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Guide Velona Team ·29 October 2026 ·18 min read

What Does an AI Coding Agent Really Cost in India? Cline, OpenCode, Aider and Claude Code in ₹ (2026)

Short answer: an AI coding agent costs whatever its model, its cache hit rate and its hidden reasoning tokens add up to, and the spread is huge. On our worked example of a heavy month (1,549 agent steps), the same workload costs about ₹350 to ₹700 on DeepSeek V4.1 Flash, about ₹6,400 on Claude Sonnet 5.5 and about ₹18,000 on GPT-5.5, all with a good 90% cache hit rate. Break the cache and every one of those numbers roughly triples to quintuples.

The agent you pick matters less than most comparisons suggest. Cline, OpenCode, Aider and Claude Code are all loops that send your code to a model again and again. What you pay is set by four levers: the model's price, how much of each request is served from cache, how much fixed text the agent sends before your prompt, and how many reasoning tokens the model burns that you never see.

This guide works through all four with real 2026 prices, converted to rupees, so you can estimate your own bill before it arrives.

AI coding agent cost at a glance

All figures use the worked example explained below: each agent step sends 84,000 input tokens and gets 1,500 output tokens back. Prices are converted at ₹95.04 per US dollar (the rate on 27 October 2026) and exclude 18% GST.

ModelCost per step, no cachingCost per step, 90% cachedHeavy month (1,549 steps), 90% cachedLight month (214 steps), 90% cached
DeepSeek V4.1 Flash (off-peak)₹1.28₹0.23₹351₹49
DeepSeek V4.1 Flash (peak)₹2.57₹0.45₹703₹97
Claude Haiku 5.5₹0.87₹0.24₹376₹52
DeepSeek V4 Pro (peak)₹11.10₹1.93₹2,997₹414
Claude Sonnet 5.5₹17.39₹4.14₹6,413₹886
Claude Opus 5.5₹34.78₹8.28₹12,826₹1,772
GPT-5.5₹44.19₹11.86₹18,373₹2,538

The heavy and light step counts are not guesses. They are the average monthly API calls per user that the Requesty gateway reported for Claude Code (1,549) and Cline (214) in its May 2026 study of coding-agent traffic. Your own numbers will differ, so the formula later in this guide lets you plug in yours.

Why coding agent bills jumped in 2026

The flat-rate era of AI coding ended between mid-2025 and mid-2026. Three changes pushed developers towards paying per token, whether they noticed it or not.

The result shows up clearly in gateway data. Requesty's study of nine coding agents found the average monthly cost per user rose from about $14 to over $54 between May 2025 and April 2026, a 3.8x increase. Among active users, the average was about $92 a month (about ₹8,740). Claude Code's active users averaged $108 (about ₹10,260), and the 95th percentile hit $291 (about ₹27,660).

For an Indian freelancer or student, ₹10,000 a month is not a rounding error. It is the reason to understand where the money goes.

How an AI coding agent spends tokens

Every coding agent runs the same basic loop. It sends the model a large request, gets back a short reply, acts on it and sends the next request.

  1. System prompt and tool definitions. The agent's own instructions plus a description of every tool it can call (read file, edit file, run command, search). This is the fixed overhead, sent on every step.
  2. Project context. Rules files such as AGENTS.md or CLAUDE.md, a repository map or file contents the agent has read.
  3. Conversation so far. Your request, every earlier model reply and every tool result, such as file contents and test output.
  4. The model's reply. Usually short: a plan, an edit or a tool call. Reasoning models also generate hidden thinking tokens first.

Because steps 1 to 3 are resent every time, input tokens dominate. Requesty measured average input per call growing 68%, from about 50,000 to about 84,000 tokens, over twelve months. That 84,000 figure is the input size used in this guide's worked example.

Output is small by comparison, which is why the cache hit rate matters so much. If most of those 84,000 input tokens are identical to the previous step, a provider with prompt caching charges a fraction of the normal input price for them.

Lever 1: the model's price

Model prices vary by more than 100x per token. Here are the list prices in rupees per million tokens, converted from each provider's published US dollar rates at ₹95.04.

