Convertible Note Calculator
Calculate conversion price, discount, and cap for convertible note financing rounds. Enter values for instant results with step-by-step formulas.
Calculator
Adjust values & calculateSeries A Round Terms
Conversion Price Comparison
Formula
The convertible note converts at the lower of two prices: the cap-based price (valuation cap divided by shares outstanding) or the discount-based price (round price reduced by the discount percentage). The total amount converting includes both the original investment and any accrued interest.
Last reviewed: December 2025
Worked Examples
Example 1: Standard Convertible Note Conversion
Example 2: Discount-Driven Conversion
Background & Theory
The Convertible Note Calculator applies the following established principles and formulas. Large language models process text by breaking it into tokens, sub-word units produced by algorithms such as byte-pair encoding. In English, one token approximates four characters or three-quarters of a word on average, though this ratio varies considerably across languages and code. A 1000-word document typically requires around 1300 to 1500 tokens. Token count drives both context window constraints and inference billing, making accurate estimation essential for budgeting API usage. The capability of a neural network scales primarily with its parameter count. Parameters are the numerical weights adjusted during training via gradient descent. GPT-3 contains 175 billion parameters; larger models in the trillion-parameter range require correspondingly greater compute and memory. Training compute is measured in floating-point operations (FLOPs): the Chinchilla scaling laws derived by Hoffmann et al. in 2022 show that optimal training allocates roughly 20 tokens per parameter, meaning a 70B-parameter model benefits from approximately 1.4 trillion training tokens. Inference latency depends on model size, hardware, and batching strategy. Running a 7B-parameter model in FP16 precision requires roughly 14 GB of GPU VRAM (2 bytes per parameter), while INT8 quantisation halves this to around 7 GB with modest quality loss, and INT4 reduces it to approximately 3.5 GB. This quantisation trade-off between memory, speed, and accuracy is central to deploying models on consumer hardware. Perplexity measures how surprised a language model is by a given text corpus; lower perplexity indicates better predictive accuracy. Embedding dimensions determine the size of the dense vector representations used to encode semantic meaning. Models like OpenAI's text-embedding-ada-002 produce 1536-dimensional vectors, while compact models may use 384 dimensions. Context window size defines the maximum token span a model can attend to in a single forward pass. Extending context windows from 4K to 128K tokens enables document-scale reasoning but substantially increases memory requirements, as the attention mechanism scales quadratically with sequence length without architectural modifications such as flash attention.
History
The history behind the Convertible Note Calculator traces back through the following developments. The mathematical neuron model published by Warren McCulloch and Walter Pitts in 1943 first proposed that logical functions could be computed by networks of simple threshold units, planting the seed of neural computation. Frank Rosenblatt's Perceptron, introduced in 1957 and implemented in custom hardware by 1960, could learn linear classifiers from examples and generated enormous public excitement before Marvin Minsky and Seymour Papert's 1969 book rigorously analysed its fundamental limitations, demonstrating it could not learn the simple XOR function. The first AI winter, roughly 1974 to 1980, followed as funding agencies in the US and UK grew disillusioned with unrealised promises. A second wave of interest during the 1980s produced rule-based expert systems deployed in medicine and finance, and saw the re-derivation of backpropagation by Rumelhart, Hinton, and Williams in 1986, making it practical to train multi-layer networks on real problems. A second winter from 1987 to 1993 followed as expert systems proved brittle and hardware remained insufficient for genuine deep learning. The deep learning revival crystallised at the ImageNet Large Scale Visual Recognition Challenge in 2012, when Alex Krizhevsky's convolutional network AlexNet slashed the top-5 error rate by nearly 11 percentage points compared to the prior year's winner. This demonstrated that deep networks trained on GPUs with large labelled datasets could achieve human-competitive image recognition. Subsequent years saw rapid advances in recurrent networks, sequence-to-sequence models, and the attention mechanism, culminating in the transformer architecture introduced by Vaswani et al. in 2017. OpenAI released GPT-1 in 2018, demonstrating that unsupervised pre-training on large text corpora followed by task-specific fine-tuning could transfer knowledge broadly across language tasks. GPT-2 in 2019 demonstrated surprisingly fluent long-form text generation. GPT-3 in 2020, with 175 billion parameters, showed that scale alone could unlock few-shot learning. Kaplan et al.'s 2020 scaling laws paper provided the theoretical grounding. ChatGPT launched in November 2022, reaching one million users within five days and igniting mainstream global awareness of large language models.
Frequently Asked Questions
Formula
Effective Price = min(Valuation Cap / Shares Outstanding, Round Price x (1 - Discount Rate))
The convertible note converts at the lower of two prices: the cap-based price (valuation cap divided by shares outstanding) or the discount-based price (round price reduced by the discount percentage). The total amount converting includes both the original investment and any accrued interest.
Worked Examples
Example 1: Standard Convertible Note Conversion
Problem: An investor puts $500K into a convertible note with 5% interest, 18-month term, $5M cap, and 20% discount. The Series A is at $10M pre-money with 10M shares outstanding.
