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New Models Added: Grok 4.20 Stable, GPT-Image-2, Qwen3.6 Series, Kimi K2.6, and Gemini Embedding 2

Date: 2026-04-23 / (1405-02-03)

Summary

We announce the addition of ten new models across five leading providers: X.AI's stable grok-4.20-reasoning and grok-4.20-non-reasoning, OpenAI's gpt-image-2 (the next-generation image generation model), Alibaba's qwen3.6-flash, qwen3.6-27b, qwen3.6-35b-a3b, and qwen3.6-max-preview vision-language models, Moonshot AI's kimi-k2.6 open-source agentic coding model, and Google's first multimodal embedding model gemini-embedding-2.

Details

X.AI (Grok)

The stable release of Grok 4.20 is now available with both reasoning and non-reasoning variants, moving beyond the previous beta versions while retaining the same 2M context window and industry-leading speed.

grok-4.20-reasoning

grok-4.20-reasoning is the stable release of X.AI's flagship Grok 4.20 reasoning model. It delivers industry-leading speed, agentic tool calling, the lowest hallucination rate on the market, and strict prompt adherence for consistently precise responses.

Feature	Details
Context window	2,000,000 tokens
Input pricing	$2.00 / 1M tokens
Cached input pricing	$0.20 / 1M tokens (90% cost reduction)
Output pricing	$6.00 / 1M tokens
Input pricing (above 200K)	$4.00 / 1M tokens
Cached input (above 200K)	$0.40 / 1M tokens
Output pricing (above 200K)	$12.00 / 1M tokens
Input modalities	Text, Image
Output modalities	Text
Supported endpoints	`v1/chat/completions`

Key Features:

Reasoning Mode: Extended thinking before responding for complex problem-solving
Massive Context: 2M token context window for extensive documents and conversations
Lowest Hallucination Rate: Industry-leading accuracy and truthfulness
Strict Prompt Adherence: Consistently precise responses
Function Calling & Structured Outputs: Full support for external tools and systems

grok-4.20-non-reasoning

grok-4.20-non-reasoning is the stable non-reasoning variant optimized for fast responses without extended thinking, ideal for high-throughput and latency-sensitive applications.

Feature	Details
Context window	2,000,000 tokens
Input pricing	$2.00 / 1M tokens
Cached input pricing	$0.20 / 1M tokens (90% cost reduction)
Output pricing	$6.00 / 1M tokens
Input pricing (above 200K)	$4.00 / 1M tokens
Cached input (above 200K)	$0.40 / 1M tokens
Output pricing (above 200K)	$12.00 / 1M tokens
Input modalities	Text, Image
Output modalities	Text
Supported endpoints	`v1/chat/completions`

Key Features:

Fast Inference: Optimized for quick responses without reasoning overhead
Massive Context: 2M token context window
High-Throughput: Ideal for latency-sensitive applications
Function Calling & Structured Outputs: Full tool support

OpenAI

gpt-image-2

gpt-image-2 is OpenAI's next-generation image generation model, delivering greater precision and control, stronger multilingual text rendering, stylistic sophistication and realism, enhanced real-world intelligence, and support for flexible aspect ratios. It is available on both v1/images/generations and v1/images/edits endpoints.

Feature	Details
Text input pricing	$5.00 / 1M tokens
Image input pricing	$8.00 / 1M tokens
Cached text input	$1.25 / 1M tokens
Cached image input	$2.00 / 1M tokens
Text output pricing	$10.00 / 1M tokens
Image output pricing	$30.00 / 1M tokens
Input modalities	Text, Image
Output modalities	Image, Text
Supported endpoints	`v1/images/generations`, `v1/images/edits`

Key Features:

Greater Precision & Control: Significantly improved image generation accuracy and adherence to prompt details
Multilingual Text Rendering: Strong performance across global scripts including Japanese, Arabic, Korean, Devanagari, Cyrillic, Bengali, Greek, Chinese, and Latin
Stylistic Sophistication & Realism: Higher fidelity across photography, illustration, manga, pixel art, and other visual styles
Flexible Aspect Ratios: Horizontal, Square, and Vertical formats for use cases from banners to mobile screens
Thinking Mode: Uses reasoning to research, transform inputs, and produce cohesive, end-to-end visual assets
Image Editing: Supports both generation and edit endpoints for complete image workflows

Aliases: gpt-image-2-2026-04-21

Alibaba (Qwen3.6 Series)

Four new models from the Qwen3.6 series are now available, delivering significant improvements in agentic coding, STEM reasoning, spatial intelligence, and object detection over the Qwen3.5 generation.

qwen3.6-flash

qwen3.6-flash is the Qwen3.6 native vision-language Flash model, featuring a 1M context window and significantly improved agentic coding, mathematical reasoning, and spatial intelligence over qwen3.5-flash.

