Qwen: Qwen3 Coder Next

Qwen: Qwen3 Coder Next

Alibabacodigo

Qwen: Qwen3 Coder Next by Alibaba

Open SourceAPI AvailableTool Calling

Specifications

Context Window

262K tokens

Input Price/1M

$0.35

Output Price/1M

$1.20

Parameters

Speed

154 tok/s

Latency (TTFT)

1.3s

Max Output

262K tokens

Benchmarks

Qwen: Qwen3 Coder Next results on the main AI model evaluation benchmarks. Higher scores indicate better performance.

Agentic

BenchmarkScoreMaximumMethodology
Terminal-Bench Hard18.0100.0

Coding

BenchmarkScoreMaximumMethodology
AA Coding Index36.2100.0Artificial Analysis official API
SciCode32.0100.0

Long Context

BenchmarkScoreMaximumMethodology
AA-LCR40.0100.0

overall

BenchmarkScoreMaximumMethodology
AA Intelligence Index21.3100.0Artificial Analysis official API

Reasoning

BenchmarkScoreMaximumMethodology
GPQA Diamond74.0100.0Artificial Analysis official API
IFBench35.0100.0
HLE9.0100.0

Tool Use

BenchmarkScoreMaximumMethodology
Tau²-Bench80.0100.0

Information

Release date
February 03, 2026
Tool Calling
✅ Supported
Vision
❌ Not supported
Audio
❌ Not supported

Full Analysis: Qwen: Qwen3 Coder Next

What is Qwen: Qwen3 Coder Next?

Qwen: Qwen3 Coder Next is an AI model developed by Alibaba, classified as a codigo model. It focuses on text processing and natural language generation. As an open source model, it is available for download, customization, and on-premises deployment. With a context window of 262K tokens, it is suitable for processing long documents such as contracts, books, and complete codebases.

Pricing & Costs in 2026

Qwen: Qwen3 Coder Next is usage-based, priced at $0.35/1M input tokens and $1.2/1M output tokens. For context: 1 million tokens is approximately 750,000 words, or about 10 average-length books. At this aggressive price point, it is one of the most cost-effective options on the market, ideal for high-volume applications like chatbots, bulk document analysis, and automation.

Benchmarks & Performance

Qwen: Qwen3 Coder Next was evaluated on 9 different benchmarks, covering categories like Agentic, Coding, Long Context, overall, Reasoning, Tool Use. Results show solid performance across available evaluations.

It's important to note that benchmarks measure specific aspects and don't capture the full user experience. Factors like instruction adherence, behavior in long conversations, and real-world task quality vary significantly between models and aren't always reflected in standard scores.

Recommended Use Cases

Qwen: Qwen3 Coder Next specializes in codigo, offering advanced capabilities for creating and processing codigo content.

Comparison with Alternatives

In the 2026 AI model ecosystem, Qwen: Qwen3 Coder Next competes directly with similarly capable models. Alibaba competes in this segment against OpenAI, Anthropic, Google, and Meta. The choice between models depends on the specific use case, budget, latency requirements, and need for features like multimodality and tool calling.

For a detailed side-by-side comparison, use our comparison tool or check the overall model ranking.

Frequently Asked Questions

What is Qwen: Qwen3 Coder Next?

Qwen: Qwen3 Coder Next is an AI model developed by Alibaba. It is a codigo model, open source.

How much does Qwen: Qwen3 Coder Next cost?

Qwen: Qwen3 Coder Next costs $0.35/1M input tokens and $1.2/1M output tokens. For heavy usage (e.g., a chatbot handling 100k messages/month), costs can range from $10 to $1,000 depending on volume.

How does Qwen: Qwen3 Coder Next compare with other models?

In available benchmarks, Qwen: Qwen3 Coder Next scored: Terminal-Bench Hard: 18/100, AA Coding Index: 36.2/100, SciCode: 32/100. See the full table above for a detailed comparison.

Is Qwen: Qwen3 Coder Next open source?

Yes, Qwen: Qwen3 Coder Next is an open source model. You can deploy it on-premises, customize it via fine-tuning, and maintain full control over your data. Check the official repository for the specific license.

What is Qwen: Qwen3 Coder Next best for?

Qwen: Qwen3 Coder Next excels at general-purpose language tasks. With its large context window, it handles long documents, codebases, and extended conversations. It supports tool calling for API integrations and automation.

Last updated: August 10, 2026 View methodology →