Code Generation API in India

Generate and Review Code at Scale

Build code generators, dev tools, and AI-assisted IDEs using unoblox. Call one OpenAI-compatible endpoint with your IDE, CLI, or CI/CD pipeline—all billed in rupees.

Why code-gen over IDE plugins

Independence: No vendor lock-in to Cursor, GitHub Copilot, or VS Code extensions. Cost transparency: Pay per token; audit every call in your logs. Customization: Embed your code style, libraries, and guardrails in system prompts. Offline-friendly: Cache repeated context (function signatures, frameworks) to cut costs and latency.

Best models for code tasks

Model	Input/Output (₹)	Strength	Context
Qwen3.8-27B	₹16.32 / ₹48.96	Fast, handles most tasks, vision for screenshots	262K tokens
Qwen3 235B	₹9.07 / ₹55.44	Strong reasoning, still cheap	262K tokens
Claude Opus	₹504 / ₹2520	Best reasoning & debugging	200K+ tokens
DeepSeek V3.2	₹26.21 / ₹38.30	Reasoning-heavy tasks	164K tokens

Generate a Python function in 3 steps

Step 1: Prepare a system prompt with your stack

You are a Python expert. 
Framework: FastAPI. 
Libraries: SQLAlchemy, Pydantic, httpx.
Style: PEP 8, type hints, docstrings.
Generate production-ready code, no comments.

Step 2: Call the API

POST https://api.unoblox.ai/v1/chat/completions
Authorization: Bearer ub-gw-...

{
  "model": "qwen/qwen3-235b-a22b-instruct-2507",
  "messages": [
    {"role": "system", "content": "[Your code style guide]"},
    {"role": "user", "content": "Write a FastAPI endpoint to fetch user profile from a Postgres DB using SQLAlchemy ORM."}
  ],
  "max_tokens": 500
}

Step 3: Test and iterate — Parse the code block, run tests, commit if green, iterate if tests fail.

Real scenario: Code review at scale

Review 50 PRs per day:

1. Extract diff from PR.
2. Call Claude Opus with diff + codebase context.
3. Return structured feedback (lint issues, security risks, refactoring ideas).
4. Post as GitHub comment.

Cost: 50 PRs/day × (~1,800 input + ~200 output) tokens ≈ 90k input + 10k output tokens/day ≈ ₹71/day on Claude Opus.

Guardrails for generated code

• Always run tests before merging.
• Use prompted linting ("no eval(), no hardcoded secrets").
• Cache library docs / framework examples to avoid repeated context.
• Log all generations for auditing.

Frequently asked questions

Q: How do I ensure generated code matches my codebase style? A: Include a code sample in the system prompt. Examples of your naming, error handling, logging patterns—the model learns by example.

Q: Can I use this in Cursor, VS Code, or my IDE? A: Yes. Cursor and VS Code use OpenAI-compatible APIs. Point the base_url to https://api.unoblox.ai/v1 and use your unoblox API key.

Q: What's the latency for single-file code generation? A: Streaming starts quickly; total time scales with output length and model choice. Qwen3.8-27B is the fastest budget option; Claude Opus trades some speed for accuracy on hard bugs.

Q: Can I generate tests from code? A: Yes. Pass the function + framework (pytest, unittest) in the prompt; request "Generate 5 unit tests for this function."

Q: How do I handle language-specific code (Go, Rust, Kotlin)? A: DeepSeek V3.2 and Claude Opus both excel at polyglot code. Qwen3 is weaker on Rust. Test on your language; switch if needed.

Q: Can I use this for code reviews in production? A: Yes. Parse git diffs, call the API with guidance ("flag security issues, suggest refactoring"), aggregate results. Scale to 100+ reviews/day.

Get started in rupees → https://unoblox.ai/sign-in