Changelog

Qwen3.6-35B-A3B

Qwen3.6-35B-A3B is an open-weight Mixture-of-Experts (MoE) multimodal model designed for agentic coding and long-horizon workflows. It features ~35–36B total parameters with ~3B activated per token, enabling strong performance with high inference efficiency. The model supports text and image inputs with a ~260K token context window, and is optimized for repository-level reasoning, multi-step development, and tool-driven workflows.

With strong benchmark performance and improved coherence across extended tasks, Qwen3.6-35B-A3B is well suited for developer tools, coding agents, and real-world engineering applications that require both reasoning depth and efficiency.

Qwen3.6-27B

Qwen3.6-27B is an open-weight 27B-parameter dense multimodal model from the Qwen3.6 series, designed to deliver flagship-level coding and agentic performance at a practical deployment scale. It supports both text and image inputs and introduces improvements in agentic coding, repository-level reasoning, and iterative development workflows. Despite its relatively compact size, it achieves state-of-the-art results on coding benchmarks, outperforming much larger models in tasks such as SWE-bench and terminal-based workflows.

It also provides strong reasoning and multimodal capabilities, along with features like thinking preservation to maintain context across interactions, making it well suited for developer tools, coding agents, and real-world engineering tasks.

Enjoy it.

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.

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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.

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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.