Cheapest API for Coding Agents in 2026

Coding agents are different from chatbots in one important way: they make a lot of calls per task. A single refactor can trigger dozens of model requests — plan, edit, run tests, read the error, try again. At that call volume, the model's unit price matters more than the demo quality.

This guide breaks down what to compare, and gives you a starting shortlist of Chinese coding-capable models with their published list prices from the live NovAI price feed (snapshot 2026-10-26).

Why per-token price is the wrong metric for agents

An agent session is a loop, not a single prompt. The real cost driver is total tokens per completed task: input tokens (usually the big number, because the agent re-sends context), output tokens, retries, and any tool-call overhead. A model that looks 2x cheaper per token can cost more per task if it needs an extra retry or writes longer diffs.

So compare in this order: model capability on your task → tokens per successful task → price per unit → latency. The last one matters because slow models make agents feel broken.

Starting shortlist (list prices, snapshot)

All prices below are the provider's published list price passed through by NovAI at a 1.0x multiplier with no platform fee. The unit column is what the feed publishes — for chat models this is the comparable cost unit, not always a per-token figure, so always read the unit before comparing numbers.

A practical routing pattern

Most teams do not pick one model — they route by step:

1. Cheap model for bulk edits (formatting, renames, test scaffolding): GLM-5.3-Flash or Qwen3 Coder Flash.
2. Mid model for the main loop: DeepSeek-V4-Flash — good balance of capability, price and speed.
3. Strong model only for planning/debugging steps: DeepSeek-V4-Pro or GLM-5.3.
4. Coding-tuned model when tool-calling reliability matters: Kimi-K2.7-Code (or the HighSpeed variant if latency is the bottleneck).

Because every model is behind the same OpenAI-compatible endpoint and key, switching per step is a model parameter change — no extra accounts, no second billing relationship.

Setup (OpenAI SDK)

from openai import OpenAI

client = OpenAI(
    base_url="https://aiapi-pro.com/v1",
    api_key="nvai-...",   # from /free/index.html
)

resp = client.chat.completions.create(
    model="deepseek-v4-flash",
    messages=[{"role": "user", "content": "Fix the failing test in src/parser.py"}],
)
print(resp.choices[0].message.content)

Let the agent check prices itself (MCP)

If your coding agent supports MCP (Claude Code, Cursor, Cline, VS Code), add the read-only price server and the agent can query cost mid-session instead of guessing:

claude mcp add --transport http novai-prices https://aiapi-pro.com/api/mcp

Tools: cheapest_models, model_price, list_categories. No API key required for price lookups. Full setup guide: Chinese AI models in Claude Code and Cursor via MCP.

How to measure cost per task yourself

1. Collect 30–100 real agent tasks with their final diffs/tests.
2. Run each task twice: once on your current model, once on the candidate.
3. Log input/output tokens and number of calls per task from the API response usage field.
4. Compute cost per successful task, not cost per token — include retries and failed runs.
5. Only then compare the price columns.

This is also how you catch model swapping: if a candidate claims to be a specific model but its token counts and behaviour diverge from the official endpoint, your eval will show it. Independent canary checks for NovAI are published in the awesome-ai-gateway issue.

Where the prices come from

The live feed is machine-readable: /china-ai-leaderboard.json (regenerated daily from the model catalogue). The fee policy is in /transparency.json: 0% platform fee, 1.0x multiplier, unused balance refundable within 7 days, failed video/3D jobs refunded in full.

FAQ

Is there a free model good enough for coding agents?

GLM-4.7-Flash is $0 to call and works for simple edits and scaffolding, but it is the cheapest tier - use it for low-stakes steps, not for complex refactors. Before the first top-up a low request-rate limit applies (5 req/min per the published policy).

Which model is the best default for an agent loop?

DeepSeek-V4-Flash is a reasonable starting default: it balances capability, price and latency. Route harder planning or debugging steps to a stronger model and cheap bulk edits to GLM-5.3-Flash.

Do I need separate accounts for each model?

No. All models are behind one OpenAI-compatible endpoint and one key, so switching is a model parameter change. Billing is at provider list price with a 1.0x multiplier and no platform fee.

How do I compare costs fairly?

Compare cost per successful task, not price per unit: include retries, failed runs and the number of calls per task. The unit column in the price feed differs by model type, so read it before comparing numbers.

Can my coding agent check prices itself?

Yes, if it supports MCP. Add the read-only price server at https://aiapi-pro.com/api/mcp and the agent can call cheapest_models, model_price and list_categories without an API key.