Feature	Details
Context window	1,000,000 tokens (256K tier-one pricing)
Maximum output	64K tokens
Input pricing	$0.25 / 1M tokens
Input pricing (above 256K)	$1.00 / 1M tokens
Cache creation input	$0.3125 / 1M tokens
Cache creation input (above 256K)	$1.25 / 1M tokens
Cached input pricing	$0.025 / 1M tokens (90% cost reduction)
Cached input (above 256K)	$0.10 / 1M tokens
Output pricing	$1.50 / 1M tokens
Output pricing (above 128K)	$4.00 / 1M tokens
Input modalities	Text, Image, Video
Output modalities	Text
Supported endpoints	`v1/chat/completions`

Key Features:

Native Vision-Language: Processes text, images, and videos natively
1M Context Window: Extended context for long documents and conversations
Agentic Coding: Substantially outperforms Qwen3.5-Flash on code-agent benchmarks
Spatial Intelligence: Markedly improved object localization and detection
Deep Thinking: Optional reasoning mode via enable_thinking
Tool Support: Function calling, structured output, and web search

qwen3.6-27b

qwen3.6-27b is the Qwen3.6 27B native vision-language dense model, built on the 3.5-27B architecture with key improvements in agentic coding, STEM reasoning, and visual agent capabilities.

Feature	Details
Context window	256,000 tokens
Maximum output	64K tokens
Input pricing	$0.60 / 1M tokens
Cached input pricing	$0.06 / 1M tokens (90% cost reduction)
Output pricing	$3.60 / 1M tokens
Input modalities	Text, Image, Video
Output modalities	Text
Supported endpoints	`v1/chat/completions`

Key Features:

Native Vision-Language: Processes text, images, and videos natively
256K Context Window: Extended context for complex tasks
Enhanced STEM Reasoning: Improved mathematical and code reasoning skills
Visual Agents: Advances in video understanding, document OCR, and visual agent capabilities
Deep Thinking: Optional reasoning mode via enable_thinking

qwen3.6-35b-a3b

qwen3.6-35b-a3b is the Qwen3.6 35B-A3B native vision-language model, built on a hybrid architecture that integrates linear attention with a sparse mixture-of-experts framework for higher inference efficiency.

Feature	Details
Context window	256,000 tokens
Maximum output	64K tokens
Input pricing	$0.248 / 1M tokens
Cached input pricing	$0.025 / 1M tokens (90% cost reduction)
Output pricing	$1.485 / 1M tokens
Input modalities	Text, Image, Video
Output modalities	Text
Supported endpoints	`v1/chat/completions`

Key Features:

Hybrid Architecture: Linear attention combined with sparse MoE for higher efficiency
Improved Agentic Coding: Significantly better code-agent performance
Spatial Intelligence: Advances in object localization and detection
Cost-Effective: Very low pricing thanks to efficient sparse activation
Deep Thinking: Optional reasoning mode via enable_thinking

qwen3.6-max-preview

qwen3.6-max-preview is the largest and most capable variant in the Qwen3.6 series, available in preview with text-only capabilities. It features enhanced vibe coding, efficient coding agent execution, and upgraded long-tail knowledge retention.

Feature	Details
Context window	256,000 tokens (128K tier-one pricing)
Maximum output	64K tokens
Input pricing	$1.30 / 1M tokens
Input pricing (above 128K)	$2.00 / 1M tokens
Cache creation input	$1.625 / 1M tokens
Cache creation input (above 128K)	$2.50 / 1M tokens
Cached input pricing	$0.13 / 1M tokens (90% cost reduction)
Cached input (above 128K)	$0.20 / 1M tokens
Output pricing	$7.80 / 1M tokens
Output pricing (above 128K)	$12.00 / 1M tokens
Input modalities	Text
Output modalities	Text
Supported endpoints	`v1/chat/completions`

Key Features:

Largest Qwen3.6 Model: Most capable variant in the Qwen3.6 series
Enhanced Vibe Coding: Stronger front-end development and coding agent execution
Long-Tail Knowledge: Upgraded knowledge retention for specialized topics
256K Context Window: Extended context for complex tasks
Deep Thinking: Optional reasoning mode via enable_thinking

Moonshot AI

kimi-k2.6

kimi-k2.6 is Moonshot AI's latest open-source model featuring state-of-the-art coding, long-horizon execution, and agent swarm capabilities. It builds on K2.5 with stronger multi-step reliability, full-stack capability, and Document-to-Skills reusability.

