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Qwen3 Coder Next

  • Code Generation

Qwen3 Coder Next is an open-weight, coding-specialized language model from Qwen that uses an efficient Mixture-of-Experts architecture to deliver strong agentic coding performance while remaining practical for local deployment.

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Providers Comparison

Speed

Output tokens per second · Higher is better

246
97
92
91
90
80
60
27
LLM API
SiliconFlow (FP8)
GMI
CoreWeave
Novita
DeepSeek
Parasail (FP8)
DeepInfra (FP4)

End-to-End Response Time

Seconds · Lower is better

65.1
8.1
19.8
20.4
20.9
23.1
24.6
30.6
LLM API
SiliconFlow (FP8)
GMI
CoreWeave
Novita
DeepSeek
Parasail (FP8)
DeepInfra (FP4)

Price

USD per 1M tokens (blended) · Lower is better

65.1
8.1
19.8
20.4
20.9
23.1
24.6
30.6
LLM API
SiliconFlow (FP8)
GMI
CoreWeave
Novita
DeepSeek
Parasail (FP8)
DeepInfra (FP4)

About the model

What is Qwen3 Coder Next?

Qwen3 Coder Next is an open-weight language model from Qwen specialized for code generation and coding agents, built on an 80B-parameter sparse Mixture-of-Experts design with only about 3B active parameters at inference. It is mainly used for software engineering tasks such as code generation, refactoring, and debugging across multiple programming languages, often integrated into IDEs or developer tooling. It is also deployed as the core model in autonomous or semi-autonomous coding agents that plan changes, run tests, and iteratively fix errors in local development workflows. It belongs to the Qwen3-Next model family as a code-focused successor to earlier Qwen and Qwen2/3 coding models.

Model capabilities

5 Core Capabilities

  • Code Generation

    Specialized for writing and editing code in multiple programming languages, including implementing features, refactoring, and converting between languages.

  • Agentic Coding

    Designed for coding agents that plan multi-step tasks, run code in environments, observe outputs, and iteratively refine solutions.

  • Debugging Support

    Helps locate, understand, and fix bugs, explaining issues, suggesting patches, and improving existing implementations in complex codebases.

  • Long-Context Handling

    Handles very long codebases and project contexts efficiently, maintaining relevant details across large files and extended development sessions.

  • Multilingual Text

    Inherits Qwen family’s multilingual capability, enabling understanding and generation of natural language instructions around code in many languages.

Use cases

6 Most Valuable Use Cases

  • Code Generation Assistant
  • Code Completion Support
  • Bug Detection Assistance
  • Code Review Automation
  • Developer Productivity Tools
  • Programming Documentation Help

Transparent pricing

Cost Comparison

LLM API offers the lowest token costs and fastest Qwen3 Coder Next-compatible access across providers.

Provider Region Latency Throughput Uptime Input ($/1M) Output ($/1M) Context
LLM API BEST Global 90ms 120 tps 99.99% $0.10 $0.10 128K
Qwen Global ~160ms ~70 tps 99.9% ~$0.18 ~$0.18 128K
Alibaba Cloud APAC ~220ms ~55 tps 99.9% ~$0.20 ~$0.22 64K
OpenRouter Global ~200ms ~60 tps ~99.9% ~$0.16 ~$0.18 128K

Performance benchmarks

Technical Specifications

Metric Qwen3 Coder Next GPT-4.1 Mini Claude 3.5 Sonnet
Avg Latency ~180ms ~200ms ~350ms
Context Window 128K 128K 200K
Input Price ($/1M) $0.20 $0.15 $3.00
Output Price ($/1M) $0.60 $0.60 $15.00
Max Output Tokens 8K 8K 4K
Throughput 60 tps 80 tps 40 tps
Uptime 99.9% 99.9% 99.9%

30-day usage via LLM API

62B
Prompt tokens processed (last 30 days)
21B
Completion tokens generated (last 30 days)
3.8M
API requests served (last 30 days)
210K
Unique developers using Qwen3 Coder Next (last 30 days)
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Architecture & Integration

Why Build on LLM.API?

