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Qwen's open-weight 80B/3B-active MoE coding model with a 262K context, tool calling and Apache-2.0 licence, suited to repository-scale code tasks and IDE assistants.
Overview. Qwen3-Coder-Next is an open-weight, code-specialised model from Qwen, Alibaba's model team, released in February 2026 under the Apache-2.0 licence. It is a Mixture-of-Experts design with 80B total parameters but only 3B active per token, drawn from 512 experts, and it carries a native 262,144-token context window. Developers reach for it when they want a permissively licensed coding model that handles whole repositories in context without the compute footprint of a dense 80B model.
Key strengths. The model is built for coding agents and local development, and the benchmark signal backs that focus: its Artificial Analysis Coding Index of 36.2 has held steady across repeated measurements, a solid result for a model activating just 3B parameters. It ships with documented tool-calling support, so it can drive structured function calls inside an agent loop. Median throughput of roughly 110 tokens per second keeps Inference fast enough for IDE-style interaction, and the 262K context lets it take in large codebases in a single request.
When to use it. Use this for code generation, refactoring and repository-scale code understanding when you need a large context window and an open licence rather than top-end general reasoning. It fits interactive workloads such as IDE assistants and coding chat, where its throughput matters more than a sub-second first token. Its benchmark profile, with a Coding Index well above its general Intelligence Index, makes it a sensible pick for programming tasks that don't require extended deliberation.
Limitations. The model runs in non-thinking mode only, with no extended reasoning or chain-of-thought blocks, and its general-purpose Intelligence Index (10.1) and Agentic Index (3.6) on Artificial Analysis are low, so sustained multi-step autonomous tool loops and non-code tasks are weak spots. It is text-only, with no image, audio or document input, and its training cutoff is not published. Median time to first token sits above one second, which rules it out for latency-critical realtime use.
Sovereignty note. Qwen3-Coder-Next is built by Qwen, Alibaba's model team in China, and its weights are open under Apache-2.0, so it can be run on infrastructure of your choice. Where requests run, and whether inputs are retained or used for training, depends on the endpoint serving it; see the endpoint facts on this page.
| Inference Provider | Sovereignty | Runs in | In / out per 1M | Status | Pin |
|---|---|---|---|---|---|
| L1 | 0,15 / 0,80€ | Online |
39 use cases classified by the curator. The note says why.
Tool-calling, prompt-cache, and 262K context are the core requirements for repo-editing agents; the 3B-active MoE keeps per-token cost reasonable. Caveat: draft status and above-average verbosity will erode efficiency in high-volume loops.
Long 262K window accommodates stack traces plus surrounding source, and tool-calling lets it pull additional files from an IDE or repo agent. Coding-specialised training is directly on point.
Purpose-built code model: 80B/3B-active MoE with 262K context and a 36.2 coding index, Apache-2.0 open weights. Prompt-cache support reduces cost on repetitive IDE completion prefixes.
Coding-focused training plus 262K context supports explaining large files and cross-module call graphs in one pass. Open weights allow on-prem deployment for proprietary code.
262K context fits whole-repo diffs, and the coding-specialised MoE (3B active params) keeps cost low per review pass. Family index of 36.2 places it in the competitive coding tier.
First-class tool-calling and prompt-cache capabilities, backed by 262K context for large tool inventories. Draft status is the only flag; capability itself is directly supported.
Long-context RAG answering is viable given 262K window and prompt-caching; however the model is tuned for code, so general-document answer quality trails general-purpose flagships.
Tool-calling fits DOM/API-driven browser agents, but the model has no vision capability, so screenshot-grounded navigation is out of scope. Text-only automation is workable.
Competent via prompting and structured output through tool-calling, but no dedicated classification tuning — a smaller task-specific model is usually cheaper for high-volume labelling.
Tool-calling allows code-execution-backed analysis, which suits this coding model; narrative statistical reasoning is not its primary strength.
262K context handles long documents in a single pass, and prompt-cache reduces cost on repeated document prefixes. Coding-specialised training means non-code prose QA is competent but not class-leading.
Long context and tool-calling support span-level entity extraction from large documents; a general-purpose model would likely match or beat it outside code/log corpora.
Vision is not supported; chart parsing requires an image-capable model.
No creative-writing tuning is claimed and the family take flags verbosity — general-purpose models dominate this use case.
Not dialogue-tuned and flagged for verbosity in the family take — poor default for latency-sensitive support chat where concise answers are required.
No image input — OCR pipelines need a vision or specialist OCR model instead.
No vision capability is listed — capabilities are tool-calling and prompt-cache only.
Coding-specialised training and above-average verbosity make this a poor default for prose drafting; general-purpose long-context models are better suited.
Sourced numbers, not our own evaluations. Percentiles are against the whole Stav catalogue.
Computed from aa-intelligence-index, gpqa-diamond, aa-engineering-index, aa-coding-index, livecodebench, terminal-bench-v4, ifbench, aa-multilingual-index, aa-lcr.
curl https://api.stav.ai/v1/chat/completions \
-H "Authorization: Bearer $STAV_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen/qwen3-coder-next",
"messages": [{"role": "user", "content": "Hello!"}]
}'→ X-Stav-Request-Id · X-Stav-Model · X-Stav-Provider → X-Stav-Sovereignty-Level — the rung the request was served at