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.
| Model Name | Context | Type | GPU | Status | Link |
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There are no public endpoints for this model yet.
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We recommend creating private endpoints when you need to:
| Name | GPU | TPS | Max Concurrency | |||
|---|---|---|---|---|---|---|
80,000.0 |
1 | $0.53 | 1.313 | Launch | ||
80,000.0 tensor |
2 | $0.57 | 1.696 | Launch | ||
80,000.0 |
1 | $0.83 | 1.402 | Launch | ||
80,000.0 |
1 | $1.02 | 1.399 | Launch | ||
262,144.0 tensor |
4 | $1.26 | 118.23 | 1.155 | Launch | |
262,144.0 pipeline |
3 | $1.34 | 1.442 | Launch | ||
80,000.0 pipeline |
3 | $1.43 | 1.487 | Launch | ||
80,000.0 tensor |
2 | $1.56 | 3.197 | Launch | ||
262,144.0 tensor |
4 | $1.57 | 1.963 | Launch | ||
80,000.0 |
1 | $1.59 | 2.055 | Launch | ||
80,000.0 tensor |
4 | $1.82 | 2.113 | Launch | ||
262,144.0 pipeline |
3 | $2.29 | 157.68 | 1.631 | Launch | |
262,144.0 |
1 | $2.37 | 185.47 | 1.884 | Launch | |
262,144.0 pipeline |
3 | $2.83 | 143.33 | 1.627 | Launch | |
262,144.0 tensor |
4 | $2.89 | 2.071 | Launch | ||
262,144.0 tensor |
2 | $2.93 | 140.62 | 1.373 | Launch | |
262,144.0 tensor |
4 | $3.01 | 2.071 | Launch | ||
262,144.0 tensor |
4 | $3.60 | 2.067 | Launch | ||
262,144.0 |
1 | $3.83 | 172.66 | 1.843 | Launch | |
262,144.0 |
1 | $4.11 | 186.02 | 2.205 | Launch | |
262,144.0 tensor |
2 | $4.61 | 3.822 | Launch | ||
262,144.0 |
1 | $4.74 | 3.399 | Launch | ||
262,144.0 tensor |
2 | $9.40 | 6.917 | Launch | ||
| Name | GPU | TPS | Max Concurrency | |||
|---|---|---|---|---|---|---|
80,000.0 |
1 | $0.53 | 1.291 | Launch | ||
80,000.0 tensor |
2 | $0.57 | 1.648 | Launch | ||
80,000.0 |
1 | $0.83 | 1.379 | Launch | ||
80,000.0 |
1 | $1.02 | 1.376 | Launch | ||
262,144.0 tensor |
4 | $1.26 | 1.125 | Launch | ||
262,144.0 pipeline |
3 | $1.34 | 56.23 | 1.448 | Launch | |
80,000.0 pipeline |
3 | $1.43 | 1.413 | Launch | ||
80,000.0 tensor |
2 | $1.56 | 3.145 | Launch | ||
262,144.0 tensor |
4 | $1.57 | 1.932 | Launch | ||
80,000.0 |
1 | $1.59 | 2.032 | Launch | ||
80,000.0 tensor |
4 | $1.82 | 2.014 | Launch | ||
262,144.0 pipeline |
3 | $2.29 | 102.57 | 1.530 | Launch | |
262,144.0 |
1 | $2.37 | 131.35 | 1.844 | Launch | |
262,144.0 pipeline |
3 | $2.83 | 66.13 | 1.526 | Launch | |
262,144.0 tensor |
4 | $2.89 | 2.041 | Launch | ||
262,144.0 tensor |
2 | $2.93 | 1.359 | Launch | ||
262,144.0 tensor |
4 | $3.01 | 2.041 | Launch | ||
262,144.0 tensor |
4 | $3.60 | 2.036 | Launch | ||
262,144.0 |
1 | $3.83 | 136.71 | 1.724 | Launch | |
262,144.0 |
1 | $4.11 | 2.198 | Launch | ||
262,144.0 tensor |
2 | $4.61 | 3.807 | Launch | ||
262,144.0 |
1 | $4.74 | 3.392 | Launch | ||
262,144.0 tensor |
2 | $9.40 | 6.902 | Launch | ||
| Name | GPU | TPS | Max Concurrency | |||
|---|---|---|---|---|---|---|
80,000.0 tensor |
2 | $0.57 | 1.345 | Launch | ||
80,000.0 |
1 | $0.83 | 1.076 | Launch | ||
80,000.0 tensor |
2 | $0.93 | 2.668 | Launch | ||
80,000.0 |
1 | $1.02 | 1.073 | Launch | ||
262,144.0 tensor |
4 | $1.26 | 1.028 | Launch | ||
262,144.0 pipeline |
3 | $1.34 | 46.80 | 1.355 | Launch | |
80,000.0 pipeline |
3 | $1.43 | 1.110 | Launch | ||
80,000.0 tensor |
2 | $1.56 | 2.845 | Launch | ||
262,144.0 tensor |
4 | $1.57 | 1.840 | Launch | ||
80,000.0 |
1 | $1.59 | 1.729 | Launch | ||
80,000.0 tensor |
4 | $1.82 | 1.711 | Launch | ||
262,144.0 pipeline |
3 | $2.29 | 68.87 | 1.436 | Launch | |
262,144.0 |
1 | $2.37 | 102.86 | 1.752 | Launch | |
262,144.0 pipeline |
3 | $2.83 | 79.88 | 1.432 | Launch | |
262,144.0 tensor |
4 | $2.89 | 1.948 | Launch | ||
262,144.0 tensor |
2 | $2.93 | 1.267 | Launch | ||
262,144.0 tensor |
4 | $3.01 | 1.948 | Launch | ||
262,144.0 tensor |
4 | $3.60 | 1.944 | Launch | ||
262,144.0 |
1 | $3.83 | 118.70 | 1.631 | Launch | |
262,144.0 |
1 | $4.11 | 2.105 | Launch | ||
262,144.0 tensor |
2 | $4.61 | 3.715 | Launch | ||
262,144.0 |
1 | $4.74 | 3.299 | Launch | ||
262,144.0 tensor |
2 | $9.40 | 6.810 | Launch | ||
Contact our dedicated neural networks support team at support@immers.cloud or send your request to the sales department at sale@immers.cloud.