Abacus AI & LLM Credit Consumption Calculator v1.0.0

Illustrative token-volume and cost scenarios with explicit assumptions

Presets:
Query & Architecture Inputs
Authored base rate with a user-selected illustrative markup
150
Total user or system invocations per day
1,200
System prompt + direct user instructions
4,000
Retrieved context chunks injected per query turn
2.5
Multi-hop turns per query (amplifies context history)
800
Average completion length generated by LLM
15%
Orchestration overhead markup (1.15x = 15%)
Consumption & Forecast Summary
Monthly USD Cost $32.40 $1.08 / day
Monthly Credits 3,240 Assumed conversion: 100 credits = $1.00
Daily Token Volume 1,080,000 780k In / 300k Out
Effective Single-Query Token Amplification Map 7,200 tokens
Base Prompt
RAG Context
Agent Hop Amplification
Output Completion
💡

Cost Optimization Recommendation

Compare modeled rates in the matrix; actual usage and task quality must be checked separately.

Multi-Model Credit Burn Comparison Matrix
Model Input / Output Rate ($/M) Monthly Cost (USD) Monthly Credits Cost Index
Status: Computed | Monthly USD: $32.40 | Monthly Credits: 3,240 | Daily Tokens: 1080000

LLM Token Amplification and Credit Burn Dynamics

Read the explanation

In multi-turn agent workflows, every single query compounds base instructions and injected retrieval documents across iterative reasoning hops. When agent loops execute multiple hops, total input tokens multiply by the hop count, while completion tokens accumulate with each round. The tool applies the selected model's pricing matrix and routing markup, converting total daily token consumption into a monthly credit projection.

Read token amplification and cost assumptions together

How do the hop multiplier and output tokens contribute to the monthly estimate?

Change input sizes, average hop count, query volume and model to compare an authored cost scenario. The calculator does not run queries or read an account. Its embedded prices, routing markup, 30-day month and credit-to-dollar conversion are assumptions, not verified current provider charges.

Hops multiply both input and output linearly; the formula does not simulate growing conversation history, cache hits or individual tool calls. Fractional hops are an average assumption. The matrix uses the same workload for every model without testing quality or compatibility. Estimated savings are scenarios, not verified outcomes.

Try a worked example

Deep Research uses (1,200 + 4,000) × 2.5 = 13,000 input tokens and 800 × 2.5 = 2,000 output tokens per query. At 150 queries/day that is 2,250,000 daily tokens. With the authored GPT-4o rates and 1.15 multiplier, the 30-day estimate is $271.69 and 27,169 credits. Select Casual Search: 400 input and 300 output tokens at one hop and 50 daily queries produce 35,000 daily tokens, $6.90/month and 690 credits.

Reconcile the diagram and exported breakdown

The token diagram now counts output across every assumed hop, so its query total matches the daily calculation. In JSON, base_model_cost includes base-prompt and completion costs; rag_context_overhead contains retrieved-context input cost. The linear input model adds no separate hop surcharge, so agent_hop_overhead is zero instead of a placeholder fee. Rounded components can differ from the rounded total by a cent.

Compare with actual provider accounting

Abacus.AI’s billing documentation distinguishes credits from tokens and explains where to inspect actual usage. It notes that service rates change. Check recorded usage and applicable billing terms for the operation you actually perform; the selectable markup and 100-credits-per-dollar conversion do not establish those terms. Abacus.AI: billing and actual credit usage

Keep the scenario with its export

JSON preserves selected inputs and calculated results, including per-query and daily token totals. CSV is a smaller summary. Refocus View scrolls to the results without resetting inputs. A preset replaces workload inputs, including the model and markup, so export before switching if you want to retain a comparison.

Sources and further reading
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