--- title: 'Qwen3-Coder-Next API: Model ID, 256K Context, Coding Agents | RunAPI' description: Call qwen3-coder-next through RunAPI's OpenAI-compatible Chat Completions API. Open-weight coding MoE with 80B total and 3B active, 256K context, tool calls. url: https://runapi.ai/models/qwen/qwen3-coder-next.md canonical: https://runapi.ai/models/qwen/qwen3-coder-next locale: en site: RunAPI image: https://runapi.ai/assets/opengraph-bfaaa0f0.png alternates: en: https://runapi.ai/models/qwen/qwen3-coder-next zh-CN: https://runapi.ai/zh-CN/models/qwen/qwen3-coder-next es: https://runapi.ai/es/models/qwen/qwen3-coder-next ja: https://runapi.ai/ja/models/qwen/qwen3-coder-next de: https://runapi.ai/de/models/qwen/qwen3-coder-next fr: https://runapi.ai/fr/models/qwen/qwen3-coder-next pt-BR: https://runapi.ai/pt-BR/models/qwen/qwen3-coder-next ko: https://runapi.ai/ko/models/qwen/qwen3-coder-next it: https://runapi.ai/it/models/qwen/qwen3-coder-next nl: https://runapi.ai/nl/models/qwen/qwen3-coder-next pl: https://runapi.ai/pl/models/qwen/qwen3-coder-next tr: https://runapi.ai/tr/models/qwen/qwen3-coder-next zh-TW: https://runapi.ai/zh-TW/models/qwen/qwen3-coder-next zh-HK: https://runapi.ai/zh-HK/models/qwen/qwen3-coder-next ar: https://runapi.ai/ar/models/qwen/qwen3-coder-next x-default: https://runapi.ai/models/qwen/qwen3-coder-next model: Qwen provider: Alibaba variant_id: qwen3-coder-next sdk_model_id: qwen3-coder-next modality: text billing_unit: 1K tokens status: retired --- > HTML version: https://runapi.ai/models/qwen/qwen3-coder-next > Site index for agents: https://runapi.ai/llms.txt # qwen3-coder-next API **Retired:** Retired on 2026-10-10. Call qwen3-coder-next from the Qwen family through RunAPI. Billing is per token with no subscription, and a request that fails before its response starts is not charged. **Alibaba / [Qwen](https://runapi.ai/models/qwen.md)** **Retired · text · Commercial OK** ## What Qwen3-Coder-Next is built for Qwen3-Coder-Next is an open-weight coding model from Alibaba Qwen, released under Apache 2.0 on February 3, 2026. It is built on Qwen3-Next-80B-A3B, a sparse Mixture-of-Experts model with hybrid attention that has 80B total parameters and 3B active per token. Qwen trained it for coding agents and repository-level work. Qwen's model card says it runs in non-thinking mode only and does not produce thinking blocks, so a response starts with the answer or the tool call. Alibaba's API serves it with a 262,144-token context window, up to 204,800 input tokens, and up to 65,536 output tokens. On RunAPI, call qwen3-coder-next through the OpenAI-compatible Chat Completions endpoint (/v1/chat/completions) with an OpenAI SDK pointed at https://runapi.ai/v1. It takes text input. Tools use the standard OpenAI tools format, and the model returns tool_calls in the response. **Model ID:** qwen3-coder-next **Released:** February 3, 2026 **Architecture:** MoE, 80B total / 3B active, Apache 2.0 weights **Context window:** 262,144 tokens **Max output:** 65,536 tokens **Thinking:** None (non-thinking only) **Endpoint on RunAPI:** OpenAI Chat Completions, text input ### Switch variant - **qwen3-coder-next** (current) - [qwen3-vl-235b-a22b-instruct](https://runapi.ai/models/qwen/qwen3-vl-235b-a22b-instruct.md) - [qwen3-vl-235b-a22b-thinking](https://runapi.ai/models/qwen/qwen3-vl-235b-a22b-thinking.md) - [qwen3.5-122b-a10b](https://runapi.ai/models/qwen/qwen3.5-122b-a10b.md) - [qwen3.5-27b](https://runapi.ai/models/qwen/qwen3.5-27b.md) - [qwen3.5-35b-a3b](https://runapi.ai/models/qwen/qwen3.5-35b-a3b.md) - [qwen3.5-397b-a17b](https://runapi.ai/models/qwen/qwen3.5-397b-a17b.md) - [qwen3.6-27b](https://runapi.ai/models/qwen/qwen3.6-27b.md) - [qwen3.6-35b-a3b](https://runapi.ai/models/qwen/qwen3.6-35b-a3b.md) 9 versions available qwen3-coder-next is one of the Qwen models on RunAPI. It uses the same API key as every other model, and you switch to it by changing the model ID in your request. Each call is synchronous and billed in USD by input, output, and cache tokens, with no subscription