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 1.356 Launch
teslat4-2.16.32.160
80,000.0
tensor
2 $0.54 1.683 Launch
teslaa2-2.16.32.160
80,000.0
tensor
2 $0.57 1.690 Launch
rtx2080ti-2.12.64.160
80,000.0
tensor
2 $0.69 1.013 Launch
rtx3090-1.16.24.160
80,000.0
1 $0.83 1.445 Launch
teslat4-4.16.64.160
262,144.0
tensor
4 $0.96 1.119 Launch
rtx4090-1.16.32.160
80,000.0
1 $1.02 1.441 Launch
rtxa5000-2.16.64.160.nvlink
80,000.0
tensor
2 $1.23 3.013 Launch
teslaa2-4.32.128.160
262,144.0
tensor
4 $1.26 1.123 Launch
teslaa10-3.16.96.160
262,144.0
pipeline
3 $1.34 1.425 Launch
rtx3080-3.16.64.160
80,000.0
pipeline
3 $1.43 1.431 Launch
teslaa10-4.12.48.160
262,144.0
tensor
4 $1.57 1.930 Launch
rtx5090-1.16.64.160
80,000.0
1 $1.59 2.097 Launch
rtx3080-4.16.64.160
80,000.0
tensor
4 $1.82 2.008 Launch
rtx3090-3.16.96.160
262,144.0
pipeline
3 $2.29 1.506 Launch
rtxa5000-4.16.128.160.nvlink
262,144.0
tensor
4 $2.34 1.930 Launch
teslaa100-1.16.64.160
262,144.0
1 $2.37 93.920 1.864 Launch
rtx4090-3.16.96.160
262,144.0
pipeline
3 $2.83 1.503 Launch
rtx3090-4.16.64.160
262,144.0
tensor
4 $2.89 2.039 Launch
rtx5090-2.16.64.160
262,144.0
tensor
2 $2.93 1.372 Launch
rtx4090-4.16.64.160
262,144.0
tensor
4 $3.60 2.035 Launch
h100-1.16.64.160
262,144.0
1 $3.83 107.330 1.862 Launch
h100nvl-1.16.96.160
262,144.0
1 $4.11 2.218 Launch
teslaa100-2.24.96.160.nvlink
262,144.0
tensor
2 $4.61 3.820 Launch
h200-1.16.128.160
262,144.0
1 $4.74 3.411 Launch
h200-2.24.256.160.nvlink
262,144.0
tensor
2 $9.40 6.915 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa10-1.16.32.160
80,000.0
1 $0.53 1.217 Launch
teslat4-2.16.32.160
80,000.0
tensor
2 $0.54 1.543 Launch
teslaa2-2.16.32.160
80,000.0
tensor
2 $0.57 1.550 Launch
rtx3090-1.16.24.160
80,000.0
1 $0.83 1.305 Launch
rtx2080ti-3.12.24.120
80,000.0
pipeline
3 $0.84 1.530 Launch
teslat4-4.16.64.160
262,144.0
tensor
4 $0.96 1.076 Launch
rtx4090-1.16.32.160
80,000.0
1 $1.02 1.302 Launch
rtx2080ti-4.16.32.160
80,000.0
tensor
4 $1.12 2.187 Launch
rtxa5000-2.16.64.160.nvlink
80,000.0
tensor
2 $1.23 2.873 Launch
teslaa2-4.32.128.160
262,144.0
tensor
4 $1.26 1.080 Launch
teslaa10-3.16.96.160
262,144.0
pipeline
3 $1.34 56.230 1.382 Launch
rtx3080-3.16.64.160
80,000.0
pipeline
3 $1.43 1.292 Launch
teslaa10-4.12.48.160
262,144.0
tensor
4 $1.57 1.888 Launch
rtx5090-1.16.64.160
80,000.0
1 $1.59 1.958 Launch
rtx3080-4.16.64.160
80,000.0
tensor
