# A purpose-built coding model, one endpoint — no IDE plugin required

General-purpose models write passable code. A purpose-built coding model writes code that's correct. Here's the difference, in one request.

curl https://aibridge-api.com/v1/chat/completions \
  -H "Authorization: Bearer mb-xxxxxxxx" \
  -d '{
    "model": "deepseek-coder",
    "messages": [
      {"role": "system", "content": "You are an expert programmer. Fix bugs with minimal, correct changes."},
      {"role": "user", "content": "This function throws IndexError on empty input. Fix it:\n\n<code>"}
    ]
  }'

deepseek-coder is tuned for exactly this — code generation, debugging, review, and refactoring. And it lives behind the same OpenAI-compatible endpoint as the general models, so you reach it with the SDK you already use.

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Why "it writes code" isn't the same as "it's for code"

A general chat model can produce code. It just wasn't optimized for it. The differences show up where it matters:

• Correctness over fluency — a coding model prioritizes working output over natural-sounding output
• Edge cases — empty inputs, nulls, boundary conditions that a chat model might gloss over
• Minimal diffs — when you ask for a fix, you want the smallest correct change, not a rewrite
• Idiomatic code — it knows the conventions and APIs of the language you're in

That's the niche deepseek-coder fills. For the harder, reasoning-heavy stuff — design a system, prove an algorithm, untangle a gnarly bug — you pair it with deepseek-v4-pro or deepseek-reasoner. A coding stack, not a coding tool.

A coding workflow, one endpoint

Task	Model	Why
Autocomplete / boilerplate	deepseek-coder	Fast, idiomatic, low cost
Bug fix with a minimal diff	deepseek-coder	Correct, edge-case aware
Code review & critique	deepseek-coder	Reads like a senior reviewer
Refactor & explain	deepseek-v4-pro	Reasoning over structure
Algorithm design / proofs	deepseek-reasoner	Chain-of-thought for hard problems

Every step through the same key, the same SDK, the same stream: true you'd use anywhere else. No new IDE plugin to install, no separate subscription to manage.

Why an endpoint beats an IDE plugin

Coding copilots are great inside the editor. But a lot of code work happens outside it:

• CI pipelines — generate tests, summarize a PR, flag risky changes
• Backend services — a code-review bot that runs on every commit
• CLI tools & scripts — a fix-my-tests command, a generate-changelog command
• Custom tooling — anything where you control the prompt and the flow

An API gives you that. A plugin gives you a chat box.

The full model menu

• DeepSeek — deepseek-coder, deepseek-v4-pro, deepseek-v4-flash, deepseek-reasoner, deepseek-chat
• Qwen — qwen3-235b-a22b, qwen-plus (131K), qwen-max
• GLM — glm-4-plus, glm-4-air, glm-4-flash
• Moonshot — kimi-k3 (1M context), moonshot-v1-128k / -32k / -8k

Pricing that keeps coding cheap

• Free tier: 500K tokens/month (weighted)
• Pro: $9.90/month for 5M tokens
• Top-ups: 1M / $2.99 · 5M / $9.90 · 20M / $29.90 (never expire)

The takeaway

You don't need another subscription or another plugin to get serious about AI-assisted coding. You need a coding model on the endpoint you already call.

Generate, debug, and review with deepseek-coder — free tier included.

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