ModelInput (₹ per 1M)Cached input (₹ per 1M)Output (₹ per 1M)Notes
Claude Opus 5.5380.1619.011,900.80Cache writes cost 1.25x input
Claude Sonnet 5.5190.089.50950.40Cache writes cost 1.25x input
Claude Haiku 5.59.500.9547.52Rates for prompts up to 100k tokens. Higher above that
GPT-5.5475.2047.522,851.20Prompts over 272k tokens cost 2x input
DeepSeek V4 Pro (peak)125.454.18376.36Half price off-peak
DeepSeek V4.1 Flash (peak)28.510.57114.05Half price off-peak
DeepSeek V4.1 Flash (off-peak)14.260.2957.02Off-peak is most hours outside weekday mornings UTC

Two things stand out. First, output costs 3x to 6x more than input on every model, so verbose agents and long reasoning are expensive. Second, cached input is 10x to 50x cheaper than fresh input, so caching is the biggest single lever you control.

DeepSeek's peak and off-peak pricing is worth knowing in India. DeepSeek's peak windows are 01:00 to 04:00 and 06:00 to 10:00 UTC on weekdays, which is 6:30 to 9:30 am and 11:30 am to 3:30 pm IST. Evening and night work in India falls in the cheaper window.

Prices change often. Check the provider's own pricing page, or the live model list on Velona, before you commit to a budget. Our guide to the cheapest AI models for coding agents compares more models.

Lever 2: the cache hit rate

Prompt caching stores the start of a request on the provider's side. When the next request begins with exactly the same tokens, those tokens are billed at the cached rate.

The savings are large in practice. A January 2026 study by PwC researchers, "Don't Break the Cache", measured prompt caching on long multi-turn agent sessions across OpenAI, Anthropic and Google models. Caching cut API costs by 41% to 80%, and the savings grew to 54% to 89% when the system prompt reached 50,000 tokens.

Real coding agents get there too. Requesty reported its platform-wide cache hit rate rising from 52% to 86% over a year. Claude Code reached 92%, while Kilo Code sat at 46%. That gap translated into an estimated effective input cost of about $0.30 per million tokens for Claude Code (about ₹28.50) versus about $1.62 for Kilo Code (about ₹154).

Look at the table again to see what this means for you. On Claude Sonnet 5.5, one agent step costs ₹17.39 with no caching and ₹4.14 at a 90% hit rate. Over a heavy month, that is the difference between about ₹26,900 and ₹6,400.

What breaks the cache

Caching only works on an exact prefix match. The PwC study and provider documentation point to the same mistakes:

The study also found a trap. Caching the full context naively can make the first response slower. For one model, it caused an 8.8% regression in time to first token, while caching only the system prompt improved it by about 31%.

Lever 3: agent overhead before your prompt

Each agent sends its own fixed text before your request. That overhead differs a lot between tools.

In a July 2026 test reported by Kunal Ganglani, Systima captured the raw HTTP traffic of Claude Code and OpenCode running the same model on the same tasks. Before the user's prompt, Claude Code sent about 33,000 tokens and OpenCode about 7,000, a 4.7x gap. The test also found that OpenCode's request prefix stayed byte-identical between runs, while Claude Code's changed mid-session, which forced extra cache writes.

The same test showed how project files add up. A 72KB AGENTS.md added about 20,000 tokens to every request for both tools, and five MCP servers added another 5,000 to 7,000.

What does 26,000 extra tokens per step cost? On Claude Sonnet 5.5, about ₹4.94 per step uncached, or about ₹0.25 if it is all served from cache. On DeepSeek V4.1 Flash at peak, about ₹0.74 uncached. Overhead hurts most when the cache keeps missing.

One caution: the Systima test measured token overhead, not code quality. A larger system prompt can make an agent more capable. Measure your cost per solved task, not per call.

Lever 4: reasoning tokens you never see

Reasoning models think before they answer, and those thinking tokens are billed as output tokens, the most expensive kind.

Anthropic's documentation is explicit. Thinking tokens are billed as output for the full reasoning Claude generated, not the visible text. If you request a summarised view of the thinking, you still pay for the full thinking. You can see the count in the usage.output_tokens_details.thinking_tokens field of the response.

OpenAI's GPT-5.5 exposes five reasoning effort levels: none, low, medium (the default), high and xhigh. Claude models expose an effort setting from low to max. DeepSeek's API accepts a thinking switch and a reasoning_effort value. Higher effort usually means more hidden output tokens per step.

