Free LLM Cost Tools: Token Counter, Budget Simulator and a Pre-Spend Workflow

Short answer: AICredits publishes a set of free browser tools at aicredits.in/tools, and the useful five for budgeting are the token counter, context window checker, model finder, budget simulator and the INR cost calculator. Use them in that order to estimate a project's monthly rupee cost before you top up, then add margins and cap the API key.

Disclosure: the aicredits.in links are affiliate links and I use the service. This is a walkthrough of what the tools page and source describe as of 16 Oct 2026. The example project and its prices are invented to show the arithmetic, and the Python snippet is mine and has been run only on those invented numbers.

The expensive mistake with LLM APIs is not a high price per token. It is starting a feature without knowing whether it costs ₹800 or ₹80,000 a month. The tools are free, so the real cost of estimating is a few minutes. Here is what each one does, what to watch out for, and a workflow that ends with a number you can defend.

Which free tools exist?

I checked each against the tools directory in the AICredits site code. The ones that exist as of 16 Oct 2026:

You can open them all from aicredits.in/tools. The ones that touch your money are the first five, and that is where this post stays.

What does each budgeting tool really do?

Token counter. In the source, it imports a GPT-style tokenizer for exact-ish counts and falls back to a rough estimate (about 3.8 characters per token chunk) if the import fails. It also shows words and characters, a context usage bar per model and an estimated input cost. Two limits to remember. A GPT tokenizer is not the tokenizer Claude or Gemini use, so counts for those models are approximate. And the rupee figure in the code I read multiplies the USD cost by a fixed rate with no buffer or fee, so scale it by 1.1025 yourself to match the AICredits formula (live rate times 1.05 times 1.05).

Context window checker. You paste a document and it draws a bar per model. Its model list is a hard-coded set, and in the code I read it included several older models. Use it to see roughly whether a 60-page contract can fit in one call, then confirm the real limit of the model you pick on the models page.

Model finder. Filters the live catalog by provider, vision, context and price, and shows INR per million tokens at AICredits rates. This is the tool to use for a shortlist. The claim "300+ models" in AICredits' own docs means the shortlist step matters more than the sort order.

Budget simulator. Choose a model, set requests per day per person, team size, average prompt tokens and average completion tokens. It reports cost per request, then daily, monthly (days times 30) and annual figures. It states that the INR cost includes the 5% forex buffer and 5% platform fee. Two caveats from the source: the model list is static, and the exchange rate is a constant inside the tool (87 in the code I read), not the live rate. For this reason a real bill may sit a few percent away from what it shows.

Cost calculator. Unlike the simulator, /calculator loads models from the live catalog (the page is revalidated hourly), so prices should track the catalog. Inputs are prompt tokens, completion tokens and requests per day. It leaves out retries, caching discounts and your top-up charge.

What is a good pre-spend workflow?

Six steps. It takes about 20 minutes for a small feature.

1. Measure a real prompt, not a guess. Paste your actual system prompt plus a typical user message into the token counter. Do this for the longest realistic case too. Write down prompt tokens and a guess at completion tokens (run one real call if you can and read the usage field).
2. Check it fits. Put your largest document through the context window checker. If you plan to stuff retrieved text into the prompt, read token counting and context management first, since the system prompt, history and reply all share the window.
3. Shortlist two or three models. Use the model finder with your context and vision needs. Pick one cheap, one mid, one strong, so you see the spread. The catalog ranking script automates the same filter.
4. Run the numbers twice. Use the budget simulator and the calculator and compare. If they disagree by more than a few percent, the cause is usually the static list or fixed rate in the simulator.
5. Add margins. Retries, longer-than-average inputs, growth and the top-up charge all push the real number up. The function below applies them.
6. Cap the key. Create the API key with an INR budget slightly above your planned monthly number, so a bug cannot spend ten times your plan.

Worked example with invented numbers

A support-reply drafting feature. Invented inputs: 300 requests a day, a 1,200-token prompt, a 250-token reply, a model at an illustrative $0.50 input and $2.00 output per million tokens. I used two exchange rates, 87 (the simulator's constant) and 90 (a live-rate stand-in). The safety multiplier 1.3 and the 5% retry rate are my assumptions, not AICredits figures.