or minimum spend. A request that fails before the response starts is not charged; once a response is streaming, the tokens already generated are billed, even if the client disconnects. - Per-token billing in USD with no subscription - No charge when a request fails before its response starts - Token-by-token streaming - Switch models by changing one model ID - One API key and one bill for every model and provider ## Technical details | Field | Value | |---|---| | Provider | Alibaba | | Model ID | qwen3-coder-next | | Modality | Text | | Task types | Synchronous | | API endpoint | `/v1/chat/completions` | | | `/v1/responses` | | | `/v1/messages` | | | `/v1beta/models/qwen3-coder-next:generateContent` | | | `/v1beta/models/qwen3-coder-next:streamGenerateContent` | | Billing unit | 1K tokens | | Catalog status | Retired | **HOW IT WORKS** ## Use This Model in 3 Steps 1. **Get Your Key** - Sign up for free and create an API key for qwen3-coder-next from the dashboard. 2. **Send a Request** - POST to /v1/chat/completions with the qwen3-coder-next model slug and your parameters. 3. **Get Results** - Read the completed qwen3-coder-next response directly from the endpoint. ## What this model offers - **Endpoint coverage** — Exposes 1 public endpoint(s): chat_completion. - **Usage-based billing** — Metered by 1K tokens with no subscription requirement. **API REFERENCE** ## Endpoint Specification | Field | Value | |---|---| | Endpoint | `POST /v1/chat/completions` | | Model ID | `qwen3-coder-next` | | Task Types | `chat_completion` | ## Request parameters No request parameters are published for this endpoint. **Request example** ```json { "model": "qwen3-coder-next", "messages": [ { "role": "user", "content": "Read this screenshot of a failing dashboard, explain what the error message means, and suggest the code change that would fix it." } ] } ``` ### Model skill — qwen3-coder-next Install the skill once, then use the variant ID from this page while building. Base URL: `https://runapi.ai` | Endpoint | Protocol | |---|---| | `POST /v1/chat/completions` | OpenAI compatible | | `POST /v1/responses` | OpenAI Responses | | `POST /v1/messages` | Anthropic compatible | | `POST /v1beta/models/qwen3-coder-next:generateContent` | Gemini generateContent | | `POST /v1beta/models/qwen3-coder-next:streamGenerateContent` | Gemini streamGenerateContent | Use the OpenAI / Anthropic / Google Gen AI SDK with your RunAPI API key and `model: "qwen3-coder-next"`. ### Use qwen3-coder-next with a model skill 1. **Install** — Install the model skill for the Qwen line. It loads docs, request formats, and pricing notes into your coding workspace. 2. **Configure** — Point your SDK at https://runapi.ai and set your RunAPI API key. The same key works for every model and provider. 3. **Call** — Send a request in an API format this model supports, with the model field set to the model ID on this page. RunAPI forwards it to the provider. 4. **Receive** — Read the response from the same call, or stream it as it is generated. The usage field shows the tokens you are billed for. ### What's different about qwen3-coder-next - **VS QWEN3-VL-235B-A22B-INSTRUCT: Open-weight Qwen coding model for agents (3B active); 256K context; OpenAI-compatible API** — Qwen3-VL vision MoE with direct answers; 131K context; image input via OpenAI-compatible API - **VS QWEN3-VL-235B-A22B-THINKING: Open-weight Qwen coding model for agents (3B active); 256K context; OpenAI-compatible API** — Thinking-only Qwen3-VL vision MoE; 131K context; image input via OpenAI-compatible API - **VS QWEN3.5-122B-A10B: Open-weight Qwen coding model for agents (3B active); 256K context; OpenAI-compatible API** — Open-weight Qwen3.5 vision MoE (10B active); 256K context; OpenAI-compatible API ## USE CASES **Customer support** Answer customer questions from a private knowledge base, reducing ticket volume. **Document analysis** Draft contract summaries and flag key clauses for attorney review. **Code generation** Auto-generate unit tests, code reviews, and refactoring