Feature	Details
Input pricing	$0.95 / 1M tokens
Cached input pricing	$0.16 / 1M tokens
Output pricing	$4.00 / 1M tokens
Input modalities	Text, Image
Output modalities	Text
Supported endpoints	`v1/chat/completions`

Key Features:

SOTA Coding: Transforms prompts into Awwwards-level front-end interfaces with clean linework, animations, and interactivity
Full-Stack Generation: Creates complete working websites with authentication, interactions, and database operations from a single prompt
Long-Horizon Execution: Handles complex, multi-step tasks with higher reliability and fewer unnecessary changes
Agent Swarm: Coordinates multiple agents in parallel for search, research, analysis, long-form writing, and multi-format content generation
Document to Skills: Turn high-quality documents into reusable skills that apply across future tasks
Claw Groups (Preview): New multi-agent team workflow with a coordinator assigning tasks and managing dependencies
Kimi Slides: Production-ready presentation generation from prompts or multi-format inputs

Google

gemini-embedding-2

gemini-embedding-2 is Google's first multimodal embedding model in the Gemini API. It maps text, images, video, audio, and documents into a unified embedding space, enabling cross-modal search, classification, and clustering across over 100 languages.

Feature	Details
Text input pricing	$0.20 / 1M tokens
Cached text input	$0.02 / 1M tokens (90% cost reduction)
Image input pricing	$0.45 / 1M tokens
Audio input pricing	$6.50 / 1M tokens
Video input pricing	$12.00 / 1M tokens
Output pricing	$0.15 / 1M tokens
Input token limit	8,192 tokens
Output dimensions	Flexible (128 - 3072, default 3072, recommended 768/1536/3072)
Input modalities	Text, Image, Audio, Video, PDF
Output modalities	Embeddings
Supported endpoints	`v1/embeddings`, `v1beta/models/{model}:embedContent` (native Gemini)

Key Features:

First Multimodal Embedding: Unified embedding space across text, images, video, audio, and PDFs
Cross-Modal Search: Compare and retrieve content across modalities in the same vector space
100+ Languages: Broad multilingual support for international applications
Matryoshka Representation Learning (MRL): Flexible output dimensions (128–3072) without quality loss
Automatic Renormalization: Truncated dimensions (e.g., 768, 1536) are auto-normalized for accurate similarity
Embedding Aggregation: Single aggregated embedding for multi-part inputs (text + image, etc.)
Task Instructions: Include task types directly in prompts (e.g., task: search result | query: ...) for optimized performance
Supported Modalities: Text (8,192 tokens), Images (PNG/JPEG, max 6), Audio (MP3/WAV, max 180s), Video (MP4/MOV, max 120s, 32 frames), PDFs (max 6 pages)
Dual API Support: Available on both the OpenAI-compatible v1/embeddings endpoint and the native Gemini v1beta/models/{model}:embedContent endpoint

Aliases: gemini-embedding-2-preview

Pricing Summary

Model	Input ($/1M tokens)	Cached Input ($/1M tokens)	Output ($/1M tokens)
`grok-4.20-reasoning`	$2.00	$0.20	$6.00
`grok-4.20-non-reasoning`	$2.00	$0.20	$6.00
`gpt-image-2` (text)	$5.00	$1.25	$10.00
`gpt-image-2` (image)	$8.00	$2.00	$30.00
`qwen3.6-flash`	$0.25	$0.025	$1.50
`qwen3.6-27b`	$0.60	$0.06	$3.60
`qwen3.6-35b-a3b`	$0.248	$0.025	$1.485
`qwen3.6-max-preview`	$1.30	$0.13	$7.80
`kimi-k2.6`	$0.95	$0.16	$4.00
`gemini-embedding-2` (text)	$0.20	$0.02	$0.15

