Most AI agencies aren't running on a single LLM. One client wants OpenAI. Another is locked into Anthropic. A third wants Google's Gemini because their infra team already has a GCP contract. The result: your agency is managing API keys, invoices, and token costs across three different providers — each with its own pricing model, rate structure, and billing logic.
This creates a comparison problem. You can't evaluate whether you're overpaying on OpenAI without understanding what the equivalent request would cost on Claude. You can't set client pricing without knowing the real per-request cost across all the models you're actually using. And you can't manage margins when each provider is billing you separately, with no shared cost attribution layer.
This article compares LLM API pricing across OpenAI, Anthropic, and Google in 2026 — with real per-request math — and addresses the multi-provider attribution problem that makes agency billing harder than it should be.
OpenAI Pricing Breakdown
OpenAI's model lineup spans from cheap and fast (GPT-3.5 Turbo) to expensive and capable (GPT-4o, GPT-4 Turbo). Agencies typically use multiple tiers depending on the task: classification and quick responses on GPT-3.5, reasoning and complex generation on GPT-4o.
| Model | Input (per 1M tokens) | Output (per 1M tokens) |
|---|---|---|
| GPT-3.5 Turbo | $0.50 | $1.50 |
| GPT-4o Mini | $0.15 | $0.60 |
| GPT-4o Popular | $2.50 | $10.00 |
| GPT-4 Turbo | $10.00 | $30.00 |
| o1 (reasoning) | $15.00 | $60.00 |
GPT-4o Mini is the biggest pricing shift OpenAI has made in the last two years — 94% cheaper on input than GPT-4o, and capable enough to replace GPT-3.5 for most classification and summarization work. For agencies, the practical question is how much of your clients' workloads still justify GPT-4o when GPT-4o Mini handles the same task for a fraction of the cost.
For more on OpenAI-specific tracking, see: OpenAI API Cost Calculator — Per-Client Tracking for Agencies.
Anthropic Pricing Breakdown
Anthropic's Claude family offers a similar tiered structure, with Haiku as the budget option, Sonnet as the mid-tier workhorse, and Opus for the highest-capability tasks. The price gap between tiers is steep — Opus is 60x more expensive per input token than Claude 3 Haiku.
| Model | Input (per 1M tokens) | Output (per 1M tokens) |
|---|---|---|
| Claude 3 Haiku Cheapest | $0.25 | $1.25 |
| Claude 3.5 Haiku | $0.80 | $4.00 |
| Claude 3.5 Sonnet Popular | $3.00 | $15.00 |
| Claude 3 Opus | $15.00 | $75.00 |
| Claude 4 Opus | $15.00 | $75.00 |
Claude's output pricing is notably higher than OpenAI's at the Sonnet tier — $15/M output tokens vs. $10/M for GPT-4o. For workflows that generate long outputs (document drafts, detailed reports, code generation), that gap compounds quickly. On the other hand, Claude 3 Haiku undercuts GPT-4o Mini on input cost at $0.25/M vs. $0.15/M — close enough that other factors (response quality, latency, context window) may matter more than raw price.
For more on Claude-specific tracking, see: Claude API Cost Tracking — How Agencies Monitor Anthropic Usage Per Client.
Google Pricing: Gemini Pro and Ultra
Google's Gemini pricing has become genuinely competitive, especially on Gemini 1.5 Flash which sits among the cheapest capable models available. Agencies with GCP commitments often find Gemini the default choice for high-volume, lower-complexity tasks.
| Model | Input (per 1M tokens) | Output (per 1M tokens) |
|---|---|---|
| Gemini 1.5 Flash Cheapest | $0.075 | $0.30 |
| Gemini 1.5 Pro | $1.25 | $5.00 |
| Gemini 1.0 Ultra | $18.00 | $54.00 |
Gemini 1.5 Flash is the standout — at $0.075/M input tokens, it's the cheapest capable model in this comparison. If your workflow tolerates Gemini's quality output and you're running high call volume, Flash can cut token costs by 50–80% vs. comparable OpenAI and Anthropic tiers. The tradeoff is ecosystem maturity and integration reliability — OpenAI and Anthropic have more battle-tested tooling for most agency stacks.
