Qwen3-Coder-30B-A3B-Instruct

Qwen3-Coder-30B-A3B-Instruct is an outstanding example of a high-quality large language model with advanced specialization in programming. This Mixture-of-Experts model has 30.5 billion total parameters, of which only 3.3 billion are activated per token, and out of 128 experts, only 8 are activated per token. The model comprises 48 hidden layers with grouped query attention (32 heads for Q and 4 for KV), delivering exceptional processing efficiency with minimal computational resource consumption. Native support for a 262,144-token context window—expandable up to 1 million tokens via Yarn—makes the model ideal for working with large code repositories within complex projects.

The key unique feature of Qwen3-Coder-30B-A3B-Instruct lies in its superior agent capabilities. The model does not merely generate code; it autonomously interacts with development tools, executes multi-step programming tasks, and is capable of solving complex problems without human intervention. On the LiveCodeBench v6 benchmark, the model achieves an impressive 66.0%, significantly outperforming the base version Qwen3-30B-A3B (57.4%). In AIME25 tasks (advanced mathematics for programming), it demonstrates 85.0% accuracy, surpassing Gemini-2.5-Flash-Thinking (72.0%) and confidently competing with much larger models. The model outperforms DeepSeek V3 on most coding tasks and delivers agent workflow performance comparable to Claude Sonnet 4, a remarkable achievement for an open-source solution.

Qwen3-Coder-30B-A3B-Instruct unlocks entirely new possibilities in software development. The model is integrated with popular agent-based programming platforms, including Qwen Code, CLINE, Roo Code, and Kilo Code, offering a unified function-calling format for seamless operation within CI/CD pipelines. Support for 358 programming languages makes it a universal reference tool for developers. The model particularly excels in repository-scale understanding scenarios, where it can analyze and modify massive codebases, automatically refactor legacy code, and create complex full-stack applications with minimal developer intervention.


Announce Date: 22.07.2025
Parameters: 31B
Experts: 128
Activated at inference: 4B
Context: 263K
Layers: 48
Attention Type: Full or Sliding Window Attention
Developer: Qwen
Transformers Version: 4.52.3
License: Apache 2.0

Public endpoint

Use our pre-built public endpoints for free to test inference and explore Qwen3-Coder-30B-A3B-Instruct capabilities. You can obtain an API access token on the token management page after registration and verification.
Model Name Context Type GPU Status Link
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Private server

Rent your own physically dedicated instance with hourly or long-term monthly billing.

We recommend deploying private instances in the following scenarios:

  • maximize endpoint performance,
  • enable full context for long sequences,
  • ensure top-tier security for data processing in an isolated, dedicated environment,
  • use custom weights, such as fine-tuned models or LoRA adapters.

Recommended server configurations for hosting Qwen3-Coder-30B-A3B-Instruct

Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa10-2.16.64.160
262,144.0
tensor
2 $0.93 1.000 Launch
teslat4-4.16.64.160
262,144.0
tensor
4 $0.96 1.392 Launch
rtxa5000-2.16.64.160.nvlink
262,144.0
tensor
2 $1.23 1.000 Launch
teslaa2-4.32.128.160
262,144.0
tensor
4 $1.26 1.392 Launch
rtx3090-2.16.64.160
262,144.0
tensor
2 $1.56 1.000 Launch
rtx4090-2.16.64.160
262,144.0
tensor
2 $1.92 1.000 Launch
teslav100-2.16.64.240
262,144.0
tensor
2 $2.22 1.600 Launch
teslaa100-1.16.64.160
262,144.0
1 $2.37 2.304 Launch
rtx5090-2.16.64.160
262,144.0
tensor
2 $2.93 1.600 Launch
h100-1.16.64.160
262,144.0
1 $3.83 2.304 Launch
h100nvl-1.16.96.160
262,144.0
1 $4.11 2.829 Launch
h200-1.16.128.160
262,144.0
1 $4.74 4.592 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa10-3.16.96.160
262,144.0
pipeline
3 $1.34 1.204 Launch
teslaa10-4.16.64.160
262,144.0
tensor
4 $1.62 2.000 Launch
teslaa2-6.32.128.160
262,144.0
pipeline
6 $1.65 1.791 Launch
teslav100-2.16.64.240
262,144.0
tensor
2 $2.22 1.008 Launch
rtx3090-3.16.96.160
262,144.0
pipeline
3 $2.29 1.204 Launch
rtxa5000-4.16.128.160.nvlink
262,144.0
tensor
4 $2.34 2.000 Launch
teslaa100-1.16.64.160
262,144.0
1 $2.37 1.712 Launch
rtx4090-3.16.96.160
262,144.0
pipeline
3 $2.83 1.204 Launch
rtx3090-4.16.64.160
262,144.0
tensor
4 $2.89 2.000 Launch
rtx5090-2.16.64.160
262,144.0
tensor
2 $2.93 1.008 Launch
rtx4090-4.16.64.160
262,144.0
tensor
4 $3.60 2.000 Launch
h100-1.16.64.160
262,144.0
1 $3.83 1.712 Launch
h100nvl-1.16.96.160
262,144.0
1 $4.11 2.237 Launch
h200-1.16.128.160
262,144.0
1 $4.74 4.000 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
rtxa5000-6.24.192.160.nvlink
262,144.0
pipeline
6 $3.50 2.408 Launch
h100nvl-1.16.96.160
262,144.0
1 $4.11 1.054 Launch
teslav100-4.32.96.160
262,144.0
tensor
4 $4.35 2.016 Launch
teslaa100-2.24.96.160.nvlink
262,144.0
tensor
2 $4.61 3.425 Launch
rtxa5000-8.24.256.160.nvlink
262,144.0
tensor
8 $4.61 4.000 Launch
h200-1.16.128.160
262,144.0
1 $4.74 2.816 Launch
rtx5090-4.16.128.160
262,144.0
tensor
4 $5.74 2.016 Launch
rtx4090-6.44.256.160
262,144.0
pipeline
6 $5.83 2.408 Launch
rtx4090-8.44.256.160
262,144.0
tensor
8 $7.51 4.000 Launch
h100-2.24.256.160
262,144.0
tensor
2 $7.84 3.425 Launch

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