One unified API. Every major model. Built-in reliability, cost control, and observability.

  • Unified AI Routing

    Intelligently route each request across providers based on cost, latency, and quality. One API, always the best model for the job.

    One endpoint. Optimal model.

  • Predictable AI Costs

    Define per-request or global cost caps and let LLM.API optimize provider choice. Avoid surprise bills while still getting high-quality results.

    Control spend, not output.

  • Resilient Fallback Logic

    Automatically fail over to backup models on errors, timeouts, or degraded providers. Increase reliability without rewriting application logic.

    No single-point failure.

  • Deep Observability

    Get unified logs, traces, and metrics across every provider and model. Debug faster, tune prompts, and prove performance with real usage data.

    See every token flow.

  • Task-Level Orchestration

    Model-agnostic tasks abstract prompts, tools, and parameters into reusable units. Ship multi-model workflows without wiring each provider by hand.

    Think tasks, not models.

  • High-Throughput Batch

    Run massive inference batches with provider-aware parallelism, retries, and backoff handled for you. Maximize throughput while staying within rate limits.

    Scale batches safely.

Decision guide

When to Use — When NOT to Use

Use it if...

  • You need an open-source–style coding assistant optimized for code generation and completion.
  • You need to scaffold new projects, boilerplate, or APIs across multiple programming languages.
  • Your use case involves interactive code editing, refactoring, and adding tests to existing repositories.
  • Your use case involves translating code between languages while preserving behavior and structure.
  • You need a coding model for editor, IDE, or CLI integration with automation.
  • Your use case involves explaining complex source code or libraries to developers in natural language.

Avoid if...

  • You need state-of-the-art general-purpose reasoning and writing beyond programming-related tasks.
  • Your workload requires vision, speech, or multimodal understanding in addition to code.
  • You need highly reliable domain-specific knowledge outside software engineering or computer science.
  • Your workload requires strict enterprise guarantees, certifications, and long-term commercial support contracts.
  • You need the smallest possible latency or cost from lightweight, distilled code models.
  • Your workload requires guaranteed compatibility with proprietary platform features from other providers.

FAQ

Frequently Asked Questions

  • What is Qwen3 Coder Next?

    Qwen3 Coder Next is a code-focused large language model by Qwen, optimized for software development tasks such as generation, refactoring, and debugging.

  • What is Qwen3 Coder Next best suited for?

    It is best suited for multi-language code generation, completion, bug fixing, and explaining complex codebases or algorithms.

  • How is Qwen3 Coder Next priced when accessed through LLM.API?

    LLM.API applies its own per-token or per-call pricing on top of Qwen3 Coder Next; check your LLM.API dashboard or docs for current rates.

  • What context window does Qwen3 Coder Next support on LLM.API?

    Through LLM.API, Qwen3 Coder Next supports a large context window suitable for working with multi-file code snippets and long discussions; check docs for the exact limit.

  • How fast is Qwen3 Coder Next in terms of latency on LLM.API?

    Typical latency is comparable to other modern code LLMs, but actual speed depends on request size, load, and your region's network conditions.

  • Which modalities does Qwen3 Coder Next support?

    Qwen3 Coder Next is a text-only model focused on code and natural language, without native image, audio, or video understanding.

  • How do I call Qwen3 Coder Next via the LLM.API gateway?

    Use the LLM.API chat or completion endpoint with the model identifier for Qwen3 Coder Next and pass your messages plus any model-specific parameters.

  • How does Qwen3 Coder Next compare to general-purpose LLMs for coding?

    Compared to general-purpose models, Qwen3 Coder Next is typically stronger on coding tasks and code reasoning but less tuned for open-ended conversational topics.

  • Does Qwen3 Coder Next support multiple programming languages?

    Yes, Qwen3 Coder Next supports a wide range of popular programming languages, including Python, JavaScript, Java, C++, and more.

  • What are key limitations of Qwen3 Coder Next I should know?

    It may produce incorrect or non-compiling code, hallucinate APIs, miss project-specific constraints, and cannot access your private repositories without explicitly provided context.

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