Qwen3 Coder 480B A35B (free) - API Pricing & Benchmarks
On OpenRouter
Qwen: Qwen3 Coder 480B A35B (free)
Qwen3-Coder-480B-A35B-Instruct is a Mixture-of-Experts (MoE) code generation model developed by the Qwen team. It is optimized for agentic coding tasks such as function calling, tool use, and long-context reasoning over repositories. The model features 480 billion total parameters, with 35 billion active per forward pass (8 out of 160 experts).
Pricing for the Alibaba endpoints varies by context length. Once a request is greater than 128k input tokens, the higher pricing is used.
Modalities
Price
Free
Context
1M
Weekly Tokens
35.1B
Released
Jul 22, 2025
Providers for Qwen3 Coder 480B A35B (free)
OpenRouter routes requests to the best providers that are able to handle your prompt size and parameters, with fallbacks to maximize uptime.
Pricing
Performance for Qwen3 Coder 480B A35B (free)
Compare different providers across OpenRouter
Throughput
Latency
E2E Latency
Tool Call Error Rate
Structured Output Error Rate
Effective Pricing for Qwen3 Coder 480B A35B (free)
Actual cost per million tokens across providers over the past hour
Weighted Average
Weighted Avg Input Price
$0.0000
/M tokens
Weighted Avg Output Price
$0.0000
/M tokens
Input Price / 1M tokens (7 days)
Output Price / 1M tokens (7 days)
No effective pricing data available for this model yet.
Benchmarks for Qwen3 Coder 480B A35B (free)
Performance metrics and benchmarksOverall intelligence score combining multiple benchmarks
[
24.8
Artificial Analysis
Intelligence Index
Better than 42% of models compared
Composite coding capability score
24.6
Artificial Analysis
Coding Index
Better than 52% of models compared
Composite agentic capability score
18.3
Artificial Analysis
Agentic Index
Better than 33% of models compared
Reasoning
GPQA Diamond
Graduate-level scientific reasoning
61.8%
HLE
Humanity's Last Exam
4.4%
IFBench
Instruction-following benchmark
40.5%
τ²-Bench Telecom
Conversational AI agents in dual-control scenarios
43.6%
AA-LCR
Long context reasoning evaluation
42.3%
GDPval-AA
Economically valuable tasks
0.4%
CritPt
Research-level physics reasoning
0.0%
Coding
SciCode
Python programming for scientific computing
35.9%
Terminal-Bench Hard
Agentic coding & terminal use
18.9%
Knowledge
AA-Omniscience Accuracy
Proportion of correctly answered questions
14.7%
56.7%
Metrics sourced from Artificial Analysis
Recent activity on Qwen3 Coder 480B A35B (free)
Total usage per day on OpenRouter
Prompt
0
Completion
0
Prompt tokens measure input size. Reasoning tokens show internal thinking before a response. Completion tokens reflect total output length.
Uptime stats for Qwen3 Coder 480B A35B (free)
Uptime stats for Qwen3 Coder 480B A35B (free) on the only provider
When an error occurs in an upstream provider, we can recover by routing to another healthy provider, if your request filters allow it. You can access uptime data programmatically through the Endpoints API
Learn more about our load balancing and customization options.
Get your API key
Create an API key from your OpenRouter dashboard and set it as an environment variable:
export OPENROUTER_API_KEY=sk-or-v1-...
2
Make your first request
Use qwen/qwen3-coder:free with the OpenRouter API:
OpenRouter provides an OpenAI-compatible completion API to 400+ models & providers that you can call directly, or using the OpenAI SDK. Additionally, some third-party SDKs are available.
In the examples below, the OpenRouter-specific headers are optional. Setting them allows your app to appear on the OpenRouter leaderboards.
import { OpenRouter } from "@openrouter/sdk";
const openrouter = new OpenRouter({
apiKey: "<OPENROUTER_API_KEY>"
});
const response = await openrouter.chat.send({
model: "qwen/qwen3-coder:free",
messages: [
{
"role": "user",
"content": "What is the meaning of life?"
}
]
});
console.log(response.choices[0].message.content);
Using third-party SDKs
For information about using third-party SDKs and frameworks with OpenRouter, please see our frameworks documentation.
3
Enable streaming
Add "stream": true to your request body to receive responses as server-sent events:
curl -N https://openrouter.ai/api/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENROUTER_API_KEY" \
-d '{
"model": "qwen/qwen3-coder:free",
"stream": true,
"messages": [
{"role": "user", "content": "Hello"}
]
}'
Endpoint
Sends a request for a model response for the given chat conversation. Supports both streaming and non-streaming modes.
POST https://openrouter.ai/api/v1/chat/completions
Authorization Bearer $OPENROUTER_API_KEY
Content-Type application/json
HTTP-Referer optional — your site URL, for rankings
X-Title optional — your site name, for rankings
Model qwen/qwen3-coder:free
Creates a streaming or non-streaming response using the OpenAI Responses API format.
POST https://openrouter.ai/api/v1/responses
Authorization Bearer $OPENROUTER_API_KEY
Content-Type application/json
HTTP-Referer optional — your site URL, for rankings
X-Title optional — your site name, for rankings
Model qwen/qwen3-coder:free
Creates a message using the Anthropic Messages API format. Supports text, images, PDFs, tools, and extended thinking.