Solution: Accrued Interest: $500,000 x 5% x 1.5 years = $37,500\nTotal Convertible: $500,000 + $37,500 = $537,500\nPrice at Round: $10,000,000 / 10,000,000 = $1.00/share\nCap Price: $5,000,000 / 10,000,000 = $0.50/share\nDiscount Price: $1.00 x (1 - 0.20) = $0.80/share\nEffective Price: min($0.50, $0.80) = $0.50 (cap wins)\nShares: $537,500 / $0.50 = 1,075,000 shares
Result: Converts at $0.50/share (cap) | 1,075,000 shares | 50% effective discount | 2x implied return
Example 2: Discount-Driven Conversion
Problem: A $250K note with 4% interest, 12-month term, $8M cap, and 25% discount. Series A at $8M pre-money, 10M shares outstanding.
Solution: Accrued Interest: $250,000 x 4% x 1.0 year = $10,000\nTotal Convertible: $250,000 + $10,000 = $260,000\nPrice at Round: $8,000,000 / 10,000,000 = $0.80/share\nCap Price: $8,000,000 / 10,000,000 = $0.80/share\nDiscount Price: $0.80 x (1 - 0.25) = $0.60/share\nEffective Price: min($0.80, $0.60) = $0.60 (discount wins)\nShares: $260,000 / $0.60 = 433,333 shares
Result: Converts at $0.60/share (discount) | 433,333 shares | 25% effective discount | 1.33x implied return
Frequently Asked Questions
What is a valuation cap on a convertible note?
A valuation cap sets the maximum company valuation at which a convertible note will convert into equity, protecting early investors from excessive dilution if the company's valuation increases dramatically before the conversion event. For example, if an investor holds a note with a $5 million cap and the company raises a Series A at a $20 million pre-money valuation, the note converts as if the valuation were only $5 million, giving the investor four times as many shares as they would receive at the actual round price. The cap effectively sets a ceiling on the conversion price. Without a cap, early investors risk having their notes convert at a very high valuation that provides minimal ownership despite their early risk-taking.
How does the discount rate work on a convertible note?
The discount rate gives convertible note holders a percentage reduction on the price per share paid by investors in the qualifying financing round. A typical discount ranges from 15 to 25 percent, with 20 percent being the most common. For example, if Series A investors pay $1.00 per share and the note has a 20 percent discount, the note holder converts at $0.80 per share, receiving 25 percent more shares per dollar invested. The discount compensates early investors for the additional risk they took by investing before the company had achieved the milestones that justified the Series A valuation. When both a cap and discount exist, the note converts at whichever method gives the investor the lower price per share.
How does interest accrue on a convertible note?
Convertible notes accrue interest just like any loan, typically at rates between 2 and 8 percent annually, with 5 percent being common. However, the interest is usually not paid in cash but instead adds to the principal amount that converts into equity at the conversion event. For example, a $500,000 note at 5 percent annual interest held for 18 months would accrue $37,500 in interest, making the total convertible amount $537,500. This additional amount converts into shares at the same discounted price as the principal. While the interest rate on convertible notes is relatively low compared to other debt instruments, it provides additional shares to the investor as compensation for the time value of their money.
What triggers the conversion of a convertible note?
The most common conversion trigger is a qualified financing event, which is typically defined as the company raising a minimum amount of new equity capital, usually $1 million or more. When this trigger occurs, the note automatically converts into the same class of preferred stock being sold in the round. Other potential triggers include maturity, where the note reaches its expiration date (typically 12 to 24 months) and must either be repaid, extended, or converted. A change of control event, such as an acquisition, usually triggers conversion or repayment at a multiple (typically 1x to 2x the investment). Some notes also include optional conversion rights that allow the investor to choose when to convert.
What happens when a convertible note matures before a funding round?
When a convertible note reaches its maturity date without a qualifying financing event having occurred, several outcomes are possible depending on the note terms and negotiation. The most common resolution is extending the maturity date, giving the company more time to raise a priced round, often with improved terms for the investor such as a lower cap or higher discount. The note could also convert at the valuation cap into common stock or a new class of preferred stock. Technically, the investor could demand repayment of the principal plus accrued interest, but this rarely happens because forced repayment could bankrupt a cash-strapped startup, destroying the investor's potential upside. Most sophisticated note agreements have pre-negotiated maturity conversion terms.
What is the difference between a convertible note and a SAFE?
A SAFE (Simple Agreement for Future Equity) and a convertible note are both instruments for early-stage financing, but they differ in several important ways. A convertible note is debt with an interest rate, maturity date, and repayment obligation. A SAFE is not debt, has no interest rate, no maturity date, and no repayment requirement. SAFEs are simpler and cheaper to execute, typically requiring no legal negotiation beyond filling in the economic terms. Convertible notes give investors slightly more protection because they can theoretically demand repayment at maturity. SAFEs are more founder-friendly because they have no maturity pressure. SAFEs were introduced by Y Combinator in 2013 and have largely replaced convertible notes for very early-stage rounds.
References
Reviewed by Daniel Agrici, Founder & Lead Developer · Editorial policy