4 $1.82 1.869 Launch
rtx3090-3.16.96.160
262,144.0
pipeline
3 $2.29 1.464 Launch
rtxa5000-4.16.128.160.nvlink
262,144.0
tensor
4 $2.34 1.888 Launch
teslaa100-1.16.64.160
262,144.0
1 $2.37 131.350 1.822 Launch
rtx4090-3.16.96.160
262,144.0
pipeline
3 $2.83 1.461 Launch
rtx3090-4.16.64.160
262,144.0
tensor
4 $2.89 1.996 Launch
rtx5090-2.16.64.160
262,144.0
tensor
2 $2.93 1.329 Launch
rtx4090-4.16.64.160
262,144.0
tensor
4 $3.60 1.992 Launch
h100-1.16.64.160
262,144.0
1 $3.83 136.710 1.820 Launch
h100nvl-1.16.96.160
262,144.0
1 $4.11 2.175 Launch
teslaa100-2.24.96.160.nvlink
262,144.0
tensor
2 $4.61 3.777 Launch
h200-1.16.128.160
262,144.0
1 $4.74 3.369 Launch
h200-2.24.256.160.nvlink
262,144.0
tensor
2 $9.40 6.872 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslat4-2.16.32.160
80,000.0
tensor
2 $0.54 1.301 Launch
teslaa2-2.16.32.160
80,000.0
tensor
2 $0.57 1.308 Launch
rtx3090-1.16.24.160
80,000.0
1 $0.83 1.063 Launch
rtx2080ti-3.12.24.120
80,000.0
pipeline
3 $0.84 1.288 Launch
teslaa10-2.16.64.160
80,000.0
tensor
2 $0.93 2.631 Launch
teslat4-4.16.64.160
262,144.0
tensor
4 $0.96 1.002 Launch
rtx4090-1.16.32.160
80,000.0
1 $1.02 1.060 Launch
rtx2080ti-4.16.32.160
80,000.0
tensor
4 $1.12 1.945 Launch
rtxa5000-2.16.64.160.nvlink
80,000.0
tensor
2 $1.23 2.631 Launch
teslaa2-4.32.128.160
262,144.0
tensor
4 $1.26 1.007 Launch
teslaa10-3.16.96.160
262,144.0
pipeline
3 $1.34 46.800 1.309 Launch
rtx3080-3.16.64.160
80,000.0
pipeline
3 $1.43 1.050 Launch
teslaa10-4.12.48.160
262,144.0
tensor
4 $1.57 1.814 Launch
rtx5090-1.16.64.160
80,000.0
1 $1.59 1.716 Launch
rtx3080-4.16.64.160
80,000.0
tensor
4 $1.82 1.627 Launch
rtx3090-3.16.96.160
262,144.0
pipeline
3 $2.29 1.390 Launch
rtxa5000-4.16.128.160.nvlink
262,144.0
tensor
4 $2.34 1.814 Launch
teslaa100-1.16.64.160
262,144.0
1 $2.37 102.860 1.748 Launch
rtx4090-3.16.96.160
262,144.0
pipeline
3 $2.83 1.387 Launch
rtx3090-4.16.64.160
262,144.0
tensor
4 $2.89 1.922 Launch
rtx5090-2.16.64.160
262,144.0
tensor
2 $2.93 1.255 Launch
rtx4090-4.16.64.160
262,144.0
tensor
4 $3.60 1.918 Launch
h100-1.16.64.160
262,144.0
1 $3.83 118.700 1.746 Launch
h100nvl-1.16.96.160
262,144.0
1 $4.11 2.101 Launch
teslaa100-2.24.96.160.nvlink
262,144.0
tensor
2 $4.61 3.704 Launch
h200-1.16.128.160
262,144.0
1 $4.74 3.295 Launch
h200-2.24.256.160.nvlink
262,144.0
tensor
2 $9.40 6.798 Launch

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