API Request/Response Examples

Grok 4.20 Reasoning Example

bash

curl https://api.avalai.ir/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AVALAI_API_KEY" \
  -d '{
    "model": "grok-4.20-reasoning",
    "messages": [
      {
        "role": "user",
        "content": "Design a fault-tolerant distributed system architecture for a global payment platform."
      }
    ],
    "max_tokens": 4096
  }'

Example Response:

json

{
  "id": "chatcmpl-grok420-abc123",
  "created": 1776321600,
  "model": "grok-4.20-reasoning",
  "object": "chat.completion",
  "system_fingerprint": null,
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "message": {
        "content": "A fault-tolerant global payment platform should adopt a multi-region active-active architecture...",
        "role": "assistant",
        "thinking_blocks": [
          {
            "type": "thinking",
            "thinking": "Let me break down the requirements: global reach, fault tolerance, payment-grade reliability..."
          }
        ],
        "annotations": []
      }
    }
  ],
  "usage": {
    "completion_tokens": 1420,
    "prompt_tokens": 25,
    "total_tokens": 1445,
    "prompt_tokens_details": {
      "cached_tokens": 0,
      "text_tokens": 25
    }
  },
  "estimated_cost": {
    "unit": "0.0085700000",
    "irt": 982.13,
    "exchange_rate": 114600
  }
}

GPT-Image-2 Generation Example

bash

curl https://api.avalai.ir/v1/images/generations \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AVALAI_API_KEY" \
  -d '{
    "model": "gpt-image-2",
    "prompt": "A modernist editorial poster titled \"Typography\" that celebrates global scripts including Japanese, Arabic, Korean, and Latin letterforms, in red, blue, and black tones.",
    "size": "1024x1024",
    "n": 1
  }'

Example Response:

json

{
  "created": 1776321700,
  "data": [
    {
      "b64_json": "iVBORw0KGgoAAAANSUhEUgAABAAAAAQACAIAAADwf7zU...[TRUNCATED]",
      "revised_prompt": "A modernist editorial poster..."
    }
  ],
  "usage": {
    "input_tokens": 32,
    "input_tokens_details": {
      "text_tokens": 32,
      "image_tokens": 0
    },
    "output_tokens": 4160,
    "total_tokens": 4192
  },
  "estimated_cost": {
    "unit": "0.1249600000",
    "irt": 14320.22,
    "exchange_rate": 114600
  }
}

GPT-Image-2 Edit Example

bash

curl https://api.avalai.ir/v1/images/edits \
  -H "Authorization: Bearer $AVALAI_API_KEY" \
  -F "model=gpt-image-2" \
  -F "image=@input.png" \
  -F "prompt=Add a gold Art Deco frame and soft sunrise lighting" \
  -F "size=1024x1024"

Qwen3.6-Flash with Thinking Mode

bash

curl https://api.avalai.ir/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AVALAI_API_KEY" \
  -d '{
    "model": "qwen3.6-flash",
    "messages": [
      {
        "role": "user",
        "content": "Implement a binary search tree in Python with insert, delete, and in-order traversal methods."
      }
    ],
    "extra_body": {
      "enable_thinking": true
    },
    "stream": true
  }'

Qwen3.6-Max-Preview Example

bash

curl https://api.avalai.ir/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AVALAI_API_KEY" \
  -d '{
    "model": "qwen3.6-max-preview",
    "messages": [
      {
        "role": "user",
        "content": "Build a polished Next.js landing page for a SaaS product with hero, features, testimonials, and pricing sections."
      }
    ],
    "max_tokens": 8192
  }'

Kimi K2.6 Agent Swarm Example

bash

curl https://api.avalai.ir/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AVALAI_API_KEY" \
  -d '{
    "model": "kimi-k2.6",
    "messages": [
      {
        "role": "user",
        "content": "Build a full-stack task management web application with user authentication, real-time updates, and a clean modern UI."
      }
    ],
    "max_tokens": 8192
  }'

Gemini Embedding 2 Example (Text)

bash

curl https://api.avalai.ir/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AVALAI_API_KEY" \
  -d '{
    "model": "gemini-embedding-2",
    "input": "task: search result | query: What is the meaning of life?",
    "dimensions": 768
  }'

Example Response:

json

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [
        0.0123,
        -0.0456,
        0.0789,
        "...[TRUNCATED 768 values]"
      ]
    }
  ],
  "model": "gemini-embedding-2",
  "usage": {
    "prompt_tokens": 14,
    "total_tokens": 14
  },
  "estimated_cost": {
    "unit": "0.0000028000",
    "irt": 0.32,
    "exchange_rate": 114600
  }
}

