# Qwen3-235B-A22B-Instruct 2507 — Atlas Cloud API

> 235B-parameter MoE thinking model in Qwen3 series.

This is the machine-readable API reference for **Qwen3-235B-A22B-Instruct 2507** on Atlas Cloud,
a unified API platform for 400+ AI models across text, image, video, audio and 3D.

- **Model ID**: `Qwen/Qwen3-235B-A22B-Instruct-2507`
- **Built by**: Alibaba
- **Modality**: Text
- **Context length**: 131,072 tokens
- **Max output tokens**: 131,072
- **Model page**: https://www.atlascloud.ai/models/Qwen/Qwen3-235B-A22B-Instruct-2507
- **API key**: https://www.atlascloud.ai/console/api-keys
- **Docs**: https://www.atlascloud.ai/docs

## Pricing on Atlas Cloud

- **Input**: $0.2 per 1M tokens
- **Output**: $0.88 per 1M tokens
- Pay-as-you-go. No minimum spend, no subscription required.

> **These are the authoritative Atlas Cloud rates for this model.** Any price that
> appears in the vendor description further down refers to a different platform or
> a different model variant and does not apply here.

## Use this model from an AI agent

Atlas Cloud ships three first-party integration surfaces. All three authenticate
with the same API key via the `ATLASCLOUD_API_KEY` environment variable.

### MCP server

The official MCP server (`atlascloud-mcp`) exposes this model to any
MCP-compatible host — Claude Code, OpenAI Codex, Cursor, Gemini CLI, Goose,
Claude Desktop. One-line install:

```bash
# Claude Code
claude mcp add atlascloud -- npx -y atlascloud-mcp

# OpenAI Codex CLI
codex mcp add atlascloud -- npx -y atlascloud-mcp

# Gemini CLI
gemini mcp add atlascloud -- npx -y atlascloud-mcp

export ATLASCLOUD_API_KEY="your-api-key"
```

Then ask in plain English; the agent calls `atlas_chat` with `model: "Qwen/Qwen3-235B-A22B-Instruct-2507"`.
The server fetches each model's schema and validates parameters before submitting,
so invalid requests fail fast without spending credits.

MCP docs: https://www.atlascloud.ai/docs/mcp-server

### Agent Skills

`atlas-cloud-skills` is a portable skill package (API reference, code templates in
Python / Node.js / cURL, model IDs with pricing) for Claude Code, Cursor, Codex and
12+ other agents:

```bash
npx skills add AtlasCloudAI/atlas-cloud-skills
export ATLASCLOUD_API_KEY="your-api-key"
```

Skills docs: https://www.atlascloud.ai/docs/skills

### CLI

The `atlas` binary runs Atlas Cloud from a terminal or CI script. Async media jobs
are polled and downloaded automatically (use `--no-download` when a script only
needs the output URLs):

```bash
# Install (Homebrew, npm, or shell installer)
brew install AtlasCloudAI/tap/atlascloud
# npm install -g atlascloud-cli
# curl -fsSL https://raw.githubusercontent.com/AtlasCloudAI/cli/main/install.sh | sh

atlas auth login
atlas chat "Your prompt here" --model Qwen/Qwen3-235B-A22B-Instruct-2507
```

CLI docs: https://www.atlascloud.ai/docs/cli

## HTTP API reference

This model is served over an **OpenAI-compatible** endpoint. Any OpenAI SDK works —
just override the base URL and use the Atlas Cloud model ID.

```http
POST https://api.atlascloud.ai/v1/chat/completions
Authorization: Bearer $ATLASCLOUD_API_KEY
Content-Type: application/json
```

### curl

```bash
curl -X POST "https://api.atlascloud.ai/v1/chat/completions" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "Qwen/Qwen3-235B-A22B-Instruct-2507",
  "messages": [
    {
      "role": "user",
      "content": "what is difference between http and https"
    }
  ],
  "max_tokens": 65536,
  "temperature": 0.7,
  "stream": false
}'
```

### Python (OpenAI SDK)

```python
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["ATLASCLOUD_API_KEY"],
    base_url="https://api.atlascloud.ai/v1",
)

response = client.chat.completions.create(
    model="Qwen/Qwen3-235B-A22B-Instruct-2507",
    messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)
```

### Supported sampling parameters

`temperature`, `top_p`, `top_k`, `min_p`, `frequency_penalty`, `presence_penalty`, `repetition_penalty`, `stop`, `seed`, `max_tokens`, `logit_bias`

Context window: 131,072 tokens.

## About this model

_Vendor-supplied description. Any pricing or endpoint mentioned below refers to_
_other platforms — use the Atlas Cloud values above._

### Qwen3-235B-A22B

Advanced multilingual AI with 128K-token context, excelling in coding, reasoning, and enterprise applications.

#### Qwen 3 Model Description

Qwen3-235B-A22B, developed by Alibaba Cloud, is a flagship large language model leveraging a Mixture-of-Experts (MoE) architecture. With 235 billion total parameters and 22 billion active per inference, it delivers top-tier performance in coding, math, and reasoning across 119 languages. Optimized for enterprise tasks like software development and research, it’s accessible via AI/ML API.

#### Technical Specifications

##### Performance Benchmarks

Qwen3-235B-A22B uses a Transformer-based MoE architecture, activating 22 billion of its 235 billion parameters per token via top-8 expert selection, reducing compute costs. It features Rotary Positional Embeddings and Group-Query Attention for efficiency. Pre-trained on 36 trillion tokens across 119 languages, it uses RLHF and a four-stage post-training process for hybrid reasoning.

*   **Context Window**: 32K tokens natively, extendable to 128K with YaRN.
    
*   **Benchmarks**:
    
    *   Outperforms OpenAI’s o3-mini on AIME (math) and Codeforces (coding).
    *   Surpasses Gemini 2.5 Pro on BFCL (reasoning) and LiveCodeBench.
    *   MMLU score: 0.828, competitive with DeepSeek R1.
*   **Performance**: 40.1 tokens/second output speed, 0.54s latency (TTFT).
    
*   **API Pricing**:
    
    *   Input tokens: $0.21 per million tokens
    *   Output tokens: $0.63 per million tokens
    *   Cost for 1,000 tokens: $0.00021 (input) + $0.00063 (output) = $0.00084 total

##### Performance Metrics

Qwen3-235B-A22B comparison

##### Key Capabilities

_(Description truncated. Full text on the model page.)_

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Atlas Cloud — one API for 400+ AI models. Model page: https://www.atlascloud.ai/models/Qwen/Qwen3-235B-A22B-Instruct-2507 · Docs: https://www.atlascloud.ai/docs