POST https://openrouter.ai/api/v1/messages
Authorization Bearer $OPENROUTER_API_KEY
Content-Type application/json
HTTP-Referer optional — your site URL, for rankings
X-Title optional — your site name, for rankings
Model qwen/qwen3-coder:free
Parameters
| Name | Type | Default | Description |
|---|---|---|---|
max_tokens |
integer | — | This sets the upper limit for the number of tokens the model can generate in response. |
temperature |
float | 1 |
This setting influences the variety in the model's responses. |
top_p |
float | 1 |
This setting limits the model's choices to a percentage of likely tokens: only the top tokens whose probabilities add up to P. |
stop |
array | — | Stop generation immediately if the model encounter any token specified in the stop array. |
frequency_penalty |
float | 0 |
This setting aims to control the repetition of tokens based on how often they appear in the input. |
presence_penalty |
float | 0 |
Adjusts how often the model repeats specific tokens already used in the input. |
top_k |
integer | 0 |
This limits the model's choice of tokens at each step, making it choose from a smaller set. |
tools |
array | — | Tool calling parameter, following OpenAI's tool calling request shape. |
tool_choice |
string or object | — | Controls which (if any) tool is called by the model. |
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[
Qwen3.7-Plus is a cost-effective model in Alibaba's Qwen3.7 series. It supports text and image input with text output, building on the series' text capabilities with a comprehensive upgrade to its vision-language abilities while retaining full-stack, agent-level intelligence for coding, tool use, and productivity workflows. Its distinguishing trait is multi-modal interactive hybrid agent capability: it can perceive real-world scenes, read screens and interact with GUIs, generate code from visual references, and perform end-to-end navigation within mobile apps.
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Qwen3.7-Max is the flagship model in Alibaba's Qwen3.7 series. It supports text input and output and is designed for agent-centric workloads, with particular strengths in coding, office and productivity tasks, and long-horizon autonomous execution. The model offers notable gains in coding and agentic performance over prior Qwen generations and supports explicit prompt caching for efficient repeated context use.
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Qwen3-ASR-Flash is Alibaba's automatic speech recognition service, built on the Qwen3-Omni foundation and trained on tens of millions of hours of multimodal speech data. The model handles 11 languages — including Chinese (with Cantonese, Sichuanese, Minnan, and Wu dialects), English, Arabic, French, German, Spanish, Italian, Portuguese, Russian, Japanese, and Korean — with automatic language detection so no manual configuration is needed for mixed-language audio.
The model is designed for difficult acoustic conditions: it transcribes lyrics over background music, handles noisy and far-field recordings, filters silence and non-speech audio, and accepts arbitrary context text (names, jargon, domain terminology) to bias recognition toward specific vocabulary.
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[
Qwen3.5 Plus (April 2026) is a large-scale multimodal language model from Alibaba. It accepts text, image, and video input and produces text output, with a 1M token context window. This is an updated version of Qwen3.5 Plus with tiered pricing above 256K tokens.
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[
Qwen3.6 Flash is a fast, efficient language model from Alibaba's Qwen 3.6 series. It supports text, image, and video input with a 1M token context window. Tiered pricing kicks in above 256K tokens. Prompt caching is supported, with both explicit cache read and cache creation pricing.
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[
Qwen3.6-35B-A3B is an open-weight multimodal model from Alibaba Cloud with 35 billion total parameters and 3 billion active parameters per token. It uses a hybrid sparse mixture-of-experts architecture combining Gated DeltaNet linear attention with standard gated attention layers, enabling efficient inference at a fraction of the compute cost. The model supports a 262K token native context window (extensible to 1M via YaRN) and accepts text, image, and video inputs. It includes integrated thinking mode with reasoning traces preserved across multi-turn conversations, function calling, and structured output. Released under the Apache 2.0 license.
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[
Qwen3.6-Max-Preview is a proprietary frontier model from Alibaba Cloud built on a sparse mixture-of-experts architecture with approximately 1 trillion total parameters. It is optimized for agentic coding, tool use, and long-context reasoning, supporting a 262K token context window. The model includes an integrated thinking mode that preserves reasoning traces across multi-turn conversations and supports structured output and function calling. Access is available exclusively through the Alibaba Cloud Model Studio and Qwen Studio APIs; no open weights are provided.
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[
Qwen3.6 27B is a dense 27-billion-parameter language model from the Qwen Team at Alibaba, released in April 2026. It features hybrid multimodal capabilities — accepting text, image, and video inputs — and supports a 262,144-token context window.
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[
Qwen 3.6 Plus builds on a hybrid architecture that combines efficient linear attention with sparse mixture-of-experts routing, enabling strong scalability and high-performance inference. Compared to the 3.5 series, it delivers major gains in agentic coding, front-end development, and overall reasoning, with a significantly improved “vibe coding” experience. The model excels at complex tasks such as 3D scenes, games, and repository-level problem solving, achieving a 78.8 score on SWE-bench Verified. It represents a substantial leap in both pure-text and multimodal capabilities, performing at the level of leading state-of-the-art models.
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[
Qwen 3.6 Plus Preview is the next-generation evolution of the Qwen Plus series, featuring an advanced hybrid architecture that improves efficiency and scalability. It delivers stronger reasoning and more reliable agentic behavior compared to the 3.5 series. In benchmarks, it performs at or above leading state-of-the-art models. Designed as a flagship preview, it excels in agentic coding, front-end development, and complex problem-solving.
Note: The model collects prompt and completion data that can be used to improve the model.