Gemini Embedding 2 Multimodal via OpenAI-Compatible Endpoint

The v1/embeddings endpoint accepts multimodal inputs (images, audio, video) as data: URIs in the input array:

bash

# Text + Image (base64 data URI) via OpenAI-compatible endpoint
curl -i "https://api.avalai.ir/v1/embeddings" \
  -H "Authorization: Bearer $AVALAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "input": [
      "The food was delicious and the waiter...",
      "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAgAAAAIAQMAAAD+wSzIAAAABlBMVEX///+/v7+jQ3Y5AAAADklEQVQI12P4AIX8EAgALgAD/aNpbtEAAAAASUVORK5CYII"
    ],
    "model": "gemini-embedding-2"
  }'

bash

# Text + Audio (base64 data URI) via OpenAI-compatible endpoint
AUDIO_PATH="./speech.mp3"
AUDIO_BASE64="$(base64 -i "$AUDIO_PATH" | tr -d '\n')"

cat >/tmp/embeddings-audio.json <<EOF
{
  "input": [
    "The food was delicious and the waiter...",
    "data:audio/mpeg;base64,${AUDIO_BASE64}"
  ],
  "model": "gemini-embedding-2"
}
EOF

curl -i "https://api.avalai.ir/v1/embeddings" \
  -H "Authorization: Bearer ${AVALAI_API_KEY}" \
  -H "Content-Type: application/json" \
  --data-binary @/tmp/embeddings-audio.json

bash

# Text + Video (base64 data URI) via OpenAI-compatible endpoint
VIDEO_PATH="./sample_video.mp4"
VIDEO_BASE64="$(base64 -i "$VIDEO_PATH" | tr -d '\n')"

cat >/tmp/embeddings-video.json <<EOF
{
  "input": [
    "The food was delicious and the waiter...",
    "data:video/mp4;base64,${VIDEO_BASE64}"
  ],
  "model": "gemini-embedding-2"
}
EOF

curl -i "https://api.avalai.ir/v1/embeddings" \
  -H "Authorization: Bearer ${AVALAI_API_KEY}" \
  -H "Content-Type: application/json" \
  --data-binary @/tmp/embeddings-video.json

Gemini Embedding 2 via Native Gemini API (`v1beta`)

gemini-embedding-2 is also available via the native Gemini v1beta/models/{model}:embedContent endpoint, which accepts inline_data parts for images, audio, video, and PDFs.

Text embedding (native API):

bash

curl "https://api.avalai.ir/v1beta/models/gemini-embedding-2:embedContent" \
  -H "Content-Type: application/json" \
  -H "x-goog-api-key: ${AVALAI_API_KEY}" \
  -d '{
    "model": "gemini-embedding-2",
    "content": {
      "parts": [{
        "text": "What is the meaning of life?"
      }]
    }
  }'

Image embedding (native API):

bash

IMG_PATH="./sample_image.jpg"
# macOS: IMG_BASE64="$(base64 -i "$IMG_PATH" | tr -d '\n')"
IMG_BASE64=$(base64 -w0 "${IMG_PATH}")

cat >/tmp/payload.json <<EOF
{
  "content": {
    "parts": [
      {
        "inline_data": {
          "mime_type": "image/png",
          "data": "${IMG_BASE64}"
        }
      }
    ]
  }
}
EOF

curl -sS "https://api.avalai.ir/v1beta/models/gemini-embedding-2:embedContent" \
  -H "Content-Type: application/json" \
  -H "x-goog-api-key: ${AVALAI_API_KEY}" \
  --data-binary @/tmp/payload.json

Audio embedding (native API):

bash

AUDIO_PATH="./speech.mp3"
AUDIO_BASE64="$(base64 -i "$AUDIO_PATH" | tr -d '\n')"

curl -sS "https://api.avalai.ir/v1beta/models/gemini-embedding-2:embedContent" \
  -H "Content-Type: application/json" \
  -H "x-goog-api-key: ${AVALAI_API_KEY}" \
  -d '{
    "content": {
      "parts": [{
        "inline_data": {
          "mime_type": "audio/mpeg",
          "data": "'"${AUDIO_BASE64}"'"
        }
      }]
    }
  }'

