Changelog

A Model Context Protocol server that lets any MCP-compatible AI assistant call Apertis directly. No setup, no wrapper code — just install once.

Install (Claude Code):

claude mcp add apertis -- npx -y @apertis/mcp-server

Nine tools shipped:

| Tool | What it does | |------|--------------| | list_models | List models with optional free/paid + capability filters | | get_model_info | Detailed info for a specific model (pricing, context, provider) | | compare_models | Side-by-side comparison of 2–5 models | | check_quota | Account balance, subscription status, remaining quota | | get_usage_stats | Usage by model and period (today / week / month) | | list_api_keys | List your keys (masked) with status and quota | | create_api_key | Create a new key with an optional quota limit | | suggest_model | Freeform keyword search over the full catalog | | recommend_model | Curated Apertis pick for a task type with live pricing *(new in v0.3.0)* |

→ Guide: docs.apertis.ai/api/sdks/mcp-server → npm: `@apertis/mcp-server`

Agent Skills — one-command install for 45+ AI tools

Three curated skills that teach your AI assistant how to use Apertis correctly. Install once, works everywhere.

npx skills add theQuert/apertis-skills

Compatible with Claude Code, Cursor, GitHub Copilot, Codex, Gemini CLI, and 45+ other AI coding tools.

| Skill | What your agent learns | |-------|------------------------| | apertis-api | Auth, endpoints, :web suffix, MCP reference — the complete API surface | | apertis-model-picker | Opinionated model picks by task type with reasoning | | apertis-migrate | One-line swap from OpenAI SDK to Apertis |

→ Source: github.com/theQuert/apertis-skills

GET /v1/recommend — dynamic model selection endpoint

Ask Apertis what to use for a task and get back the curated pick with live pricing. Recommendations update as models are added, retired, or re-priced — your code stays the same.

curl "https://api.apertis.ai/v1/recommend?task=coding&budget=medium" \
  -H "Authorization: Bearer $APERTIS_API_KEY"

Task types: coding, long-context, fast-chat, reasoning, vision Budget tiers: low, medium (default), high

Response shape:

{
  "model": "claude-sonnet-4-6",
  "pricing": { "input_per_1m": 2.40, "output_per_1m": 12.00 },
  "why": "Best coding ability per dollar. 200K context.",
  "alternatives": [
    { "model": "deepseek-v3", "note": "3x cheaper, good for simpler coding" },
    { "model": "claude-opus-4-6", "note": "most capable, higher cost" }
  ]
}

Use the returned model ID directly in your next /v1/chat/completions call.

→ Reference: docs.apertis.ai/api/utilities/recommend

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Docs

• New: @apertis/mcp-server SDK guide with recommend_model walkthrough
• New: GET /v1/recommend endpoint reference with Python example
• Updated: Cursor integration guide with new screenshots and apertis/ prefix convention
• Updated: Ideas page now publicly browsable at docs.apertis.ai/help/ideas
• Updated: Timeout documentation — X-Timeout header, 408 status semantics

---

Why this release

We kept seeing two questions in support:

1. *“Which model should I use?”* 2. *“How do I wire Apertis into my agent/IDE?”*

recommend_model + the skills + the MCP server answer both — without asking you to paste the same instructions into every new session. Your agent now picks the right model and knows how to call us, natively.

Claude Opus 4.7

Opus 4.7 is the next generation of Anthropic's Opus family, designed for long-running, asynchronous agent workflows. Building on Opus 4.6, it delivers stronger performance on complex, multi-step tasks and more reliable execution across extended pipelines such as large codebases, multi-stage debugging, and end-to-end project orchestration. Beyond coding, Opus 4.7 enhances knowledge work capabilities, including document drafting, presentation creation, and data analysis. With strong coherence over long outputs and sessions, it is well suited for tasks requiring persistence, judgment, and sustained execution.

Enjoy it.

What's new

When Apertis routes your request to an upstream provider, it waits a set amount of time before switching to the next available channel. Previously this was fixed at 30 seconds — fine for most models, but too short for preview and reasoning models processing large context windows.

You can now adjust this in Settings → Subscription Keys → Fallback Timeout (range: 5s–300s).

Who should change this

• Using gemini-3-flash-preview, claude-opus-4-thinking, or other preview/reasoning models with large prompts? Increase to 120s+
• Using standard models like gpt-4o, claude-sonnet-4? Default 30s is fine

How it works

1. Go to Settings → Subscription Keys 2. Find Fallback Timeout in the metadata section 3. Enter your preferred value in milliseconds (e.g., 120000 for 120s) 4. Click Save

Changes take effect immediately.

Claude Opus 4.6 (Fast)

Opus 4.6 is Anthropic's more faster version of Opus 4.6 model for coding and long-running professional workflows, designed for agents that operate across entire workflows rather than single prompts. It demonstrates strong performance on large codebases, complex refactoring, and multi-step debugging, with improved contextual understanding, deeper problem decomposition, and higher reliability on challenging engineering tasks compared to earlier generations. Beyond software development, Opus 4.6 excels at sustained knowledge work, producing near production-ready documents, technical plans, and analyses in a single pass while maintaining coherence across long outputs and extended sessions. Its strength in persistence, judgment, and structured execution makes it well suited for technical design, migration planning, and end-to-end project execution.

Enjoy it.

GLM 5.1

GLM-5.1 delivers a major advancement in coding capability, with significant improvements in handling long-horizon tasks. It is designed to operate beyond short interactions, enabling continuous, autonomous execution over extended periods. The model can work independently on a single task for 8+ hours, performing planning, execution, and iterative self-improvement to produce complete, engineering-grade results, making it well suited for complex development workflows and autonomous agent systems.

Enjoy it.