GPT-4o vs Claude 3.5 Sonnet: Cost Comparison in Rupees (2026)

Provider list prices (USD)

GPT-4o is priced at $2.50 per million input tokens and $10.00 per million output tokens. Claude 3.5 Sonnet is priced at $3.00 per million input tokens and $15.00 per million output tokens. Both prices are from the respective provider's public pricing pages as of early 2026.

GPT-4o also offers a cached input discount of 50%, bringing repeated-context costs down to $1.25 per million tokens — useful for RAG and long-system-prompt use cases.

Converting to INR: what you actually pay

At ₹87 per USD with a 5% forex buffer and 5% markup, the effective multiplier is roughly 95.9×:

| Model | Input (per 1M tokens) | Output (per 1M tokens) | |-------|----------------------|----------------------| | GPT-4o | ₹240 | ₹960 | | Claude 3.5 Sonnet | ₹288 | ₹1,440 | | GPT-4o (cached input) | ₹120 | ₹960 |

For a simple 1,000-token prompt with 500-token completion, GPT-4o costs ₹0.72 and Claude 3.5 Sonnet costs ₹1.01. The difference is small per request but compounds at volume.

Which model is cheaper for common tasks?

For high-volume, straightforward tasks — classification, summarisation, structured extraction — GPT-4o is cheaper. For tasks requiring long context, nuanced reasoning, or code generation with large codebases, Claude 3.5 Sonnet's quality often justifies the higher cost by reducing retry and correction overhead.

Run both models on 50–100 representative prompts from your workload, measure pass rate and output quality, then divide the cost difference by the quality gap. That ratio tells you whether the premium is worth it.

from openai import OpenAI
 
client = OpenAI(
    base_url="https://api.aicredits.in/v1",
    api_key="sk-your-aicredits-key",
)
 
test_prompt = "Summarise the following support ticket in one sentence: 'My payment keeps failing even though my card is valid and I have sufficient balance. I tried three times.'"
 
for model in ["openai/gpt-4o", "anthropic/claude-3-5-sonnet-20241022"]:
    response = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": test_prompt}],
    )
    usage = response.usage
    print(f"\n{model}")
    print(f"  Output: {response.choices[0].message.content}")
    print(f"  Tokens: {usage.prompt_tokens} in / {usage.completion_tokens} out")

Cheaper alternatives worth considering

Claude 3.5 Haiku costs $1.00 per million input tokens — roughly ₹96 in INR — and handles most customer-facing tasks well at a fraction of Sonnet's price. GPT-4o Mini costs $0.15 per million input tokens and works well for classification and triage pipelines.

Mixing models in production

A common pattern: route simple triage and intent classification to a cheap fast model (Haiku, GPT-4o Mini), then invoke a capable model (Sonnet, GPT-4o) only for generation or reasoning steps. This hybrid approach typically cuts per-request cost by 40–70% with minimal quality impact.

AICredits lets you do this with a single API endpoint and one wallet. Route by setting the model field in your request, and the billing is unified across all providers.