Changelog

Type

March 2026

Feature

System Update

New Brand Identity

We've rolled out a completely refreshed visual identity for Apertis.

New Logo

Our new "Stacked A" mark features a dual-layer geometric design with metallic teal gradients, replacing the previous rainbow-arc logo. The layered depth effect represents the multiple AI providers unified behind a single API.

What Changed

  • Logo: Geometric Stacked A with Apertis Teal (#2dd4bf → #0d9488)
  • Favicon: Full icon set across 25 sizes (16px–512px) for crisp rendering on every device - OG Images: All social preview images updated with new branding
  • Loading Animation: Inline SVG with breathing pulse effect replaces static image spinner
  • Header & Footer: Transparent logo mark with brand name for both light and dark modes

What Didn't Change

Your API keys, endpoints, SDK integrations, and billing — everything works exactly as before. This update is purely visual.

Happy Building.

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Feature

Feature Added

✨ Billing Credits API — Check Your Balance Programmatically

We've launched a new API endpoint that lets you query your remaining credits and subscription quota using your API key — no dashboard login required.

Endpoint: GET /v1/dashboard/billing/credits

---

The Problem

Until now, checking your Apertis balance meant opening the dashboard in a browser. This creates friction in several real-world scenarios:

  • Coding agents running overnight — Claude Code, Cursor, or Kilo Code sessions can burn through credits while you sleep. By the time you notice, the session

has already failed mid-task with an insufficient balance error.

  • Team automation pipelines — CI/CD workflows that call AI APIs have no way to pre-check if there's enough budget before kicking off an expensive batch job.
  • Multi-key management — If you distribute API keys across projects or team members, there's no programmatic way to monitor which keys are running low.
  • Subscription cycle awareness — Subscription users couldn't check how much cycle quota remains without visiting the dashboard. Easy to accidentally exhaust

your monthly allocation without realizing it.

We looked at what other providers offer: OpenAI has no balance endpoint (this is one of the most requested features on their community forum). Anthropic's Admin API can query cost reports but not remaining credits, and requires a separate admin key. Neither provides a simple "how much do I have left?" API call.

We decided to solve this properly.

---

What It Returns

A single request gives you the complete picture:

PAYG users get their credit balance in USD:

  {
    "object": "billing_credits",
    "is_subscriber": false,
    "payg": {
      "remaining_usd": 12.50,
      "used_usd": 7.50,
      "total_usd": 20.00,
      "is_unlimited": false,
      "monthly_limit_usd": 50.00,
      "monthly_used_usd": 7.50,
      "monthly_reset_day": 1
    }
  }

  Subscription users see both their cycle quota and PAYG balance:

  {
    "object": "billing_credits",
    "is_subscriber": true,
    "payg": {
      "remaining_usd": 0.95,
      "used_usd": 0.05,
      "total_usd": 1.00,
      "is_unlimited": false
    },
    "subscription": {
      "plan_type": "pro",
      "status": "active",
      "cycle_quota_limit": 1000,
      "cycle_quota_used": 350,
      "cycle_quota_remaining": 650,
      "cycle_start": "2026-03-16T10:02:35Z",
      "cycle_end": "2026-04-16T10:02:35Z",
      "payg_fallback_enabled": true,
      "payg_spent_usd": 2.50,
      "payg_limit_usd": 10.00
    }
  }

---

Use Cases

1. Pre-flight budget check before expensive operations

Before kicking off a large batch job or a long coding agent session, check if you have enough credits:

import requests

credits = requests.get(
     "https://api.apertis.ai/v1/dashboard/billing/credits",
      headers={"Authorization": "Bearer sk-your-key"}
  ).json()

if credits["is_subscriber"]:
    remaining = credits["subscription"]["cycle_quota_remaining"]
    if remaining < 100:
        print(f"Warning: only {remaining} quota remaining in this cycle")
    else:
        remaining = credits["payg"]["remaining_usd"]
        if remaining < 1.0:
            print(f"Warning: only ${remaining:.2f} credits left")

2. Automated low-balance alerts

Set up a cron job or monitoring script that pings you when credits drop below a threshold:

  #!/bin/bash
  BALANCE=$(curl -s https://api.apertis.ai/v1/dashboard/billing/credits \
    -H "Authorization: Bearer $APERTIS_KEY" | jq '.payg.remaining_usd')

  if (( $(echo "$BALANCE < 5.0" | bc -l) )); then
    echo "Low balance alert: $BALANCE USD remaining" | \
      mail -s "Apertis Low Balance" [email protected]
  fi

What Makes This Different

This is an Apertis exclusive. We surveyed every major AI API provider:

  • OpenAI: No balance endpoint. The most upvoted feature request on their developer forum for over two years. Their Usage API shows historical spending but not remaining credits.
  • Anthropic: Admin API provides cost reports, but requires a separate admin key and doesn't return remaining balance.
  • Together AI, OpenRouter: No programmatic balance check.

We believe knowing your balance should be as simple as making one API call. No special keys, no dashboard login, no scraping.

Full API documentation

Enjoy it.

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Feature

Models Added

Add GLM 5 Turbo

GLM 5 Turbo

GLM 5 Turbo

GLM-5 Turbo is a high-performance model from Z.ai optimized for fast inference and agent-driven workflows. Designed for real-world environments such as OpenClaw scenarios, it delivers strong performance across long execution chains and complex task pipelines. The model features improved instruction decomposition, tool integration, scheduled and persistent execution, and enhanced stability for extended multi-step tasks, making it well suited for autonomous agents and production automation workflows.

Enjoy it!

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Feature

Models Added

Add OpenRouter and NVIDIA models

Nemotron 3 Super (Free)Healer AlphaHunter Alpha

Nemotron 3 Super (Free)

NVIDIA Nemotron 3 Super is a 120B-parameter open hybrid Mixture-of-Experts model designed for complex multi-agent and long-horizon reasoning workflows. It activates only 12B parameters per token, enabling high compute efficiency while maintaining strong accuracy on advanced tasks. Built on a hybrid Mamba–Transformer MoE architecture with multi-token prediction (MTP), the model delivers significantly higher token generation throughput than leading open models.

Healer Alpha

Healer Alpha is a frontier omni-modal model that integrates vision, audio understanding, reasoning, and action capabilities within a single system. It can natively perceive visual and auditory inputs, reason across multiple modalities, and execute complex multi-step tasks with precision, enabling advanced real-world agentic applications. Note: Prompts and completions processed by this model are logged by the provider and may be used for model improvement.

Hunter Alpha

Hunter Alpha is a frontier intelligence model with over 1 trillion parameters and a 1M-token context window, designed specifically for agentic applications. It excels at long-horizon planning, complex reasoning, and sustained multi-step task execution, delivering strong reliability and precise instruction following for advanced agent frameworks such as OpenClaw. Note: Prompts and completions processed by this model are logged by the provider and may be used for model improvement.

These models are in free tier.

Enjoy it!

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Feature

Models Added

Add Qwen3.5-9B & Seed-2.0-Lite

Qwen3.5-9BSeed-2.0-Lite

Qwen3.5-9B

Qwen3.5-9B is a multimodal foundation model from the Qwen3.5 family, built to deliver strong reasoning, coding, and visual understanding within an efficient 9B-parameter architecture. It adopts a unified vision-language design with early fusion of multimodal tokens, enabling the model to process and reason across text and images within the same context.

With balanced multimodal capability and efficient deployment requirements, Qwen3.5-9B is well suited for applications that combine visual analysis, coding assistance, and general reasoning.

Seed-2.0-Lite

Seed-2.0-Lite is a balanced model designed for high-frequency enterprise workloads, optimizing for both capability and cost efficiency. It surpasses the previous-generation Seed-1.8 in overall performance while maintaining stable, production-ready quality. The model supports long-context processing, multi-source information fusion, multi-step instruction execution, and high-fidelity structured outputs.

It is well suited for enterprise scenarios such as unstructured data processing, content generation, search and recommendation, and data analysis, delivering reliable results while significantly reducing operational cost.

Enjoy it!

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