Qwen3-4B-Instruct-2507

Qwen3-4B-Instruct-2507 is a revolutionary model built on an innovative architecture with 4.02 billion parameters (including embeddings), 36 transformer hidden layers, and Group Query Attention (GQA) using 32 attention heads for queries and 8 for keys and values—providing an optimal balance between performance and memory efficiency. The model is optimized from the hybrid Qwen3-4B base to operate exclusively in non-thinking mode, completely eliminating the generation of <think></think> blocks, thereby maximizing query processing speed. Native support for a context length of 262,144 tokens enables efficient handling of large documents, extended conversations, and complex multi-step tasks without degradation in information processing quality.

Architectural innovations include an advanced user-preference alignment system, delivering more relevant and useful responses, along with significant improvements in multilingual content processing.

The model demonstrates outstanding results on key benchmarks, outperforming the proprietary GPT-4.1-nano across all major metrics: MMLU-Pro (69.6 vs 62.8), GPQA (62.0 vs 50.3), and particularly impressive scores on ZebraLogic (80.2 vs 14.8) and creative content generation, where it achieves 83.5 (vs 72.7). The model excels in instruction-following tasks, achieving 83.4% on IFEval and 43.4 on Arena-Hard v2. It also performs exceptionally well in agent-based tasks and tool usage, showing strong results on the BFCL-v3 (61.9) and TAU benchmark suites, making it ideal for integration into automated systems.

Qwen3-4B-Instruct-2507 is highly suitable for business process automation, including customer service via intelligent chatbots, document processing and analysis, report generation, and personalized recommendations. It is effective in creating and localizing SEO-optimized marketing content, product descriptions, social media posts, and more. Thanks to seamless API integration, the model can be deployed for automation within CRM and ERP systems, as well as for any tasks requiring intelligent routing and fast, real-time query processing.


Announce Date: 07.08.2025
Parameters: 5B
Context: 263K
Layers: 36
Attention Type: Full or Sliding Window Attention
Developer: Qwen
Transformers Version: 4.51.0
License: Apache 2.0

Public endpoint

Use our pre-built public endpoints for free to test inference and explore Qwen3-4B-Instruct-2507 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 Qwen3-4B-Instruct-2507

Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa10-1.16.32.160
80,000.0
1 $0.53 1.292 Launch
teslat4-2.16.32.160
80,000.0
tensor
2 $0.54 1.546 Launch
teslaa2-2.16.32.160
80,000.0
tensor
2 $0.57 1.553 Launch
rtx3090-1.16.24.160
80,000.0
1 $0.83 1.381 Launch
rtx2080ti-3.12.24.120
80,000.0
pipeline
3 $0.84 1.460 Launch
teslat4-4.16.64.160
262,144.0
tensor
4 $0.96 1.033 Launch
rtx4090-1.16.32.160
80,000.0
1 $1.02 1.377 Launch
rtx2080ti-4.16.32.160
80,000.0
tensor
4 $1.12 2.044 Launch
rtxa5000-2.16.64.160.nvlink
80,000.0
tensor
2 $1.23 2.876 Launch
teslaa2-4.32.128.160
262,144.0
tensor
4 $1.26 1.037 Launch
teslaa10-3.16.96.160
262,144.0
pipeline
3 $1.34 1.361 Launch
rtx3080-3.16.64.160
80,000.0
pipeline
3 $1.43 1.221 Launch
teslaa10-4.12.48.160
262,144.0
tensor
4 $1.57 1.844 Launch
rtx5090-1.16.64.160
80,000.0
1 $1.59 2.033 Launch
rtx3080-4.16.64.160
80,000.0
tensor
4 $1.82 1.726 Launch
rtx3090-3.16.96.160
262,144.0
pipeline
3 $2.29 1.442 Launch
rtxa5000-4.16.128.160.nvlink
262,144.0
tensor
4 $2.34 1.844 Launch
teslaa100-1.16.64.160
262,144.0
1 $2.37 93.920 1.845 Launch
rtx4090-3.16.96.160
262,144.0
pipeline
3 $2.83 1.439 Launch
rtx3090-4.16.64.160
262,144.0
tensor
4 $2.89 1.953 Launch
rtx5090-2.16.64.160
262,144.0
tensor
2 $2.93 1.330 Launch
rtx4090-4.16.64.160
262,144.0
tensor
4 $3.60 1.949 Launch
h100-1.16.64.160
262,144.0
1 $3.83 107.330 1.843 Launch
h100nvl-1.16.96.160
262,144.0
1 $4.11 186.020 2.198 Launch
teslaa100-2.24.96.160.nvlink
262,144.0
tensor
2 $4.61 3.778 Launch
h200-1.16.128.160
262,144.0
1 $4.74 3.392 Launch
h200-2.24.256.160.nvlink
262,144.0
tensor
2 $9.40 6.873 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa10-1.16.32.160
80,000.0
1 $0.53 1.144 Launch
teslat4-2.16.32.160
80,000.0
tensor
2 $0.54 1.398 Launch
teslaa2-2.16.32.160
80,000.0
tensor
2 $0.57 1.405 Launch
rtx3090-1.16.24.160
80,000.0
1 $0.83 1.233 Launch
rtx2080ti-3.12.24.120
80,000.0
pipeline
3 $0.84 1.312 Launch
rtx4090-1.16.32.160
80,000.0
1 $1.02 1.229 Launch
rtx2080ti-4.16.32.160
80,000.0
tensor
4 $1.12 1.896 Launch
rtxa5000-2.16.64.160.nvlink
80,000.0
tensor
2 $1.23 2.728 Launch
teslaa10-3.16.96.160
262,144.0
pipeline
3 $1.34 56.230 1.316 Launch
rtx3080-3.16.64.160
80,000.0
pipeline
3 $1.43 1.073 Launch
teslaa10-4.12.48.160
262,144.0
tensor
4 $1.57 1.799 Launch
rtx5090-1.16.64.160
80,000.0
1 $1.59 1.885 Launch
teslaa2-6.32.128.160
262,144.0
pipeline
6 $1.65 1.554 Launch
rtx3080-4.16.64.160
80,000.0
tensor
4 $1.82 1.578 Launch
rtx3090-3.16.96.160
262,144.0
pipeline
3 $2.29 1.397 Launch
rtxa5000-4.16.128.160.nvlink
262,144.0
tensor
4 $2.34 1.799 Launch
teslaa100-1.16.64.160
262,144.0
1 $2.37 131.350 1.799 Launch
rtx4090-3.16.96.160
262,144.0
pipeline
3 $2.83 1.394 Launch
rtx3090-4.16.64.160
262,144.0
tensor
4 $2.89 1.907 Launch
rtx5090-2.16.64.160
262,144.0
tensor
2 $2.93 1.285 Launch
rtx4090-4.16.64.160
262,144.0
tensor
4 $3.60 1.903 Launch
h100-1.16.64.160
262,144.0
1 $3.83 136.710 1.798 Launch
h100nvl-1.16.96.160
262,144.0
1 $4.11 2.153 Launch
teslaa100-2.24.96.160.nvlink
262,144.0
tensor
2 $4.61 3.733 Launch
h200-1.16.128.160
262,144.0
1 $4.74 3.347 Launch
h200-2.24.256.160.nvlink
262,144.0
tensor
2 $9.40 6.828 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslat4-2.16.32.160
80,000.0
tensor
2 $0.54 1.156 Launch
teslaa2-2.16.32.160
80,000.0
tensor
2 $0.57 1.162 Launch
rtx2080ti-3.12.24.120
80,000.0
pipeline
3 $0.84 1.070 Launch
teslaa10-2.16.64.160
80,000.0
tensor
2 $0.93 2.486 Launch
rtx2080ti-4.16.32.160
80,000.0
tensor
4 $1.12 1.654 Launch
rtxa5000-2.16.64.160.nvlink
80,000.0
tensor
2 $1.23 2.486 Launch
teslaa10-3.16.96.160
262,144.0
pipeline
3 $1.34 46.800 1.242 Launch
rtx3090-2.16.64.160
80,000.0
tensor
2 $1.56 2.663 Launch
teslaa10-4.12.48.160
262,144.0
tensor
4 $1.57 1.725 Launch
rtx5090-1.16.64.160
80,000.0
1 $1.59 1.643 Launch
teslaa2-6.32.128.160
262,144.0
pipeline
6 $1.65 1.481 Launch
rtx3080-4.16.64.160
80,000.0
tensor
4 $1.82 1.336 Launch
rtx4090-2.16.64.160
80,000.0
tensor
2 $1.92 2.656 Launch
rtx3090-3.16.96.160
262,144.0
pipeline
3 $2.29 1.323 Launch
rtxa5000-4.16.128.160.nvlink
262,144.0
tensor
4 $2.34 1.725 Launch
teslaa100-1.16.64.160
262,144.0
1 $2.37 102.860 1.725 Launch
rtx4090-3.16.96.160
262,144.0
pipeline
3 $2.83 1.320 Launch
rtx3090-4.16.64.160
262,144.0
tensor
4 $2.89 1.834 Launch
rtx5090-2.16.64.160
262,144.0
tensor
2 $2.93 1.211 Launch
rtx4090-4.16.64.160
262,144.0
tensor
4 $3.60 1.829 Launch
h100-1.16.64.160
262,144.0
1 $3.83 118.700 1.724 Launch
h100nvl-1.16.96.160
262,144.0
1 $4.11 2.079 Launch
teslaa100-2.24.96.160.nvlink
262,144.0
tensor
2 $4.61 3.659 Launch
h200-1.16.128.160
262,144.0
1 $4.74 3.273 Launch
h200-2.24.256.160.nvlink
262,144.0
tensor
2 $9.40 6.754 Launch

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