FOREX_BUFFER = 1.05
PLATFORM_FEE = 1.05
TOPUP_LOSS = 0.0236   # about 2.36% lost on each top-up


def per_request_inr(prompt_tokens, completion_tokens, usd_in_per_m, usd_out_per_m, usd_inr):
    usd = prompt_tokens / 1e6 * usd_in_per_m + completion_tokens / 1e6 * usd_out_per_m
    return usd * usd_inr * FOREX_BUFFER * PLATFORM_FEE


def monthly_plan(requests_per_day, prompt_tokens, completion_tokens,
                 usd_in_per_m, usd_out_per_m, usd_inr,
                 days=30, retry_rate=0.05, safety=1.3):
    base = per_request_inr(prompt_tokens, completion_tokens, usd_in_per_m, usd_out_per_m, usd_inr)
    monthly = base * requests_per_day * days
    with_retries = monthly * (1 + retry_rate)
    planned = with_retries * safety
    topup = planned / (1 - TOPUP_LOSS)
    return {
        "per_request_inr": round(base, 4),
        "monthly_inr": round(monthly, 2),
        "with_retries_inr": round(with_retries, 2),
        "planned_inr": round(planned, 2),
        "topup_to_pay_inr": round(topup, 2),
        "key_budget_inr": round(planned, -1),
    }


for rate in (87.0, 90.0):
    print(rate, monthly_plan(300, 1200, 250, 0.50, 2.00, rate))

Output from my run:

87.0 {'per_request_inr': 0.1055, 'monthly_inr': 949.58, 'with_retries_inr': 997.06, 'planned_inr': 1296.18, 'topup_to_pay_inr': 1327.51, 'key_budget_inr': 1300.0}
90.0 {'per_request_inr': 0.1091, 'monthly_inr': 982.33, 'with_retries_inr': 1031.44, 'planned_inr': 1340.88, 'topup_to_pay_inr': 1373.29, 'key_budget_inr': 1340.0}

Read it like this. The raw model cost is about ₹950 to ₹980 a month depending on the rate. After retries and a 1.3 safety factor the plan is about ₹1,300 to ₹1,340, a top-up of about ₹1,330 to ₹1,370 covers it, and the key cap goes near ₹1,300. The difference between the two rates is under 4%, which is the margin by which the simulator can be off. The point of the exercise is the order of magnitude: hundreds of rupees, not tens of thousands, so the feature is safe to build.

If your number comes out ten times your tolerance, change something before building: a cheaper model, a shorter prompt, or a smaller reply cap. Cost per 1,000 requests, worked examples shows how those levers move the total.

What do you do after the estimate?

An estimate is a hypothesis. Test it:

• Top up a small amount, minimum ₹50, and send the first 100 real requests.
• Read the average cost per request from your usage log and compare it with per_request_inr.
• If it is off by more than 20%, your token guesses were wrong. Re-measure and rerun.
• Log cost per feature from then on. The cost-per-feature script writes each call's INR cost to a file you can chart.

Credits last a year and there are no free credits, so avoid a large first top-up until the estimate has survived real traffic.

Where do these tools fall short?

• They give you list-price cost, not performance. A cheaper model that needs two attempts may cost more. Pair the cost work with a small evaluation set.
• Caching, batch tiers and reasoning tokens are not in the simple inputs. Reasoning models can bill thinking tokens as output, which can multiply cost on hard prompts.
• Static model lists age. Prefer the calculator and model finder, which read the live catalog, for final numbers.
• Tool pages change. I read the source on 16 Oct 2026; if a tool behaves differently, believe the tool.

Next step

Open the token counter, paste your real prompt, and run the six steps. When the number looks acceptable, create a capped key at AICredits and test the first hundred requests. For the wider picture, start at the AICredits review. If you use workflow tools, the n8n, Dify and Open WebUI setup guide shows where to put the key.