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[
Qwen3.5-9B is a multimodal foundation model from the Qwen3.5 family, designed to deliver strong reasoning, coding, and visual understanding in an efficient 9B-parameter architecture. It uses a unified vision-language design with early fusion of multimodal tokens, allowing the model to process and reason across text and images within the same context.
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[
The Qwen3.5 Series 35B-A3B is a native vision-language model designed with a hybrid architecture that integrates linear attention mechanisms and a sparse mixture-of-experts model, achieving higher inference efficiency. Its overall performance is comparable to that of the Qwen3.5-27B.
[
The Qwen3.5 27B native vision-language Dense model incorporates a linear attention mechanism, delivering fast response times while balancing inference speed and performance. Its overall capabilities are comparable to those of the Qwen3.5-122B-A10B.
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The Qwen3.5 122B-A10B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. In terms of overall performance, this model is second only to Qwen3.5-397B-A17B. Its text capabilities significantly outperform those of Qwen3-235B-2507, and its visual capabilities surpass those of Qwen3-VL-235B.
[
The Qwen3.5 native vision-language Flash models are built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. Compared to the 3 series, these models deliver a leap forward in performance for both pure text and multimodal tasks, offering fast response times while balancing inference speed and overall performance.
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[
The Qwen3.5 native vision-language series Plus models are built on a hybrid architecture that integrates linear attention mechanisms with sparse mixture-of-experts models, achieving higher inference efficiency. In a variety of task evaluations, the 3.5 series consistently demonstrates performance on par with state-of-the-art leading models. Compared to the 3 series, these models show a leap forward in both pure-text and multimodal capabilities.
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[
The Qwen3.5 series 397B-A17B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. It delivers state-of-the-art performance comparable to leading-edge models across a wide range of tasks, including language understanding, logical reasoning, code generation, agent-based tasks, image understanding, video understanding, and graphical user interface (GUI) interactions. With its robust code-generation and agent capabilities, the model exhibits strong generalization across diverse agent.
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Qwen3-Coder-Next is an open-weight causal language model optimized for coding agents and local development workflows. It uses a sparse MoE design with 80B total parameters and only 3B activated per token, delivering performance comparable to models with 10 to 20x higher active compute, which makes it well suited for cost-sensitive, always-on agent deployment.
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The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining.
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The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining.
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[
The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining.
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[
Qwen3-VL-32B-Instruct is a large-scale multimodal vision-language model designed for high-precision understanding and reasoning across text, images, and video. With 32 billion parameters, it combines deep visual perception with advanced text comprehension, enabling fine-grained spatial reasoning, document and scene analysis, and long-horizon video understanding.Robust OCR in 32 languages, and enhanced multimodal fusion through Interleaved-MRoPE and DeepStack architectures. Optimized for agentic interaction and visual tool use, Qwen3-VL-32B delivers state-of-the-art performance for complex real-world multimodal tasks.
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[
Qwen3-VL-8B-Thinking is the reasoning-optimized variant of the Qwen3-VL-8B multimodal model, designed for advanced visual and textual reasoning across complex scenes, documents, and temporal sequences. It integrates enhanced multimodal alignment and long-context processing (native 256K, expandable to 1M tokens) for tasks such as scientific visual analysis, causal inference, and mathematical reasoning over image or video inputs.
Compared to the Instruct edition, the Thinking version introduces deeper visual-language fusion and deliberate reasoning pathways that improve performance on long-chain logic tasks, STEM problem-solving, and multi-step video understanding. It achieves stronger temporal grounding via Interleaved-MRoPE and timestamp-aware embeddings, while maintaining robust OCR, multilingual comprehension, and text generation on par with large text-only LLMs.
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[
Qwen3-VL-8B-Instruct is a multimodal vision-language model from the Qwen3-VL series, built for high-fidelity understanding and reasoning across text, images, and video. It features improved multimodal fusion with Interleaved-MRoPE for long-horizon temporal reasoning, DeepStack for fine-grained visual-text alignment, and text-timestamp alignment for precise event localization.
The model supports a native 256K-token context window, extensible to 1M tokens, and handles both static and dynamic media inputs for tasks like document parsing, visual question answering, spatial reasoning, and GUI control. It achieves text understanding comparable to leading LLMs while expanding OCR coverage to 32 languages and enhancing robustness under varied visual conditions.
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[
Qwen3-VL-30B-A3B-Thinking is a multimodal model that unifies strong text generation with visual understanding for images and videos. Its Thinking variant enhances reasoning in STEM, math, and complex tasks. It excels in perception of real-world/synthetic categories, 2D/3D spatial grounding, and long-form visual comprehension, achieving competitive multimodal benchmark results. For agentic use, it handles multi-image multi-turn instructions, video timeline alignments, GUI automation, and visual coding from sketches to debugged UI. Text performance matches flagship Qwen3 models, suiting document AI, OCR, UI assistance, spatial tasks, and agent research.
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[
Qwen3-VL-30B-A3B-Instruct is a multimodal model that unifies strong text generation with visual understanding for images and videos. Its Instruct variant optimizes instruction-following for general multimodal tasks. It excels in perception of real-world/synthetic categories, 2D/3D spatial grounding, and long-form visual comprehension, achieving competitive multimodal benchmark results. For agentic use, it handles multi-image multi-turn instructions, video timeline alignments, GUI automation, and visual coding from sketches to debugged UI. Text performance matches flagship Qwen3 models, suiting document AI, OCR, UI assistance, spatial tasks, and agent research.