The scale can be startling. Tian Pan's May 2026 analysis gives an example of a complex query on Claude Opus using 20,000 to 40,000 thinking tokens for about 500 visible tokens. Most observability dashboards do not capture reasoning tokens, so teams often notice only when the monthly invoice arrives higher than expected.

A practical rule follows. Use low or medium effort for routine agent steps such as reading files and small edits, and raise it only for planning or hard debugging. Our companion guide, Is Thinking Mode Worth It?, covers this in depth.

The formula: estimate your own bill

You can estimate any agent's cost with one formula per step:

cost per step = input tokens × [cache hit rate × cached price + (1 − cache hit rate) × fresh input price]
              + output tokens (including reasoning) × output price

Then multiply by steps per day and working days per month. For Claude models, use the cache-write price (1.25x input) instead of the fresh input price for the uncached share when caching is on.

To find your real inputs:

  1. Input tokens per step. Your gateway or provider dashboard shows input tokens per request. 50,000 to 100,000 is typical for agents on a medium repository.
  2. Cache hit rate. Divide cached input tokens by total input tokens. On Velona, responses report cached tokens in usage.prompt_tokens_details.cached_tokens.
  3. Output tokens per step. Include reasoning tokens. Check the provider's usage fields, not just the visible reply.
  4. Steps per month. Count requests over a normal week and multiply by four.

Worked example in full

Take Claude Sonnet 5.5 at a 90% cache hit rate:

Now change one thing at a time. Drop to 50% cache hits and the step costs ₹11.80. Switch to DeepSeek V4.1 Flash at peak with 90% hits and it costs ₹0.45. Add 8,000 hidden reasoning tokens per step on Sonnet and the output part alone rises by about ₹7.60, which nearly triples the step cost.

Cline, OpenCode, Aider and Claude Code: where each one's cost comes from

All four agents can be cheap or expensive. Here is what drives the bill in each, based on how they work and on the published measurements above.

Cline

Cline runs inside VS Code and asks you to approve each action by default. That approval step naturally limits runaway loops, which may be one reason Cline users in the Requesty data made far fewer calls (214 a month on average) than Claude Code users.

OpenCode

OpenCode is a fast terminal agent. In the Systima test, it sent about 7,000 tokens of overhead and kept a byte-identical prefix between runs, which is exactly what prompt caching rewards.

Aider

Aider is a git-first pair programmer for the terminal. You add files to the chat explicitly, so you control how much context is sent.

Claude Code

Claude Code is Anthropic's own agent. It is capable and widely used, and Requesty found Claude's share of coding-agent spend rose to about 92%. It is also the heaviest user of calls in that data, at 1,549 a month on average.

Claude Code is built for Claude models. Cline, OpenCode and Aider work with any OpenAI-compatible API, so you can mix cheap and premium models across tasks with a single key.

Subscription or pay-as-you-go?

A subscription makes sense when your usage is steady and the plan's included allowance covers it. Pay-as-you-go makes sense when usage is bursty or when cheap models can do most of the work.

GitHub Copilot's 2026 plans illustrate the trade-off in rupees, converted at ₹95.04:

PlanPrice per monthIncluded AI CreditsCredit value
Copilot Pro$10 (about ₹950)1,500$15 (about ₹1,426)
Copilot Pro+$39 (about ₹3,707)7,000$70 (about ₹6,653)
Copilot Max$100 (about ₹9,504)20,000$200 (about ₹19,008)

Credits are spent at each model's token rate, so the same caching and model-choice levers still decide how far they go. Once credits run out, usage is either bought or blocked by your spending limit.

Compare that with the table at the top. A heavy month on DeepSeek V4.1 Flash with good caching costs less than the cheapest Copilot plan. A heavy month on GPT-5.5 costs more than Copilot Max. Neither is wrong. The right choice depends on which model your tasks actually need.

Do not forget GST

If you pay a foreign AI provider directly, 18% IGST usually applies on top of the list price.

This is general information, not tax advice. Confirm your treatment with a chartered accountant. Paying through an Indian gateway that issues a proper GST invoice in rupees can simplify the paperwork.

