $15.00, and Claude Haiku 4.5 is $1.00 / $5.00. Older Claude 3.5 and Claude 3 Opus models remain in the catalog with higher output rates.
Google's Gemini models span 1M to 2M context windows. Gemini 3 Pro is $2.00 / $12.00, while Gemini 3 Flash is $0.50 / $3.00. The Gemini 2.5 Pro tier is $1.25 / $10.00, and Gemini 2.5 Flash is $0.30 / $2.50. The older Gemini 1.5 Flash remains among the cheapest at $0.075 / $0.30.
Mistral AI
Mistral is aggressively priced. Mistral Large 3 is $0.50 / $1.50, Mistral Medium 3 is $0.40 / $2.00, and Mistral Small 3.2 is $0.10 / $0.30. If cost efficiency is your top priority, Mistral is often a strong baseline.
DeepSeek
DeepSeek's V3.2 and R1 V3.2 models are priced at $0.28 / $0.42. That's unusually low for a model positioned as strong at code and reasoning. If your workload does not need multimodal input, DeepSeek is one of the most cost-effective options.
Meta (via Together AI)
Meta's open-source Llama 3.1 models are available through inference providers. Llama 3.1 405B is $0.88 / $0.88, Llama 3.1 70B is $0.52 / $0.52, and Llama 3.1 8B is $0.10 / $0.10. These models are compelling for teams that want predictable, symmetric input/output pricing.
xAI
xAI's Grok 3 flagship is $3.00 / $15.00, with Grok 3 Mini at $0.30 / $0.50. Grok sits near the middle of the market on price and is often evaluated for reasoning-heavy workloads.
Cohere
Cohere's Command R+ is $2.50 / $10.00, while Command R is $0.15 / $0.60. Cohere is popular for enterprise RAG and tool-use workflows where strong retrieval support matters.
How to compare providers without getting misled
Raw price per million tokens is not enough. Use this checklist instead:
- Compare output pricing first. It usually drives the total bill.
- Look at context window limits. If your prompt is larger than the limit, you pay for chunking overhead.
- Track real output length. A model that outputs longer responses can cost more even if the per-token rate is lower.
- Match tier to task. Use efficient models for routine tasks and route hard cases to premium models.
A practical pricing workflow
If you are choosing a provider for production, follow this flow:
- Estimate your average input and output tokens per request.
- Pick three candidate models from different tiers.
- Compare monthly cost using your request volume.
- Run a small quality evaluation and choose the lowest cost model that meets your acceptance threshold.
This approach keeps the decision grounded in cost and quality rather than hype.
Final thoughts
The 2026 market is rich with choice. OpenAI and Anthropic remain premium options, while Google offers massive context windows, and Mistral and DeepSeek push aggressive pricing. Meta's Llama models provide stable open-source economics, and Cohere and xAI each have distinct strengths.
If you want a quick, concrete comparison, use the AI Cost Check. You can plug in your real usage and instantly see how each provider's pricing tier impacts your budget.