](https://openrouter.ai/qwen/qwen3-vl-30b-a3b-instruct)[Qwen3 VL 235B A22B Thinking](https://openrouter.ai/qwen/qwen3-vl-235b-a22b-thinking)
[
Qwen3-VL-235B-A22B Thinking is a multimodal model that unifies strong text generation with visual understanding across images and video. The Thinking model is optimized for multimodal reasoning in STEM and math. The series emphasizes robust perception (recognition of diverse real-world and synthetic categories), spatial understanding (2D/3D grounding), and long-form visual comprehension, with competitive results on public multimodal benchmarks for both perception and reasoning.
Beyond analysis, Qwen3-VL supports agentic interaction and tool use: it can follow complex instructions over multi-image, multi-turn dialogues; align text to video timelines for precise temporal queries; and operate GUI elements for automation tasks. The models also enable visual coding workflows, turning sketches or mockups into code and assisting with UI debugging, while maintaining strong text-only performance comparable to the flagship Qwen3 language models. This makes Qwen3-VL suitable for production scenarios spanning document AI, multilingual OCR, software/UI assistance, spatial/embodied tasks, and research on vision-language agents.
](https://openrouter.ai/qwen/qwen3-vl-235b-a22b-thinking)[Qwen3 VL 235B A22B Instruct](https://openrouter.ai/qwen/qwen3-vl-235b-a22b-instruct)
[
Qwen3-VL-235B-A22B Instruct is an open-weight multimodal model that unifies strong text generation with visual understanding across images and video. The Instruct model targets general vision-language use (VQA, document parsing, chart/table extraction, multilingual OCR). The series emphasizes robust perception (recognition of diverse real-world and synthetic categories), spatial understanding (2D/3D grounding), and long-form visual comprehension, with competitive results on public multimodal benchmarks for both perception and reasoning.
Beyond analysis, Qwen3-VL supports agentic interaction and tool use: it can follow complex instructions over multi-image, multi-turn dialogues; align text to video timelines for precise temporal queries; and operate GUI elements for automation tasks. The models also enable visual coding workflows—turning sketches or mockups into code and assisting with UI debugging—while maintaining strong text-only performance comparable to the flagship Qwen3 language models. This makes Qwen3-VL suitable for production scenarios spanning document AI, multilingual OCR, software/UI assistance, spatial/embodied tasks, and research on vision-language agents.
](https://openrouter.ai/qwen/qwen3-vl-235b-a22b-instruct)[Qwen3 Max](https://openrouter.ai/qwen/qwen3-max)
[
Qwen3-Max is an updated release built on the Qwen3 series, offering major improvements in reasoning, instruction following, multilingual support, and long-tail knowledge coverage compared to the January 2025 version. It delivers higher accuracy in math, coding, logic, and science tasks, follows complex instructions in Chinese and English more reliably, reduces hallucinations, and produces higher-quality responses for open-ended Q&A, writing, and conversation. The model supports over 100 languages with stronger translation and commonsense reasoning, and is optimized for retrieval-augmented generation (RAG) and tool calling, though it does not include a dedicated “thinking” mode.
](https://openrouter.ai/qwen/qwen3-max)[Qwen3 Coder Plus](https://openrouter.ai/qwen/qwen3-coder-plus)
[
Qwen3 Coder Plus is Alibaba's proprietary version of the Open Source Qwen3 Coder 480B A35B. It is a powerful coding agent model specializing in autonomous programming via tool calling and environment interaction, combining coding proficiency with versatile general-purpose abilities.
](https://openrouter.ai/qwen/qwen3-coder-plus)[Qwen3 Coder Flash](https://openrouter.ai/qwen/qwen3-coder-flash)
[
Qwen3 Coder Flash is Alibaba's fast and cost efficient version of their proprietary Qwen3 Coder Plus. It is a powerful coding agent model specializing in autonomous programming via tool calling and environment interaction, combining coding proficiency with versatile general-purpose abilities.
](https://openrouter.ai/qwen/qwen3-coder-flash)[Qwen3 Next 80B A3B Thinking](https://openrouter.ai/qwen/qwen3-next-80b-a3b-thinking)
[
Qwen3-Next-80B-A3B-Thinking is a reasoning-first chat model in the Qwen3-Next line that outputs structured “thinking” traces by default. It’s designed for hard multi-step problems; math proofs, code synthesis/debugging, logic, and agentic planning, and reports strong results across knowledge, reasoning, coding, alignment, and multilingual evaluations. Compared with prior Qwen3 variants, it emphasizes stability under long chains of thought and efficient scaling during inference, and it is tuned to follow complex instructions while reducing repetitive or off-task behavior.
The model is suitable for agent frameworks and tool use (function calling), retrieval-heavy workflows, and standardized benchmarking where step-by-step solutions are required. It supports long, detailed completions and leverages throughput-oriented techniques (e.g., multi-token prediction) for faster generation. Note that it operates in thinking-only mode.