Head-to-Head Cost Comparison
Numbers are only useful in context. Here's how the major models compare on a standardized request: a 1,000-token input prompt with a 500-token output response. This is a reasonable baseline for a typical API call — a user query with context plus a medium-length response.
| Model | Provider | Cost per 1M tokens (avg) | Cost per typical request |
|---|---|---|---|
| Gemini 1.5 Flash | $0.15 blended | $0.000225 | |
| Claude 3 Haiku | Anthropic | $0.58 blended | $0.000875 |
| GPT-4o Mini | OpenAI | $0.30 blended | $0.000450 |
| GPT-4o | OpenAI | $5.83 blended | $0.007500 |
| Claude 3.5 Sonnet | Anthropic | $7.50 blended | $0.010500 |
| GPT-4 Turbo | OpenAI | $16.67 blended | $0.025000 |
| Claude 4 Opus | Anthropic | $35.00 blended | $0.052500 |
The 233x gap: Gemini 1.5 Flash at $0.000225 per typical request vs. Claude 4 Opus at $0.052500 — the same API call costs 233x more at the top of the market than the bottom. At 50,000 calls/month, that's $11.25 vs. $2,625. Model selection isn't a technical preference — it's a margin decision.
The Hidden Cost: Per-Client Attribution Across Providers
The pricing comparison above is the easy part. The harder problem is that all three providers bill your organization as a whole — not per client. OpenAI issues one invoice. Anthropic issues one invoice. Google issues one invoice. None of them know or care about your downstream clients.
For an agency running five clients across three providers, this means:
- Three separate monthly invoices with no client breakdown
- No way to know, from the provider's bill alone, which client drove which costs
- No reconciliation between what you charged clients and what each provider actually billed you
- Margin bleed that surfaces only when you add up all three invoices and realize the total exceeds what you've collected
The provider-level billing gap gets worse as you add more LLMs. One provider is manageable with a spreadsheet. Three providers means triple the data entry, triple the invoice reconciliation, and triple the chance that a usage spike on one provider doesn't get attributed to the right client.
See the core problem framed in more detail: Why AI Agencies Are Losing Money Without Token Cost Tracking.
How TokenTally Solves Multi-Provider Tracking
TokenTally is built for exactly this scenario: an agency using multiple LLM providers, needing unified per-client cost attribution across all of them.
The architecture is straightforward. For each API call your application makes — whether to OpenAI, Anthropic, or Google — you log the usage to TokenTally via webhook or direct integration. TokenTally knows the model, the token counts, and the client the call belongs to. It applies the correct per-model pricing and calculates the cost immediately.
- Pre-loaded model pricing for all major providers — OpenAI (GPT-3.5, GPT-4o Mini, GPT-4o, GPT-4 Turbo, o1), Anthropic (Claude Haiku, Sonnet, Opus), and Google (Gemini Flash, Pro) rates are built in and editable when providers update their pricing
- Unified client dashboard — one view showing total AI spend per client, broken down by provider and model, for the current billing period
- Webhook integration — Zapier, Make.com, and n8n support for logging usage without custom code; direct API logging for teams that want tighter integration
- Per-client markup — your margin percentage applied automatically across all providers so client-facing costs are always marked up consistently
- Budget tracking — color-coded alerts (green <80%, yellow 80–99%, red ≥100%) across the combined multi-provider spend for each client
- Invoice and CSV export — itemized client billing with provider breakdown, ready to send without manual assembly
The goal is a single attribution layer that sits above all three provider invoices. Instead of reconciling three bills manually every month, you see the full picture — per client, per provider, with your markup applied — in one place.
For more on how client billing works in practice: How to Bill Clients for AI API Usage (Without Losing Money).
Which Provider Should Agencies Default To?
There's no single right answer — but there's a framework.
Default to GPT-4o Mini or Claude 3.5 Haiku for high-volume tasks. Both are capable enough for classification, summarization, quick Q&A, and routine generation at a fraction of the cost of their flagship models. If a client's workload is 80% high-volume low-complexity, this choice alone saves most agencies significant margin.
Use GPT-4o or Claude 3.5 Sonnet for complex reasoning and long-context tasks. They're in the same ballpark ($2.50/M vs. $3.00/M on input) with different output cost profiles — OpenAI cheaper on output, Anthropic stronger on long context. Let the workload requirements drive the choice, not habit.
Reserve Opus and o1-class models for tasks that genuinely need them. At $15/M input tokens and $60–75/M output, these are expensive enough that every call needs to justify the cost. Most agency workloads don't.
Track all of it the same way. Regardless of which provider you use for what, the attribution problem is identical — and needs the same solution. Once you've chosen your model mix, the multi-client agency setup guide walks through exactly how to implement per-client tracking across all three providers. And when you're ready to charge clients for those costs, billing clients for AI API usage covers how to apply markup and build the line item into invoices.
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