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Cost per 1M Tokens: A Developer’s Provider Comparison

Modelos · EN · 2026-10-28

Comparing AI API prices should take minutes, not a spreadsheet archaeology session. The key is to compare the cost of your workload—not whichever provider advertises the lowest input-token rate. This quick-start guide shows how to normalize pricing, compare direct providers with 59API, and check whether cheaper tokens actually produce cheaper results.

1. Separate input and output pricing

There is no universal cost per 1M tokens. Most text APIs charge separately for input and output, and additional rules can change the bill. Before comparing providers, collect these details for each exact model:

Convert every quote to the same currency and unit. Multiply a per-1,000-token price by 1,000 to get its per-1M-token equivalent. Record the date: model prices and relay discounts can change.

2. Calculate your blended cost per 1M tokens

Start with this formula for ordinary, uncached text requests:

Total cost = (input tokens ÷ 1,000,000 × input rate) + (output tokens ÷ 1,000,000 × output rate).

Then divide total cost by total tokens in millions to calculate your workload’s blended cost per 1M tokens. The following dollar rates are illustrative arithmetic examples, not current quotes from any provider:

For 8M input tokens and 2M output tokens, A costs $16, B costs $16.40, and C costs $9.80. Their blended rates are $1.60, $1.64, and $0.98 per 1M tokens.

Despite its cheaper input rate, B loses to A because output costs more. For generation-heavy workloads, that difference grows. Use token usage from representative requests rather than assuming a 50/50 split.

3. Compare providers without mixing up models

Build a shortlist from direct providers such as Anthropic, OpenAI, Google, and DeepSeek, then add relay pricing for the same available models. Use current official pricing pages and the relay’s current rate card; a historical price comparison is not a reliable purchasing quote.

Run 20–50 representative tasks and track successful results. A useful second metric is cost per accepted result = total test spend ÷ accepted results. Include retries, failed attempts that incur charges, and follow-up requests.

4. Test 59API as your low-cost route

59API offers cheap, pay-as-you-go access across major model families and positions itself among the cheapest AI API relays. It provides native, official-quality models without downgrading, making it worth checking against direct-provider prices for your chosen model.

Its API service host is https://api.59api.com. Create an API key, check the current model rate, and copy the exact model identifier and endpoint configuration from the documentation. Use the documented base URL format rather than guessing whether your client needs an additional path.

59API is compatible with Claude Code, Codex, Gemini CLI, and OpenAI SDKs. Follow each tool’s configuration instructions, keep keys in environment variables, and start with a small test budget. Compare reported token usage with the actual balance deduction.

5. Choose on measured spend

Before committing, check rate limits, latency, error rates, and any additional charges. Keep referral rebates separate from your baseline comparison; 59API offers a referral rebate, but its value depends on the current terms.

If you want a low-cost comparison route, consider signing up for 59API and replaying a small, non-sensitive test set. Choose the option that delivers reliable, accepted results at the lowest measured cost—not simply the lowest advertised input rate.

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