](https://openrouter.ai/qwen/qwen3-next-80b-a3b-thinking)[Qwen3 Next 80B A3B Instruct](https://openrouter.ai/qwen/qwen3-next-80b-a3b-instruct)
[
Qwen3-Next-80B-A3B-Instruct is an instruction-tuned chat model in the Qwen3-Next series optimized for fast, stable responses without “thinking” traces. It targets complex tasks across reasoning, code generation, knowledge QA, and multilingual use, while remaining robust on alignment and formatting. Compared with prior Qwen3 instruct variants, it focuses on higher throughput and stability on ultra-long inputs and multi-turn dialogues, making it well-suited for RAG, tool use, and agentic workflows that require consistent final answers rather than visible chain-of-thought.
The model employs scaling-efficient training and decoding to improve parameter efficiency and inference speed, and has been validated on a broad set of public benchmarks where it reaches or approaches larger Qwen3 systems in several categories while outperforming earlier mid-sized baselines. It is best used as a general assistant, code helper, and long-context task solver in production settings where deterministic, instruction-following outputs are preferred.
](https://openrouter.ai/qwen/qwen3-next-80b-a3b-instruct)[Qwen3 Next 80B A3B Instruct](https://openrouter.ai/qwen/qwen3-next-80b-a3b-instruct:free)
[
Qwen3-Next-80B-A3B-Instruct is an instruction-tuned chat model in the Qwen3-Next series optimized for fast, stable responses without “thinking” traces. It targets complex tasks across reasoning, code generation, knowledge QA, and multilingual use, while remaining robust on alignment and formatting. Compared with prior Qwen3 instruct variants, it focuses on higher throughput and stability on ultra-long inputs and multi-turn dialogues, making it well-suited for RAG, tool use, and agentic workflows that require consistent final answers rather than visible chain-of-thought.
The model employs scaling-efficient training and decoding to improve parameter efficiency and inference speed, and has been validated on a broad set of public benchmarks where it reaches or approaches larger Qwen3 systems in several categories while outperforming earlier mid-sized baselines. It is best used as a general assistant, code helper, and long-context task solver in production settings where deterministic, instruction-following outputs are preferred.
](https://openrouter.ai/qwen/qwen3-next-80b-a3b-instruct:free)[Qwen Plus 0728](https://openrouter.ai/qwen/qwen-plus-2025-07-28)
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Qwen Plus 0728, based on the Qwen3 foundation model, is a 1 million context hybrid reasoning model with a balanced performance, speed, and cost combination.
](https://openrouter.ai/qwen/qwen-plus-2025-07-28)[Qwen Plus 0728](https://openrouter.ai/qwen/qwen-plus-2025-07-28:thinking)
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Qwen Plus 0728, based on the Qwen3 foundation model, is a 1 million context hybrid reasoning model with a balanced performance, speed, and cost combination.
](https://openrouter.ai/qwen/qwen-plus-2025-07-28:thinking)[Qwen3 30B A3B Thinking 2507](https://openrouter.ai/qwen/qwen3-30b-a3b-thinking-2507)
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Qwen3-30B-A3B-Thinking-2507 is a 30B parameter Mixture-of-Experts reasoning model optimized for complex tasks requiring extended multi-step thinking. The model is designed specifically for “thinking mode,” where internal reasoning traces are separated from final answers.
Compared to earlier Qwen3-30B releases, this version improves performance across logical reasoning, mathematics, science, coding, and multilingual benchmarks. It also demonstrates stronger instruction following, tool use, and alignment with human preferences. With higher reasoning efficiency and extended output budgets, it is best suited for advanced research, competitive problem solving, and agentic applications requiring structured long-context reasoning.
](https://openrouter.ai/qwen/qwen3-30b-a3b-thinking-2507)[Qwen3 Coder 30B A3B Instruct](https://openrouter.ai/qwen/qwen3-coder-30b-a3b-instruct)
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Qwen3-Coder-30B-A3B-Instruct is a 30.5B parameter Mixture-of-Experts (MoE) model with 128 experts (8 active per forward pass), designed for advanced code generation, repository-scale understanding, and agentic tool use. Built on the Qwen3 architecture, it supports a native context length of 256K tokens (extendable to 1M with Yarn) and performs strongly in tasks involving function calls, browser use, and structured code completion.
This model is optimized for instruction-following without “thinking mode”, and integrates well with OpenAI-compatible tool-use formats.
](https://openrouter.ai/qwen/qwen3-coder-30b-a3b-instruct)[Qwen3 30B A3B Instruct 2507](https://openrouter.ai/qwen/qwen3-30b-a3b-instruct-2507)
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Qwen3-30B-A3B-Instruct-2507 is a 30.5B-parameter mixture-of-experts language model from Qwen, with 3.3B active parameters per inference. It operates in non-thinking mode and is designed for high-quality instruction following, multilingual understanding, and agentic tool use. Post-trained on instruction data, it demonstrates competitive performance across reasoning (AIME, ZebraLogic), coding (MultiPL-E, LiveCodeBench), and alignment (IFEval, WritingBench) benchmarks. It outperforms its non-instruct variant on subjective and open-ended tasks while retaining strong factual and coding performance.
](https://openrouter.ai/qwen/qwen3-30b-a3b-instruct-2507)[Qwen3 235B A22B Thinking 2507](https://openrouter.ai/qwen/qwen3-235b-a22b-thinking-2507)
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Qwen3-235B-A22B-Thinking-2507 is a high-performance, open-weight Mixture-of-Experts (MoE) language model optimized for complex reasoning tasks. It activates 22B of its 235B parameters per forward pass and natively supports up to 262,144 tokens of context. This "thinking-only" variant enhances structured logical reasoning, mathematics, science, and long-form generation, showing strong benchmark performance across AIME, SuperGPQA, LiveCodeBench, and MMLU-Redux. It enforces a special reasoning mode () and is designed for high-token outputs (up to 81,920 tokens) in challenging domains.