Video embedding (native API):

bash

VIDEO_PATH="./sample_video.mp4"
VIDEO_BASE64="$(base64 -i "$VIDEO_PATH" | tr -d '\n')"

curl -sS "https://api.avalai.ir/v1beta/models/gemini-embedding-2:embedContent" \
  -H "Content-Type: application/json" \
  -H "x-goog-api-key: ${AVALAI_API_KEY}" \
  --data-binary @- <<EOF
{
  "content": {
    "parts": [
      {
        "inline_data": {
          "mime_type": "video/mp4",
          "data": "${VIDEO_BASE64}"
        }
      }
    ]
  }
}
EOF

Text + Image aggregation (native API):

bash

curl "https://api.avalai.ir/v1beta/models/gemini-embedding-2:embedContent" \
  -H "Content-Type: application/json" \
  -H "x-goog-api-key: $AVALAI_API_KEY" \
  -d '{
    "content": {
      "parts": [
        {"text": "An image of a dog"},
        {
          "inline_data": {
            "mime_type": "image/png",
            "data": "iVBORw0KGgo...[TRUNCATED]"
          }
        }
      ]
    }
  }'

Note

When multiple parts are provided in a single request, gemini-embedding-2 returns a single aggregated embedding. Use the Batch API or multiple requests if you need separate embeddings per input.

SDK Usage Examples

Chat Completions (Grok, Qwen, Kimi)

BashPythonJavaScript

bash

curl https://api.avalai.ir/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AVALAI_API_KEY" \
  -d '{
    "model": "kimi-k2.6",
    "messages": [
      {
        "role": "user",
        "content": "Build a minimalist personal portfolio website with a hero section, projects grid, and contact form."
      }
    ]
  }'

python

from openai import OpenAI

client = OpenAI(api_key="your-avalai-api-key", base_url="https://api.avalai.ir/v1")

# Using Kimi K2.6 for full-stack coding
response = client.chat.completions.create(
    model="kimi-k2.6",
    messages=[
        {
            "role": "user",
            "content": "Build a minimalist personal portfolio website with a hero section, projects grid, and contact form.",
        }
    ],
)

print(response.choices[0].message.content)

# Using Grok 4.20 Reasoning
response = client.chat.completions.create(
    model="grok-4.20-reasoning",
    messages=[
        {
            "role": "user",
            "content": "Design a fault-tolerant distributed system architecture.",
        }
    ],
    max_tokens=4096,
)

print(response.choices[0].message.content)

# Using Qwen3.6-Flash with thinking mode
response = client.chat.completions.create(
    model="qwen3.6-flash",
    messages=[
        {
            "role": "user",
            "content": "Explain quantum entanglement with practical implications.",
        }
    ],
    extra_body={"enable_thinking": True},
)

print(response.choices[0].message.content)

javascript

import { OpenAI } from "openai";

const client = new OpenAI({
  apiKey: process.env.AVALAI_API_KEY,
  baseURL: "https://api.avalai.ir/v1",
});

// Using Kimi K2.6
const kimiResponse = await client.chat.completions.create({
  model: "kimi-k2.6",
  messages: [
    {
      role: "user",
      content: "Build a minimalist personal portfolio website with a hero section, projects grid, and contact form.",
    },
  ],
});

console.log(kimiResponse.choices[0].message.content);

// Using Qwen3.6-Max-Preview
const qwenResponse = await client.chat.completions.create({
  model: "qwen3.6-max-preview",
  messages: [
    {
      role: "user",
      content: "Build a polished Next.js landing page for a SaaS product.",
    },
  ],
  max_tokens: 8192,
});

console.log(qwenResponse.choices[0].message.content);

Image Generation (GPT-Image-2)

BashPythonJavaScript

bash

curl https://api.avalai.ir/v1/images/generations \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AVALAI_API_KEY" \
  -d '{
    "model": "gpt-image-2",
    "prompt": "A Bauhaus-inspired poster with bold typography and geometric shapes in red, blue, and black.",
    "size": "1024x1024",
    "n": 1
  }'

python

from openai import OpenAI

client = OpenAI(api_key="your-avalai-api-key", base_url="https://api.avalai.ir/v1")

response = client.images.generate(
    model="gpt-image-2",
    prompt="A Bauhaus-inspired poster with bold typography and geometric shapes in red, blue, and black.",
    size="1024x1024",
    n=1,
)

print(response.data[0].b64_json[:100] + "...")

