TL;DR

For a realistic solo trading research loop — 10 ideas/day, 5 calls per idea, 8K input + 1.5K output tokens, 15% retry rate — April 2026 monthly cost looks like: Claude Haiku 4.5 ≈ $19/mo, Claude Sonnet 4.6 (no cache) ≈ $180/mo, Claude Sonnet 4.6 (50% cache) ≈ $125/mo, GPT-5 ≈ $440/mo, Claude Opus 4.7 ≈ $870/mo. The real metric is cost per validated trade (per idea ÷ validation rate), not cost per call. Sonnet with prompt caching is the price/performance sweet spot for almost every retail loop in 2026. Verify your own numbers at /tools/token-cost-optimizer/.

The honest question

Retail AI traders constantly underestimate the cost of the research layer. The common failure mode: pick the frontier model ("I want the best reasoning"), run it at full rate, wake up to a $900 invoice, then cut the loop entirely because it "didn't pay for itself."

It didn't pay for itself because it was priced at Opus rates for a task Sonnet does 95% as well. The fix isn't the cheapest model; it's a calibrated model mix where each step uses the cheapest model that passes its own quality bar.

2026 pricing baseline (April)

Per 1M tokens, USD:

Model Input Output Cache read (Anthropic)
Claude Opus 4.7 $15 $75 $1.50
Claude Sonnet 4.6 $3 $15 $0.30
Claude Haiku 4.5 $1 $5 $0.10
GPT-5 $10 $40 —
GPT-5 mini $2 $8 —
o4-mini $3 $12 —
Gemini 2.5 Pro $1.25 $10 —
Gemini 2.5 Flash $0.30 $2.50 —

Sources inline at the end.

The formula

effective_call   = input × price_in + output × price_out
                   (Anthropic: cache-hit fraction at cache_read price)
calls_per_idea   = calls × (1 + retry_rate)
cost_per_idea    = effective_call × calls_per_idea
cost_per_day     = cost_per_idea × ideas_per_day
cost_per_val     = cost_per_idea / validation_rate
cost_per_year    = cost_per_day × 365

validation_rate is the share of ideas that become a trade you'd actually take. This is the single most important number in the formula and the one retail setups most often underestimate. If your research loop produces 10 ideas but only 2 pass your risk gates, your effective cost per trade is 5× the per-idea cost.

Reference workloads

Small operator (solo trader)

  • 10 ideas/day · 5 calls/idea · 8K input / 1.5K output · 15% retry · 30% validation
Model Per call Per month Per validated trade
Haiku 4.5 $0.016 $19 $0.30
Sonnet 4.6 (no cache) $0.047 $180 $2.71
Sonnet 4.6 (50% cache) $0.029 $125 $1.92
Opus 4.7 $0.233 $870 $12.94
GPT-5 $0.118 $440 $6.56
Gemini 2.5 Flash $0.006 $23 $0.35
Gemini 2.5 Pro $0.024 $91 $1.32

Mid-scale (team of 1–3)

  • 50 ideas/day · 5 calls/idea · 12K input / 2K output · 20% retry · 25% validation
Model Per month Per validated trade
Haiku 4.5 $108 $0.89
Sonnet 4.6 (50% cache) $703 $5.93
Opus 4.7 $5,000 $43.55
GPT-5 $2,280 $19.38
Gemini 2.5 Pro $517 $4.17

The pattern

Three clusters:

  1. Cheap tier ($0–$25/mo for small operators): Haiku 4.5 + Gemini 2.5 Flash. Adequate for filtering, light extraction, boilerplate research. Not good enough for a synthesis pass.
  2. Mid tier ($100–$300/mo): Sonnet 4.6 + Gemini 2.5 Pro. Price/performance sweet spot. Sonnet with prompt caching at 50% hit rate is nearly identical cost to Gemini 2.5 Pro.
  3. Frontier tier ($500+/mo): Opus 4.7 + GPT-5. Worth it only for the 5–10% of prompts that actually need frontier reasoning.

Anthropic prompt caching math

If your research loop sends the same ~6K-token context (system prompt + persistent memory + tool descriptions) to Claude on every call, prompt caching is the biggest lever. Sonnet 4.6 input at $3/MT drops to $0.30/MT for cached tokens — a 10× reduction.

At 50% cache hit rate on a 6K-token fixed context + 2K-token variable context:

cost_per_input = (2000 × $3 + 6000 × $0.30) / 1,000,000
              = ($0.006 + $0.0018) per call
              = $0.0078 per call

vs the no-cache baseline of $0.024 per call. That's a ~67% reduction on input cost. Output cost is unaffected (no caching).

How to actually get high cache hit rates: keep everything above the dynamic user prompt stable across calls. Pin system prompt, pin memory, pin tool definitions. Any variable text at the top of the input breaks caching for everything below it. Most retail setups break caching by accident by inserting a timestamp or a ticker name at the top.

The model-mix pattern that works

Staged cascade:

  1. Ingestion filter (Haiku 4.5 or Gemini Flash): is this news/filing/event worth analyzing? Returns boolean + one-line reason. Budget: 100 tokens output. Cost: ~$0.0001/call.
  2. Structured extraction (Sonnet 4.6 with caching): extract specific fields from the document. Budget: 500 tokens output. Cost: ~$0.015/call.
  3. Synthesis (Sonnet 4.6 or Opus 4.7): given the extracted facts, produce a thesis + calibrated probability. Budget: 1500 tokens output. Cost: ~$0.05–$0.20/call.
  4. Risk layer (deterministic code, no LLM): sizing + execution.

Under this pattern, 80% of calls terminate at step 1 (boolean filter). Only the ~20% that pass reach step 2. Only a subset of those reach step 3. End-to-end cost per validated idea drops by roughly 5–10× vs running Sonnet on everything.

Retry economics

retry_rate in the cost formula is the fraction of calls that need redoing (transient errors, model refusals, output parse failures). For a well-instrumented setup this is 5–10%. For a rushed setup with no retry logic you might skip it entirely but eat more failed ideas.

The ugly corner case: structured-output retries that add a larger "please return valid JSON this time" prefix. Those retries are more expensive than the original call and are often invisible in raw cost data. Audit your retry path.

What the token-cost optimizer lets you do

/tools/token-cost-optimizer/ takes your exact inputs (token counts, calls, retry, ideas/day, validation) and shows the per-call, per-idea, per-validated-trade, per-month, and per-year cost across all 8 tracked models in one table. Use it before you subscribe to a frontier tier.

The practical test

The Prompt Regression Tester lets you run the same prompt against Haiku, Sonnet, Opus, GPT-5, and Gemini 2.5 Pro side-by-side. For most research prompts you'll find:

  • Haiku's output is ~80% as good as Sonnet's
  • Sonnet's output is ~95% as good as Opus's
  • Opus is meaningfully better only on prompts requiring multi-step reasoning or long-context synthesis

If Sonnet is 95% as good at 1/5 the cost, use Sonnet. Keep Opus for the 5% of prompts that actually need it.

Sources