suggestions in CI. ## Frequently asked questions about Qwen3-Coder-Next ### What is Qwen3-Coder-Next built for? Coding agents and repository-level work, such as reading a codebase, editing several files, and calling tools in a loop. Qwen reports SWE-Bench-Pro results comparable to models with 10 to 20 times more active parameters. ### Does qwen3-coder-next support tool calling? Yes. Send tools in the OpenAI format on Chat Completions and the model returns tool_calls. Qwen's model card describes tool calling as one of the model's main strengths. ### Does Qwen3-Coder-Next have a thinking mode? No. Qwen's model card says it supports non-thinking mode only and does not generate thinking blocks. If a task needs a reasoning step, use a Qwen3.5 or Qwen3.6 model, which have a thinking mode. ### How many parameters does Qwen3-Coder-Next use per token? 3B of its 80B parameters are active for each token. The model is built on Qwen3-Next-80B-A3B, which combines a sparse Mixture-of-Experts design with hybrid attention. ### How do I select qwen3-coder-next? Pass the model ID shown in the quickstart. ### What's the rate limit on this variant? Per-key rate limits scale with your usage tier. The pricing page shows current limits. If you need higher throughput, contact support to discuss tier upgrades. ### Can I switch variants later? Yes. Variant is a parameter in the request. Switch by changing the model ID — no code changes, no re-authentication, no separate billing setup. All variants share the same API key and request shape. ### Does it stream? Where streaming is available, RunAPI streams end-to-end. LLM models support token-level streaming. Media models use async task polling or webhook callbacks for result delivery. ### Where do I report quality issues? Open an issue on the public GitHub repo or email support at contact@runapi.ai. Include the task ID and model ID so the team can investigate the specific generation. ### Do I need a separate provider account? No. Your RunAPI API key is enough to access this variant and every other model in the catalog. You do not need accounts with the underlying provider. ### RELATED - [qwen3.5-35b-a3b](https://runapi.ai/models/qwen/qwen3.5-35b-a3b.md) — by Alibaba · Input $0.25 / 1M tokens | Output $2.00 / 1M tokens - [qwen3.5-27b](https://runapi.ai/models/qwen/qwen3.5-27b.md) — by Alibaba · Input $0.30 / 1M tokens | Output $2.40 / 1M tokens - [qwen3.6-35b-a3b](https://runapi.ai/models/qwen/qwen3.6-35b-a3b.md) — by Alibaba · Input $0.38 / 1M tokens | Output $2.25 / 1M tokens - [qwen3.5-122b-a10b](https://runapi.ai/models/qwen/qwen3.5-122b-a10b.md) — by Alibaba · Input $0.40 / 1M tokens | Output $3.20 / 1M tokens ### Start building with Qwen. - [Create free account](https://runapi.ai/login) - [Read the quickstart →](https://runapi.ai/docs/api/openai/chat-completions) ## Building with a team? We're here to help with enterprise setup, integrations, and technical questions. [Contact Us](https://runapi.ai/contact.md) --- ## More from RunAPI - [Home](https://runapi.ai/.md) ### Product - [Model Catalog](https://runapi.ai/models.md) - [Pricing](https://runapi.ai/pricing.md) - [Providers](https://runapi.ai/models#all-models) ### Developers - [Documentation](https://runapi.ai/docs/guides) - [SDKs](https://runapi.ai/sdk.md) - [CLI](https://runapi.ai/cli.md) - [MCP Server](https://runapi.ai/mcp.md) - [x402](https://runapi.ai/x402.md) ### Guides - [Claude Code vs Cursor](https://runapi.ai/claude-code-vs-cursor.md) - [Cursor API Setup](https://runapi.ai/cursor-api-setup.md) - [RunAPI vs OpenRouter](https://runapi.ai/openrouter-alternative.md) ### Company - [Enterprise](https://runapi.ai/contact.md) - [Contact](https://runapi.ai/contact.md) ### Legal - [Terms](https://runapi.ai/terms.md) - [Privacy](https://runapi.ai/privacy.md) - [Site index for agents](https://runapi.ai/llms.txt) Contact: contact@runapi.ai ## Structured data ```json [ { "@context": "https://schema.org", "inLanguage": "en", "@type": "WebSite", "name": "RunAPI", "url": "https://runapi.ai/", "potentialAction": { "@type": "SearchAction", "target": { "@type": "EntryPoint", "urlTemplate": "https://runapi.ai/models?q={search_term_string}" }, "query-input": "required