Qwen3-Coder-30B-A3B-Instruct

coding

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
There are no public endpoints for this model yet.

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
teslaa2-4.32.128.160
262,144.0
tensor
4 $1.26 1.194 Launch
teslaa10-3.16.96.160
262,144.0
pipeline
3 $1.34 1.627 Launch
teslaa10-4.12.48.160
262,144.0
tensor
4 $1.57 2.405 Launch
rtx3090-3.16.96.160
262,144.0
pipeline
3 $2.29 138.17 1.761 Launch
teslaa100-1.16.64.160
262,144.0
1 $2.37 281.00 2.274 Launch
rtx4090-3.16.96.160
262,144.0
pipeline
3 $2.83 1.744 Launch
rtx3090-4.16.64.160
262,144.0
tensor
4 $2.89 2.568 Launch
rtx5090-2.16.64.160
262,144.0
tensor
2 $2.93 1.528 Launch
rtx3090-4.16.128.160.nvlink
262,144.0
tensor
4 $3.01 2.568 Launch
rtx4090-4.16.64.160
262,144.0
tensor
4 $3.60 2.561 Launch
h100-1.16.64.160
262,144.0
1 $3.83 152.96 2.244 Launch
h100nvl-1.16.96.160
262,144.0
1 $4.11 202.68 2.777 Launch
teslaa100-2.24.96.160.nvlink
262,144.0
tensor
2 $4.61 5.200 Launch
h200-1.16.128.160
262,144.0
1 $4.74 4.568 Launch
h200-2.24.256.160.nvlink
262,144.0
tensor
2 $9.40 9.842 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa10-3.16.96.160
262,144.0
pipeline
3 $1.34 1.138 Launch
teslaa10-4.16.64.160
262,144.0
tensor
4 $1.62 1.962 Launch
teslaa2-6.32.128.160
262,144.0
pipeline
6 $1.65 1.792 Launch
rtx3090-3.16.96.160
262,144.0
pipeline
3 $2.29 154.00 1.261 Launch
teslaa100-1.16.64.160
262,144.0
1 $2.37 156.50 1.755 Launch
rtx4090-3.16.96.160
262,144.0
pipeline
3 $2.83 148.67 1.269 Launch
rtx3090-4.16.64.160
262,144.0
tensor
4 $2.89 2.124 Launch
rtx3090-4.16.128.160.nvlink
262,144.0
tensor
4 $3.01 2.124 Launch
rtx4090-4.16.64.160
262,144.0
tensor
4 $3.60 2.118 Launch
h100-1.16.64.160
262,144.0
1 $3.83 147.58 1.664 Launch
h100nvl-1.16.96.160
262,144.0
1 $4.11 196.88 2.276 Launch
rtx5090-3.16.96.160
262,144.0
pipeline
3 $4.34 2.156 Launch
teslaa100-2.24.96.160.nvlink
262,144.0
tensor
2 $4.61 4.666 Launch
h200-1.16.128.160
262,144.0
1 $4.74 3.988 Launch
rtx5090-4.16.128.160
262,144.0
tensor
4 $5.74 3.319 Launch
h200-2.24.256.160.nvlink
262,144.0
tensor
2 $9.40 9.308 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
h100nvl-1.16.96.160
262,144.0
1 $4.11 196.26 1.113 Launch
teslaa100-2.24.96.160.nvlink
262,144.0
tensor
2 $4.61 142.52 3.564 Launch
h200-1.16.128.160
262,144.0
1 $4.74 2.904 Launch
teslaa100-2.24.256.160
262,144.0
tensor
2 $4.93 142.52 3.564 Launch
rtx5090-4.16.128.160
262,144.0
tensor
4 $5.74 2.103 Launch
rtx4090-6.44.256.160
262,144.0
pipeline
6 $5.83 2.540 Launch
dedicated-rtx3090-8.64.128.960-1
262,144.0
tensor
8 $6.04 2.095 Launch
rtx4090-8.44.256.160
262,144.0
tensor
8 $7.51 2.089 Launch
h100-2.24.256.160
262,144.0
tensor
2 $7.84 148.03 3.570 Launch
h200-2.24.256.160.nvlink
262,144.0
tensor
2 $9.40 8.180 Launch

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