LLM API Pricing Comparison 2026
Token prices vary by an order of magnitude across providers and models. For autonomous agents, the right comparison isn't just the rate — it's the rate × what agents actually do.
What to compare
Input vs output rates (output is typically 3–5× input).
Cache-hit discounts — big for agents with stable system prompts.
Free tiers and their limits.
Volume pricing tiers.
What the rates mean for agents
A retry loop multiplies volume on whatever rate you chose.
Long-context agent runs make input rate the dominant cost.
Model selection is the single biggest lever — then volume.
How sipi.bot fits
sipi.bot doesn't pick your model — it governs the spend: per-agent caps, category rules, velocity limits, and an approval queue. The pricing page is the map; the firewall is the gate.
The data at a glance
| Provider | Pricing model | What to watch |
|---|---|---|
| OpenAI | Per-token by model | Frontier model rates; overage |
| Anthropic | Per-token by model | Output-heavy agent runs |
| Google Gemini | Per-token by generation + free tier | Model drift across tiers |
| DeepSeek | Low per-token + cache discounts | Volume at low rates |
| AWS Bedrock | Per-token + provisioned throughput | Model mix and volume |
FAQ
Which provider is cheapest?
Rates change frequently. Compare current per-token prices for the exact models you use — and remember total cost is rate × volume.
What's the biggest agent cost driver?
Volume, not rate. Retry loops and fan-out multiply tokens on whatever model you chose.
Where does sipi.bot fit in pricing decisions?
It enforces the budget around any provider — caps, allowlists, velocity limits, and approvals. The pricing page informs; the firewall enforces.
Related
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