name=search_term_string" } }, { "@context": "https://schema.org", "inLanguage": "en", "@type": "Organization", "name": "RunAPI", "url": "https://runapi.ai/", "logo": { "@type": "ImageObject", "url": "https://runapi.ai/icon.svg" }, "sameAs": [ "https://github.com/runapi-ai" ] }, { "@context": "https://schema.org", "inLanguage": "en", "@type": "SoftwareApplication", "name": "Qwen qwen3-coder-next", "applicationCategory": "DeveloperApplication" }, { "@context": "https://schema.org", "inLanguage": "en", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Home", "item": "https://runapi.ai/" }, { "@type": "ListItem", "position": 2, "name": "Models", "item": "https://runapi.ai/models" }, { "@type": "ListItem", "position": 3, "name": "Qwen", "item": "https://runapi.ai/models/qwen" }, { "@type": "ListItem", "position": 4, "name": "qwen3-coder-next", "item": "https://runapi.ai/models/qwen/qwen3-coder-next" } ] }, { "@context": "https://schema.org", "inLanguage": "en", "@type": "FAQPage", "mainEntity": [ { "@type": "Question", "name": "What is Qwen3-Coder-Next built for?", "acceptedAnswer": { "@type": "Answer", "text": "Coding agents and repository-level work, such as reading a codebase, editing several files, and calling tools in a loop. Qwen reports SWE-Bench-Pro results comparable to models with 10 to 20 times more active parameters." } }, { "@type": "Question", "name": "Does qwen3-coder-next support tool calling?", "acceptedAnswer": { "@type": "Answer", "text": "Yes. Send tools in the OpenAI format on Chat Completions and the model returns tool_calls. Qwen's model card describes tool calling as one of the model's main strengths." } }, { "@type": "Question", "name": "Does Qwen3-Coder-Next have a thinking mode?", "acceptedAnswer": { "@type": "Answer", "text": "No. Qwen's model card says it supports non-thinking mode only and does not generate thinking blocks. If a task needs a reasoning step, use a Qwen3.5 or Qwen3.6 model, which have a thinking mode." } }, { "@type": "Question", "name": "How many parameters does Qwen3-Coder-Next use per token?", "acceptedAnswer": { "@type": "Answer", "text": "3B of its 80B parameters are active for each token. The model is built on Qwen3-Next-80B-A3B, which combines a sparse Mixture-of-Experts design with hybrid attention." } }, { "@type": "Question", "name": "How do I select qwen3-coder-next?", "acceptedAnswer": { "@type": "Answer", "text": "Pass the model ID shown in the quickstart." } }, { "@type": "Question", "name": "What's the rate limit on this variant?", "acceptedAnswer": { "@type": "Answer", "text": "Per-key rate limits scale with your usage tier. The pricing page shows current limits. If you need higher throughput, contact support to discuss tier upgrades." } }, { "@type": "Question", "name": "Can I switch variants later?", "acceptedAnswer": { "@type": "Answer", "text": "Yes. Variant is a parameter in the request. Switch by changing the model ID — no code changes, no re-authentication, no separate billing setup. All variants share the same API key and request shape." } }, { "@type": "Question", "name": "Does it stream?", "acceptedAnswer": { "@type": "Answer", "text": "Where streaming is available, RunAPI streams end-to-end. LLM models support token-level streaming. Media models use async task polling or webhook callbacks for result delivery." } }, { "@type": "Question", "name": "Where do I report quality issues?", "acceptedAnswer": { "@type": "Answer", "text": "Open an issue on the public GitHub repo or email support at contact@runapi.ai. Include the task ID and model ID so the team can investigate the specific generation." } }, { "@type": "Question", "name": "Do I need a separate provider account?", "acceptedAnswer": { "@type": "Answer", "text": "No. Your RunAPI API key is enough to access this variant and every other model in the catalog. You do not need accounts with the underlying provider." } } ] } ] ```