llm_cost_by_usecase
What does an LLM actually cost for your workload?
Token prices alone do not tell the story. A semantic search service at 10K requests/day looks nothing like a research agent at 150. These five real workload profiles are priced across Claude, GPT, and Gemini, every figure computed from the same model as the AI Agent Cost Calculator so they never drift as prices change.
Semantic Search with RAG
Real-time search across documentation or knowledge base. User query → embedding lookup → context retrieval → LLM ranking & formatting.
cost_across_models
| Model | Per request | Per month | vs cheapest |
|---|---|---|---|
| Claude Haiku Anthropic | $0.0091 | $2,719 | 11.2× |
| Claude Sonnet Anthropic | $0.03 | $10,177 | 41.8× |
| Claude Opus Anthropic | $0.17 | $50,857 | 208.9× |
| GPT-4o mini OpenAI | $0.0016 | $480 | 2.0× |
| GPT-4o OpenAI | $0.03 | $7,882 | 32.4× |
| Gemini 1.5 Flash Google (Vertex) | $0.0008 | $243 | Cheapest |
| Gemini 1.5 Pro Google (Vertex) | $0.01 | $3,945 | 16.2× |
Total input/request: 7.3k (base + agentic context re-send)
Total output/request: 800
LLM turns per request: 2
Customer Support Agent
Multi-turn support conversation. Agent retrieves ticket history + KB → responds → optionally calls tools (create ticket, escalate, fetch order).
cost_across_models
| Model | Per request | Per month | vs cheapest |
|---|---|---|---|
| Claude Haiku Anthropic | $0.02 | $1,342 | 11.4× |
| Claude Sonnet Anthropic | $0.08 | $5,025 | 42.7× |
| Claude Opus Anthropic | $0.42 | $25,113 | 213.3× |
| GPT-4o mini OpenAI | $0.0039 | $233 | 2.0× |
| GPT-4o OpenAI | $0.06 | $3,828 | 32.5× |
| Gemini 1.5 Flash Google (Vertex) | $0.0020 | $118 | Cheapest |
| Gemini 1.5 Pro Google (Vertex) | $0.03 | $1,916 | 16.3× |
Total input/request: 15.9k (base + agentic context re-send)
Total output/request: 2.4k
LLM turns per request: 3
Code Generation & Refactoring
IDE copilot or PR review agent. Given code context → suggest implementation or refactor → run linter checks → iterate.
cost_across_models
| Model | Per request | Per month | vs cheapest |
|---|---|---|---|
| Claude Haiku Anthropic | $0.04 | $4,217 | 11.8× |
| Claude Sonnet Anthropic | $0.15 | $15,813 | 44.2× |
| Claude Opus Anthropic | $0.75 | $79,065 | 221.1× |
| GPT-4o mini OpenAI | $0.0068 | $715 | 2.0× |
| GPT-4o OpenAI | $0.11 | $11,918 | 33.3× |
| Gemini 1.5 Flash Google (Vertex) | $0.0034 | $358 | Cheapest |
| Gemini 1.5 Pro Google (Vertex) | $0.06 | $5,959 | 16.7× |
Total input/request: 26.2k (base + agentic context re-send)
Total output/request: 4.8k
LLM turns per request: 4
Research & Summarization Agent
Multi-step research loop: query web API → fetch pages → extract key facts → synthesize → cite sources.
cost_across_models
| Model | Per request | Per month | vs cheapest |
|---|---|---|---|
| Claude Haiku Anthropic | $0.14 | $678 | 6.7× |
| Claude Sonnet Anthropic | $0.53 | $2,415 | 23.9× |
| Claude Opus Anthropic | $2.63 | $11,892 | 117.9× |
| GPT-4o mini OpenAI | $0.02 | $156 | 1.5× |
| GPT-4o OpenAI | $0.41 | $1,885 | 18.7× |
| Gemini 1.5 Flash Google (Vertex) | $0.01 | $101 | Cheapest |
| Gemini 1.5 Pro Google (Vertex) | $0.20 | $965 | 9.6× |
Total input/request: 115.5k (base + agentic context re-send)
Total output/request: 12.0k
LLM turns per request: 6
Content Moderation at Scale
High-volume screening: classify text + flags + confidence + optional manual escalation. Runs low-cost model with spot checks by premium model.
cost_across_models
| Model | Per request | Per month | vs cheapest |
|---|---|---|---|
| Claude Haiku Anthropic | $0.0006 | $1,005 | 8.1× |
| Claude Sonnet Anthropic | $0.0024 | $3,645 | 29.5× |
| Claude Opus Anthropic | $0.01 | $18,045 | 145.8× |
| GPT-4o mini OpenAI | $0.0001 | $203 | 1.6× |
| GPT-4o OpenAI | $0.0017 | $2,670 | 21.6× |
| Gemini 1.5 Flash Google (Vertex) | $0.0001 | $124 | Cheapest |
| Gemini 1.5 Pro Google (Vertex) | $0.0009 | $1,358 | 11.0× |
Total input/request: 300 (base + agentic context re-send)
Total output/request: 100
LLM turns per request: 1
model_selection
When to reach for cheap vs premium
Use cheap models when
- High volume (10K+ req/day) on a simple task: classification, ranking
- Fallback or batch processing that tolerates 24h latency
- The user never sees the model choice (backend only)
Use premium models when
- Complex reasoning is needed: research, code review, multi-step agents
- It is user-facing and accuracy beats cost: support, search
- Tool-calling reliability is critical (agent success equals revenue)
cost_trends
What drives the bill
Semantic search + RAG. Embedding cost ($0.02 per 1M tokens) is often 10–20% of total. High-volume search favors cheap models because LLM cost dominates.
Agentic loops (support, research). Tool calls multiply input tokens. A 5-tool agent can cost 3–5× as much as single-turn. Budget accordingly.
Code generation. Large context (entire files) plus premium models (accuracy matters) gets expensive fast. Batch or offline processing is cheaper than IDE real-time.
Moderation at scale. 50K req/day on cheap models is feasible. Premium spot-checks at a 5% escalation rate add ~$50/mo but catch the edge cases.
next_steps
Customize your numbers: the AI Agent Cost Calculator lets you tune token counts, request volume, and tool calls for your exact workload.
Understand the concepts: read the tokens glossary term or explore model comparisons.
Want this priced against your real traffic?
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