The model is instruction-tuned and excels at step-by-step reasoning, tool use, agentic workflows, and multilingual tasks. This release represents the most capable open-source variant in the Qwen3-235B series, surpassing many closed models in structured reasoning use cases.
](https://openrouter.ai/qwen/qwen3-235b-a22b-thinking-2507)[Qwen3 Coder 480B A35B](https://openrouter.ai/qwen/qwen3-coder)
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Qwen3-Coder-480B-A35B-Instruct is a Mixture-of-Experts (MoE) code generation model developed by the Qwen team. It is optimized for agentic coding tasks such as function calling, tool use, and long-context reasoning over repositories. The model features 480 billion total parameters, with 35 billion active per forward pass (8 out of 160 experts).
Pricing for the Alibaba endpoints varies by context length. Once a request is greater than 128k input tokens, the higher pricing is used.
](https://openrouter.ai/qwen/qwen3-coder)[Qwen3 Coder 480B A35B](https://openrouter.ai/qwen/qwen3-coder:free)
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Qwen3-Coder-480B-A35B-Instruct is a Mixture-of-Experts (MoE) code generation model developed by the Qwen team. It is optimized for agentic coding tasks such as function calling, tool use, and long-context reasoning over repositories. The model features 480 billion total parameters, with 35 billion active per forward pass (8 out of 160 experts).
Pricing for the Alibaba endpoints varies by context length. Once a request is greater than 128k input tokens, the higher pricing is used.
](https://openrouter.ai/qwen/qwen3-coder:free)[Qwen3 235B A22B Instruct 2507](https://openrouter.ai/qwen/qwen3-235b-a22b-2507)
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Qwen3-235B-A22B-Instruct-2507 is a multilingual, instruction-tuned mixture-of-experts language model based on the Qwen3-235B architecture, with 22B active parameters per forward pass. It is optimized for general-purpose text generation, including instruction following, logical reasoning, math, code, and tool usage. The model supports a native 262K context length and does not implement "thinking mode" ( blocks).
Compared to its base variant, this version delivers significant gains in knowledge coverage, long-context reasoning, coding benchmarks, and alignment with open-ended tasks. It is particularly strong on multilingual understanding, math reasoning (e.g., AIME, HMMT), and alignment evaluations like Arena-Hard and WritingBench.
](https://openrouter.ai/qwen/qwen3-235b-a22b-2507)[Qwen3 0.6B](https://openrouter.ai/qwen/qwen3-0.6b-04-28)
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Qwen3-0.6B is a lightweight, 0.6 billion parameter language model in the Qwen3 series, offering support for both general-purpose dialogue and structured reasoning through a dual-mode (thinking/non-thinking) architecture. Despite its small size, it supports long contexts up to 32,768 tokens and provides multilingual, tool-use, and instruction-following capabilities.
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Qwen3-1.7B is a compact, 1.7 billion parameter dense language model from the Qwen3 series, featuring dual-mode operation for both efficient dialogue (non-thinking) and advanced reasoning (thinking). Despite its small size, it supports 32,768-token contexts and delivers strong multilingual, instruction-following, and agentic capabilities, including tool use and structured output.
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Qwen3-4B is a 4 billion parameter dense language model from the Qwen3 series, designed to support both general-purpose and reasoning-intensive tasks. It introduces a dual-mode architecture—thinking and non-thinking—allowing dynamic switching between high-precision logical reasoning and efficient dialogue generation. This makes it well-suited for multi-turn chat, instruction following, and complex agent workflows.
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Qwen3, the latest generation in the Qwen large language model series, features both dense and mixture-of-experts (MoE) architectures to excel in reasoning, multilingual support, and advanced agent tasks. Its unique ability to switch seamlessly between a thinking mode for complex reasoning and a non-thinking mode for efficient dialogue ensures versatile, high-quality performance.
Significantly outperforming prior models like QwQ and Qwen2.5, Qwen3 delivers superior mathematics, coding, commonsense reasoning, creative writing, and interactive dialogue capabilities. The Qwen3-30B-A3B variant includes 30.5 billion parameters (3.3 billion activated), 48 layers, 128 experts (8 activated per task), and supports up to 131K token contexts with YaRN, setting a new standard among open-source models.
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Qwen3-8B is a dense 8.2B parameter causal language model from the Qwen3 series, designed for both reasoning-heavy tasks and efficient dialogue. It supports seamless switching between "thinking" mode for math, coding, and logical inference, and "non-thinking" mode for general conversation. The model is fine-tuned for instruction-following, agent integration, creative writing, and multilingual use across 100+ languages and dialects. It natively supports a 32K token context window and can extend to 131K tokens with YaRN scaling.
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Qwen3-14B is a dense 14.8B parameter causal language model from the Qwen3 series, designed for both complex reasoning and efficient dialogue. It supports seamless switching between a "thinking" mode for tasks like math, programming, and logical inference, and a "non-thinking" mode for general-purpose conversation. The model is fine-tuned for instruction-following, agent tool use, creative writing, and multilingual tasks across 100+ languages and dialects. It natively handles 32K token contexts and can extend to 131K tokens using YaRN-based scaling.
