LLM Cost Engine
v1.0 Prices verified 2026-02-24Know your monthly LLM cost before you commit. Real TCO based on volume, cache strategy, and token mix โ not just $/token.
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+ More scenarios (Dev Tools, Content Generation)
Real-world monthly cost scenarios
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LLM-2026-7B2CMeta
Monthly Cost
$3.30
ValueScore: 0.7737
Why Switch to Llama 4 Maverick?
Switching to Llama 4 Maverick saves you 69.4% monthly compared to DeepSeek R1.
Save $7.50 per month
Minimal Savings
Migration effort may exceed $7.50/mo savings. Consider if quality/latency differences justify the switch.
Get notified weekly if pricing changes or a competitor undercuts by 15%+
Smart Routing Simulator
NewRoute simple queries to a cheaper model, complex ones to a smarter model. See your blended cost.
Example: 80% FAQ queries โ Gemini Flash ($0.50/M), 20% debugging โ Claude Sonnet ($3/M) = 68% savings
Llama 4 Maverick
Meta
Monthly Cost
$3.30
Daily Breakdown
- Input (non-cached)
- $0.01
- Input (cached)
- $0.00
- Output
- $0.09
- Daily Total
- $0.11
This model is the #1 Best Value choice for your volume.
DeepSeek R1
DeepSeek
Monthly Cost
$10.80
Daily Breakdown
- Input (non-cached)
- $0.03
- Input (cached)
- $0.00
- Output
- $0.33
- Daily Total
- $0.36
Operating this model costs $7.50 more/mo than Llama 4 Maverick. Ensure the Latency/Context justifies the premium.
Gemini 3.1 Pro
Monthly Cost
$57.60
Daily Breakdown
- Input (non-cached)
- $0.12
- Input (cached)
- $0.00
- Output
- $1.80
- Daily Total
- $1.92
Operating this model costs $54.30 more/mo than Llama 4 Maverick. Ensure the Latency/Context justifies the premium.
GPT-5.2
OpenAI
Monthly Cost
$66.30
Daily Breakdown
- Input (non-cached)
- $0.11
- Input (cached)
- $0.00
- Output
- $2.10
- Daily Total
- $2.21
Operating this model costs $63.00 more/mo than Llama 4 Maverick. Ensure the Latency/Context justifies the premium.
Claude Opus 4.6
Anthropic
Monthly Cost
$121.80
Daily Breakdown
- Input (non-cached)
- $0.30
- Input (cached)
- $0.01
- Output
- $3.75
- Daily Total
- $4.06
Operating this model costs $118.50 more/mo than Llama 4 Maverick. Ensure the Latency/Context justifies the premium.
Prompt Caching ROI
Cache static prompts. Break-even analysis included.
Context Window Comparator
Check which models fit your document size.
Batch API Calculator
Trade 24h turnaround for 50% savings.
Export Signed LLM Cost Analysis
Includes TCO Breakdown, Token Forecast & Vendor Comparison ready for CTO/CFO Approval.
# tco_forecast --model="Llama 4 Maverick"
| Traffic | Monthly | Annual |
|---|---|---|
| 1x(current) | $3.30 | $40 |
| 2x(doubled) | $6.30 | $76 |
| 3x(tripled) | $9.60 | $115 |
Engineering Snapshot:
- โ Workload assumptions
- โ Monthly + Annual costs
- โ Cost variance (Nx)
Executive Report:
- โ Executive Summary + Annual TCO
- โ Cost Variance Analysis (Nx)
- โ Sensitivity at 2ร / 3ร volume
- โ Compliance & Neutrality statement
Our scoring model balances cost efficiency (65%) with contextual capacity (35%), adjusted by real-world latency benchmarks.
Methodology: ValueScore Algorithm
LLM Cost Engine uses a deterministic ValueScore algorithm to rank LLM providers. This metric balances operational cost with model capability for objective vendor comparison.
The formula rewards efficiency without sacrificing enterprise requirements:
- Cost Efficiency (ฮฑ = 0.65): Cost savings weighted at 65% โ the primary driver for ROI decisions. Lower costs significantly boost the score.
- Context Capacity (ฮฒ = 0.35): Logarithmic scale for Context Window (35% weight). Larger windows are valuable but show diminishing returns at scale.
- Latency Index: A linear multiplier (0-1) penalizing slower models. High latency impacts user experience regardless of cost savings.
This deterministic approach ensures reproducible, auditable results for procurement and compliance requirements.
Frequently Asked Questions
- Why do input and output tokens have different prices?
- LLM providers charge more for "generation" (output) than for "reading" (input). Output requires more computational resources as the model predicts each token sequentially. LLM Cost Engine separates these costs for accurate TCO projections.
- How does Prompt Caching affect TCO?
Prompt Caching stores static prompt components (system instructions, knowledge bases) for reuse at significant discounts (50-90% cheaper).
- Without Cache: Full price for system prompts on every API call.
- With Cache: One-time write cost, then discounted read costs on subsequent calls.
Default cache hit rate is 20%, adjustable based on your deployment architecture.
- Why is Context Window factored into ValueScore?
- For enterprise LLM deployments, context capacity is critical for maintaining conversation state, processing documents, and avoiding context overflow errors. We apply logarithmic scaling to acknowledge diminishing returns while ensuring we don't recommend models that fail at production workloads.
- Can I use the TCO Analysis for procurement?
- Yes. The exported PDF includes a signed analysis with deterministic methodology, sensitivity projections, and vendor comparison matrix โ formatted for CTO/CFO review and procurement documentation.