How to cut your AI coding bill: a checklist

  1. Measure first. Log input, cached, output and reasoning tokens per step for one week.
  2. Fix the cache. Move timestamps and session IDs to the end of the prompt, keep tool definitions stable and avoid mid-session compaction unless you must.
  3. Trim fixed overhead. Keep AGENTS.md or CLAUDE.md short and load only the MCP servers a task needs.
  4. Match the model to the step. Use a cheap model for reading, searching and small edits. Save frontier models for planning and hard bugs.
  5. Set reasoning effort deliberately. Default to low or medium and raise it per task, not per session.
  6. Work off-peak where it helps. DeepSeek's off-peak window covers most Indian evenings and nights at half price.
  7. Cap each key. Give every agent its own key with a monthly budget, so one runaway loop cannot drain your wallet.
  8. Track cost per solved task. A cheap model that needs three attempts can cost more than a strong model that succeeds once. Our guide to evaluating coding models by cost per solved task shows how.

Set up a budgeted key for each agent with Velona

Velona is an INR-native AI API gateway. You top up with UPI, pay in rupees and use one OpenAI-compatible endpoint for Claude, GPT, DeepSeek, Qwen and hundreds of other models.

The OpenAI-compatible base URL is:

https://velona.in/v1

Aider

export OPENAI_API_BASE=https://velona.in/v1
export OPENAI_API_KEY=your_velona_key
aider --model openai/<model-id>

Replace <model-id> with an ID from the Velona models page. Create a Velona account, add ₹10 with UPI and make one budgeted key per agent. The openai/ prefix tells Aider to treat Velona as an OpenAI-compatible provider.

OpenCode

Add a provider to opencode.json:

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "velona": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "Velona",
      "options": {
        "baseURL": "https://velona.in/v1",
        "apiKey": "{env:VELONA_API_KEY}"
      },
      "models": {
        "<model-id>": { "name": "Your chosen model" }
      }
    }
  }
}

Cline

  1. Open Cline settings in VS Code and choose OpenAI Compatible as the API provider.
  2. Set the base URL to https://velona.in/v1.
  3. Paste your Velona key and enter a model ID from the Velona models page.

For a full walkthrough of each tool, see our guide to using Velona with OpenAI-compatible coding agents, and our comparison of Cline, Aider, OpenCode and OpenClaw.

Frequently asked questions

How much does Cline cost per day?

Cline itself is free and open source. You pay only for the model. Using this guide's example of 84,000 input tokens and 1,500 output tokens per step with 90% caching, 50 steps a day costs about ₹22 on DeepSeek V4.1 Flash at peak and about ₹207 on Claude Sonnet 5.5, before GST.

Is there a cheaper alternative to Claude Code?

Yes, if your tasks do not need Claude specifically. Cline, OpenCode and Aider work with any OpenAI-compatible API, so you can run them on cheaper models such as DeepSeek V4.1 Flash or Claude Haiku 5.5, and switch to a frontier model only for hard tasks.

Which coding agent uses the fewest tokens?

It depends on the task and settings. In one published test, OpenCode sent about 7,000 tokens of overhead before the user's prompt versus about 33,000 for Claude Code. That test did not measure task success, so the cheapest agent per call is not always the cheapest per solved task.

Why is my AI coding bill so high?

The usual causes are a low cache hit rate, hidden reasoning tokens billed as output, a large fixed overhead from rules files and MCP servers, and expensive models used for routine steps. Check your usage fields for cached and reasoning tokens first.

Does prompt caching really save money?

Yes. A 2026 study of long agent sessions measured cost reductions of 41% to 80% with prompt caching. It only works when the start of each request stays identical, so dynamic content must go at the end.

Do reasoning tokens cost extra?

They are billed as output tokens, which are the most expensive tokens. On Claude, you pay for the full thinking even if you only see a summary. Lower the effort setting for routine steps to control this.

Can I pay for AI coding agents with UPI in India?

Yes. Velona lets you top up with UPI and pay per token in rupees, then use that balance with Cline, OpenCode, Aider and other OpenAI-compatible agents.

Limits of this guide

The cost figures in this guide are modelled, not measured on a single benchmark. They combine published prices with published averages for tokens per call and calls per month from one gateway's data. Your repository, prompts and habits will change the numbers. We are running a controlled benchmark of these agents on the same coding tasks, measuring rupees per solved task, and will publish the results separately.

Sources