javascript

import { OpenAI } from "openai";

const client = new OpenAI({
  apiKey: process.env.AVALAI_API_KEY,
  baseURL: "https://api.avalai.ir/v1",
});

const response = await client.images.generate({
  model: "gpt-image-2",
  prompt: "A Bauhaus-inspired poster with bold typography and geometric shapes in red, blue, and black.",
  size: "1024x1024",
  n: 1,
});

console.log(response.data[0].b64_json.slice(0, 100) + "...");

Embeddings (Gemini Embedding 2)

BashPythonJavaScript

bash

curl https://api.avalai.ir/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AVALAI_API_KEY" \
  -d '{
    "model": "gemini-embedding-2",
    "input": "task: search result | query: What is the meaning of life?",
    "dimensions": 768
  }'

python

from openai import OpenAI

client = OpenAI(api_key="your-avalai-api-key", base_url="https://api.avalai.ir/v1")

response = client.embeddings.create(
    model="gemini-embedding-2",
    input="task: search result | query: What is the meaning of life?",
    dimensions=768,
)

print(f"Embedding length: {len(response.data[0].embedding)}")
print(f"First 5 values: {response.data[0].embedding[:5]}")

javascript

import { OpenAI } from "openai";

const client = new OpenAI({
  apiKey: process.env.AVALAI_API_KEY,
  baseURL: "https://api.avalai.ir/v1",
});

const response = await client.embeddings.create({
  model: "gemini-embedding-2",
  input: "task: search result | query: What is the meaning of life?",
  dimensions: 768,
});

console.log(`Embedding length: ${response.data[0].embedding.length}`);
console.log(`First 5 values:`, response.data[0].embedding.slice(0, 5));

New Models Added: Grok 4.20 Stable, GPT-Image-2, Qwen3.6 Series, Kimi K2.6, and Gemini Embedding 2 ​

Summary ​

Details ​

X.AI (Grok) ​

grok-4.20-reasoning ​

grok-4.20-non-reasoning ​

OpenAI ​

gpt-image-2 ​

Alibaba (Qwen3.6 Series) ​

qwen3.6-flash ​

qwen3.6-27b ​

qwen3.6-35b-a3b ​

qwen3.6-max-preview ​

Moonshot AI ​

kimi-k2.6 ​

Google ​

gemini-embedding-2 ​

Pricing Summary ​

API Request/Response Examples ​

Grok 4.20 Reasoning Example ​

GPT-Image-2 Generation Example ​

GPT-Image-2 Edit Example ​

Qwen3.6-Flash with Thinking Mode ​

Qwen3.6-Max-Preview Example ​

Kimi K2.6 Agent Swarm Example ​

Gemini Embedding 2 Example (Text) ​

Gemini Embedding 2 Multimodal via OpenAI-Compatible Endpoint ​

Gemini Embedding 2 via Native Gemini API (v1beta) ​

SDK Usage Examples ​

Chat Completions (Grok, Qwen, Kimi) ​

Image Generation (GPT-Image-2) ​

Embeddings (Gemini Embedding 2) ​

Documentation Links ​

New Models Added: Grok 4.20 Stable, GPT-Image-2, Qwen3.6 Series, Kimi K2.6, and Gemini Embedding 2

Summary

Details

X.AI (Grok)

grok-4.20-reasoning

grok-4.20-non-reasoning

OpenAI

gpt-image-2

Alibaba (Qwen3.6 Series)

qwen3.6-flash

qwen3.6-27b

qwen3.6-35b-a3b

qwen3.6-max-preview

Moonshot AI

kimi-k2.6

Google

gemini-embedding-2

Pricing Summary

API Request/Response Examples

Grok 4.20 Reasoning Example

GPT-Image-2 Generation Example

GPT-Image-2 Edit Example

Qwen3.6-Flash with Thinking Mode

Qwen3.6-Max-Preview Example

Kimi K2.6 Agent Swarm Example

Gemini Embedding 2 Example (Text)

Gemini Embedding 2 Multimodal via OpenAI-Compatible Endpoint

Gemini Embedding 2 via Native Gemini API (`v1beta`)

SDK Usage Examples

Chat Completions (Grok, Qwen, Kimi)

Image Generation (GPT-Image-2)

Embeddings (Gemini Embedding 2)

Documentation Links