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Qwen3-32B is a dense 32.8B parameter causal language model from the Qwen3 series, optimized for both complex reasoning and efficient dialogue. It supports seamless switching between a "thinking" mode for tasks like math, coding, and logical inference, and a "non-thinking" mode for faster, general-purpose conversation. The model demonstrates strong performance in instruction-following, agent tool use, creative writing, and multilingual tasks across 100+ languages and dialects. It natively handles 32K token contexts and can extend to 131K tokens using YaRN-based scaling.
](https://openrouter.ai/qwen/qwen3-32b)[Qwen3 235B A22B](https://openrouter.ai/qwen/qwen3-235b-a22b)
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Qwen3-235B-A22B is a 235B parameter mixture-of-experts (MoE) model developed by Qwen, activating 22B parameters per forward pass. It supports seamless switching between a "thinking" mode for complex reasoning, math, and code tasks, and a "non-thinking" mode for general conversational efficiency. The model demonstrates strong reasoning ability, multilingual support (100+ languages and dialects), advanced instruction-following, and agent tool-calling capabilities. It natively handles a 32K token context window and extends up to 131K tokens using YaRN-based scaling.
](https://openrouter.ai/qwen/qwen3-235b-a22b)[Qwen2.5 Coder 7B Instruct](https://openrouter.ai/qwen/qwen2.5-coder-7b-instruct)
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Qwen2.5-Coder-7B-Instruct is a 7B parameter instruction-tuned language model optimized for code-related tasks such as code generation, reasoning, and bug fixing. Based on the Qwen2.5 architecture, it incorporates enhancements like RoPE, SwiGLU, RMSNorm, and GQA attention with support for up to 128K tokens using YaRN-based extrapolation. It is trained on a large corpus of source code, synthetic data, and text-code grounding, providing robust performance across programming languages and agentic coding workflows.
This model is part of the Qwen2.5-Coder family and offers strong compatibility with tools like vLLM for efficient deployment. Released under the Apache 2.0 license.
](https://openrouter.ai/qwen/qwen2.5-coder-7b-instruct)[Qwen2.5 VL 3B Instruct](https://openrouter.ai/qwen/qwen2.5-vl-3b-instruct)
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Qwen2.5 VL 3B is a multimodal LLM from the Qwen Team with the following key enhancements:
For more details, see this blog post and GitHub repo.
Usage of this model is subject to Tongyi Qianwen LICENSE AGREEMENT.
](https://openrouter.ai/qwen/qwen2.5-vl-3b-instruct)[Qwen2.5 VL 32B Instruct](https://openrouter.ai/qwen/qwen2.5-vl-32b-instruct)
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Qwen2.5-VL-32B is a multimodal vision-language model fine-tuned through reinforcement learning for enhanced mathematical reasoning, structured outputs, and visual problem-solving capabilities. It excels at visual analysis tasks, including object recognition, textual interpretation within images, and precise event localization in extended videos. Qwen2.5-VL-32B demonstrates state-of-the-art performance across multimodal benchmarks such as MMMU, MathVista, and VideoMME, while maintaining strong reasoning and clarity in text-based tasks like MMLU, mathematical problem-solving, and code generation.
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QwQ is the reasoning model of the Qwen series. Compared with conventional instruction-tuned models, QwQ, which is capable of thinking and reasoning, can achieve significantly enhanced performance in downstream tasks, especially hard problems. QwQ-32B is the medium-sized reasoning model, which is capable of achieving competitive performance against state-of-the-art reasoning models, e.g., DeepSeek-R1, o1-mini.
](https://openrouter.ai/qwen/qwq-32b)[Qwen2.5 32B Instruct](https://openrouter.ai/qwen/qwen2.5-32b-instruct)
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Qwen2.5 32B Instruct is the instruction-tuned variant of the latest Qwen large language model series. It provides enhanced instruction-following capabilities, improved proficiency in coding and mathematical reasoning, and robust handling of structured data and outputs such as JSON. It supports long-context processing up to 128K tokens and multilingual tasks across 29+ languages. The model has 32.5 billion parameters, 64 layers, and utilizes an advanced transformer architecture with RoPE, SwiGLU, RMSNorm, and Attention QKV bias.
For more details, please refer to the Qwen2.5 Blog.
](https://openrouter.ai/qwen/qwen2.5-32b-instruct)[Qwen VL Plus](https://openrouter.ai/qwen/qwen-vl-plus)
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Qwen's Enhanced Large Visual Language Model. Significantly upgraded for detailed recognition capabilities and text recognition abilities, supporting ultra-high pixel resolutions up to millions of pixels and extreme aspect ratios for image input. It delivers significant performance across a broad range of visual tasks.
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Qwen VL Max is a visual understanding model with 7500 tokens context length. It excels in delivering optimal performance for a broader spectrum of complex tasks.
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Qwen-Turbo, based on Qwen2.5, is a 1M context model that provides fast speed and low cost, suitable for simple tasks.
](https://openrouter.ai/qwen/qwen-turbo)[Qwen2.5 VL 72B Instruct](https://openrouter.ai/qwen/qwen2.5-vl-72b-instruct)
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Qwen2.5-VL is proficient in recognizing common objects such as flowers, birds, fish, and insects. It is also highly capable of analyzing texts, charts, icons, graphics, and layouts within images.
