Changelog

MiMo-V2.5

MiMo-V2.5 is Xiaomi's native omnimodal model, delivering pro-level agentic performance at roughly half the inference cost. It surpasses MiMo-V2-Omni in multimodal perception, particularly in image and video understanding. With a 1M-token context window, it can handle complete documents, extended conversations, and complex task contexts in a single pass.

Combining strong reasoning, rich perception, and cost efficiency, MiMo-V2.5 is well suited for integration into advanced agent frameworks and real-world multimodal applications.

MiMo-V2.5-Pro

MiMo-V2.5-Pro is Xiaomi's flagship model, delivering top-tier performance in agentic capabilities, complex software engineering, and long-horizon tasks. It ranks highly on benchmarks such as ClawEval, GDPVal, and SWE-bench Pro, demonstrating strong real-world reliability. The model can autonomously complete professional tasks that would take human experts days or weeks, executing thousands of tool calls within a single workflow.

With a 1M-token context window, it is well suited for integration into advanced agent frameworks and large-scale task orchestration systems.

Enjoy it.

GPT Image 2

GPT Image 2 combines OpenAI's GPT-5.4 with advanced image generation capabilities from GPT Image 2, enabling fully integrated multimodal workflows.

It allows users to seamlessly transition between reasoning, coding, and visual generation within a single interaction, making it well suited for creative, development, and agent-driven applications that require both intelligence and visual output.

Enjoy it.

Kimi K2.6

Kimi K2.6 is Moonshot AI's next-generation multimodal model, designed for long-horizon coding, UI/UX generation, and multi-agent orchestration. It handles complex end-to-end development tasks across languages such as Python, Rust, and Go, and can transform prompts and visual inputs into production-ready interfaces.

Powered by a scalable agent swarm architecture, K2.6 can coordinate hundreds of parallel sub-agents for autonomous task decomposition, enabling the generation of documents, websites, and spreadsheets in a single run without human intervention.

Enjoy it.

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:

→ 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.

→ 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

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.

Effective 2026-04-24 at 00:00 UTC, quota multipliers across Lite, Pro, and Plus plans will be adjusted in response to recent upstream AI provider pricing changes.

Why

• Anthropic Claude (upstream routing channel) base rates have trended upward across multiple providers
• Z.AI GLM-5.1 base rates have trended upward across multiple providers
• OpenAI GPT-5.4 serving costs have increased on several routes
• Additionally, claude-opus-4-6 on the Lite plan is being lowered because our review showed the previous multiplier was set higher than current real cost justifies

Changes

Lite Plan ($12/month, 600 quota per cycle)

• glm-5.1: 0.51 → 1.5
• code:claude-opus-4-6: 5.00 → 10.0
• claude-opus-4-6: 5.00 → 2.0 (decrease)
• gemini-3-flash-preview: 0.10 → 0.2
• gemini-3.1-pro-preview: 0.43 → 0.77

Pro Plan ($25/month, 900 quota per cycle)

• glm-5.1: 0.37 → 1.5
• claude-opus-4-6: 1.00 → 1.5
• gpt-5.4: 0.62 → 1.0
• code:claude-opus-4-6: 0.75 → 1.5

Plus Plan ($60/month, 1,500 quota per cycle)

• claude-opus-4-6: 0.75 → 1.5
• glm-5.1: 0.25 → 1.0
• gpt-5.4: 0.45 → 0.8
• claude-opus-4-7: 3.00 → 4.0

What stays the same

• Monthly subscription fee
• Billing cycle and quota allowance per plan
• Model access and plan tiers
• Pay-As-You-Go (PAYG) fallback behavior

Your options

If you do not agree with the changes, you may cancel your subscription at any time before 2026-04-24 00:00 UTC at https://apertis.ai/setting. Affected users will also be notified by email.

For questions, contact us at hi@apertis.ai.