](https://openrouter.ai/qwen/qwen2.5-vl-72b-instruct)[Qwen-Plus](https://openrouter.ai/qwen/qwen-plus)
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Qwen-Plus, based on the Qwen2.5 foundation model, is a 131K context model with a balanced performance, speed, and cost combination.
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Qwen-Max, based on Qwen2.5, provides the best inference performance among Qwen models, especially for complex multi-step tasks. It's a large-scale MoE model that has been pretrained on over 20 trillion tokens and further post-trained with curated Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) methodologies. The parameter count is unknown.
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QwQ-32B-Preview is an experimental research model focused on AI reasoning capabilities developed by the Qwen Team. As a preview release, it demonstrates promising analytical abilities while having several important limitations:
](https://openrouter.ai/qwen/qwq-32b-preview)[Qwen2.5 Coder 32B Instruct](https://openrouter.ai/qwen/qwen-2.5-coder-32b-instruct)
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Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). Qwen2.5-Coder brings the following improvements upon CodeQwen1.5:
To read more about its evaluation results, check out Qwen 2.5 Coder's blog.
](https://openrouter.ai/qwen/qwen-2.5-coder-32b-instruct)[Qwen2.5 7B Instruct](https://openrouter.ai/qwen/qwen-2.5-7b-instruct)
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Qwen2.5 7B is the latest series of Qwen large language models. Qwen2.5 brings the following improvements upon Qwen2:
Usage of this model is subject to Tongyi Qianwen LICENSE AGREEMENT.
](https://openrouter.ai/qwen/qwen-2.5-7b-instruct)[Qwen2.5 72B Instruct](https://openrouter.ai/qwen/qwen-2.5-72b-instruct)
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Qwen2.5 72B is the latest series of Qwen large language models. Qwen2.5 brings the following improvements upon Qwen2:
Usage of this model is subject to Tongyi Qianwen LICENSE AGREEMENT.
](https://openrouter.ai/qwen/qwen-2.5-72b-instruct)[Qwen2.5-VL 7B Instruct](https://openrouter.ai/qwen/qwen-2.5-vl-7b-instruct)
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Qwen2.5 VL 7B is a multimodal LLM from the Qwen Team with the following key enhancements:
For more details, see this blog post and GitHub repo.
Usage of this model is subject to Tongyi Qianwen LICENSE AGREEMENT.
](https://openrouter.ai/qwen/qwen-2.5-vl-7b-instruct)[Qwen 2 7B Instruct](https://openrouter.ai/qwen/qwen-2-7b-instruct)
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Qwen2 7B is a transformer-based model that excels in language understanding, multilingual capabilities, coding, mathematics, and reasoning.
It features SwiGLU activation, attention QKV bias, and group query attention. It is pretrained on extensive data with supervised finetuning and direct preference optimization.
For more details, see this blog post and GitHub repo.
Usage of this model is subject to Tongyi Qianwen LICENSE AGREEMENT.
](https://openrouter.ai/qwen/qwen-2-7b-instruct)[Qwen 2 72B Instruct](https://openrouter.ai/qwen/qwen-2-72b-instruct)
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Qwen2 72B is a transformer-based model that excels in language understanding, multilingual capabilities, coding, mathematics, and reasoning.
It features SwiGLU activation, attention QKV bias, and group query attention. It is pretrained on extensive data with supervised finetuning and direct preference optimization.
For more details, see this blog post and GitHub repo.
Usage of this model is subject to Tongyi Qianwen LICENSE AGREEMENT.
](https://openrouter.ai/qwen/qwen-2-72b-instruct)[Qwen 1.5 7B Chat](https://openrouter.ai/qwen/qwen-7b-chat)
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Qwen1.5 7B is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include:
For more details, see this blog post and GitHub repo.
Usage of this model is subject to Tongyi Qianwen LICENSE AGREEMENT.
](https://openrouter.ai/qwen/qwen-7b-chat)[Qwen 1.5 72B Chat](https://openrouter.ai/qwen/qwen-72b-chat)
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Qwen1.5 72B is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include:
For more details, see this blog post and GitHub repo.
Usage of this model is subject to Tongyi Qianwen LICENSE AGREEMENT.
](https://openrouter.ai/qwen/qwen-72b-chat)[Qwen 1.5 4B Chat](https://openrouter.ai/qwen/qwen-4b-chat)
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Qwen1.5 4B is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include:
For more details, see this blog post and GitHub repo.
Usage of this model is subject to Tongyi Qianwen LICENSE AGREEMENT.
](https://openrouter.ai/qwen/qwen-4b-chat)[Qwen 1.5 32B Chat](https://openrouter.ai/qwen/qwen-32b-chat)
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Qwen1.5 32B is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include:
For more details, see this blog post and GitHub repo.
Usage of this model is subject to Tongyi Qianwen LICENSE AGREEMENT.
](https://openrouter.ai/qwen/qwen-32b-chat)[Qwen 1.5 14B Chat](https://openrouter.ai/qwen/qwen-14b-chat)
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Qwen1.5 14B is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include:
For more details, see this blog post and GitHub repo.
Usage of this model is subject to Tongyi Qianwen LICENSE AGREEMENT.
](https://openrouter.ai/qwen/qwen-14b-chat)[Qwen 1.5 110B Chat](https://openrouter.ai/qwen/qwen-110b-chat)
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Qwen1.5 110B is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include:
For more details, see this blog post and GitHub repo.
Usage of this model is subject to Tongyi Qianwen LICENSE AGREEMENT.