aquif-3.5
The aquif-3.5 series is the successor to aquif-3, featuring a simplified naming scheme, expanded Mixture of Experts (MoE) options, and across-the-board performance improvements. This release streamlines model selection while delivering enhanced capabilities across reasoning, multilingual support, and general intelligence tasks.
Model Repository Links
| Model | HuggingFace Repository |
|---|---|
| aquif-3.5-A0.6B-Preview | aquiffoo/aquif-3.5-A0.6B-Preview |
| aquif-3.5-3B | aquiffoo/aquif-3.5-3B |
| aquif-3.5-7B | aquiffoo/aquif-3.5-7B |
| aquif-3.5-8B-Think | aquiffoo/aquif-3.5-8B-Think |
| aquif-3.5-A4B-Think | aquiffoo/aquif-3.5-A4B-Think |
Model Overview
| Model | Size (B) | Active Params (B) | Reasoning | MoE | Multilingual | MMLU | Context Window |
|---|---|---|---|---|---|---|---|
| aquif-3.5-A0.6B | 2.61 | 0.6 | ❌ | ✅ | ✅ | 60.5% | 4k |
| aquif-3.5-3B | 2.67 | 2.67 | ❌ | ❌ | ✅ | 70.2% | 32k |
| aquif-3.5-7B | 7.3 | 7.3 | ❌ | ❌ | ✅ | 78.5% | 16k |
| aquif-3.5-8B-Think | 8.2 | 8.2 | ✅ | ❌ | ✅ | 81.1% | 40k |
| aquif-3.5-A4B-Think | 12 | 4 | ✅ | ✅ | ✅ | 86.9% | 128k |
Model Details
aquif-3.5-A0.6B (Experimental MoE)
An experimental small-scale Mixture of Experts model designed for multilingual applications with minimal computational overhead. Despite its compact active parameter count, it demonstrates competitive performance against larger dense models.
Performance Comparison:
| Metric | aquif-3.5 (2.6B A0.6B) | Qwen3 (0.8B) | LFM2 (0.7B) | aquif-3 (0.4B) |
|---|---|---|---|---|
| MMLU | 60.5 | 44.9 | 49.9 | 55.6 |
| GPQA | 30.2 | 22.1 | 28.5 | 28.5 |
| GSM8K | 50.7 | 36.5 | 46.4 | 52.1 |
| HumanEval | 45.2 | 36.0 | 40.0 | 37.4 |
| Average | 46.7 | 34.9 | 41.2 | 43.4 |
aquif-3.5-3B (State-of-the-Art Dense)
The new standard for small dense models, offering optimal performance-per-parameter efficiency for general-purpose applications.
Performance Comparison:
| Metric | aquif-3.5 (2.7B) | EXAONE 3.5 (2.4B) | Qwen3 (4B) | Gemma 3 (4B) | Phi-4-mini (3.8B) | Apriel-5B-Instruct (4.8B) | aquif-3 (3.2B) |
|---|---|---|---|---|---|---|---|
| MMLU (General Knowledge) | 70.2 | 60.4 | 70.4 | 59.6 | 67.3 | 64.6 | 67.5 |
| GPQA Diamond (Science) | 35.8 | 28.4 | 39.3 | 30.9 | 25.2 | 28.4 | 36.1 |
| LiveCodeBench (Coding) | 23.1 | 12.5 | 21.3 | 11.2 | 10.4 | 11.6 | 15.4 |
| IFEval (Instruction Following) | 78.9 | 73.6 | 71.2 | 80.2 | 68.6 | 80.8 | 78.9 |
| AIME 2025 (Competition Math) | 13.4 | 4.5 | 9.8 | 12.7 | 5.3 | 4.3 | 9.6 |
| Average | 44.3 | 35.9 | 42.4 | 38.9 | 35.4 | 37.9 | 41.5 |
aquif-3.5-7B (Multilingual Long Context)
A Qwen-based architecture optimized for multilingual applications with extended context capabilities, delivering state-of-the-art performance in its size class.
Performance Comparison:
| Metric | aquif-3.5 (7.3B) | EXAONE 3.5 (7.8B) | Qwen3 (8.2B) | Gemma 3 (12B) | Llama 3.1 (8B) | Kanana 1.5 (8B) | aquif-3 (3.2B) |
|---|---|---|---|---|---|---|---|
| MMLU (General Knowledge) | 78.5 | 72.2 | 82.9 | 74.5 | 69.2 | 68.8 | 67.5 |
| GPQA Diamond (Science) | 42.3 | 39.4 | 39.3 | 40.9 | 32.8 | 37.5 | 36.1 |
| LiveCodeBench (Coding) | 21.3 | 18.0 | 23.9 | 13.7 | 10.8 | 16.5 | 15.4 |
| IFEval (Instruction Following) | 85.6 | 82.6 | 85.4 | 80.2 | 75.0 | 80.1 | 78.9 |
| AIME 2025 (Competition Math) | 23.4 | 18.3 | 20.9 | 18.8 | 2.7 | 13.4 | 9.6 |
| Average | 50.2 | 46.1 | 50.4 | 45.6 | 38.1 | 43.3 | 41.5 |
aquif-3.5-8B-Think & aquif-3.5-A4B-Think (Reasoning Models)
Advanced reasoning-capable models designed for complex problem-solving tasks. The A4B variant leverages MoE architecture for enhanced efficiency while maintaining superior reasoning performance.
Performance Comparison:
| Metric | aquif-3.5 (12B A4B) | aquif-3.5 (8B) | Qwen3 Thinking 2507 (31B A3B) | gpt-oss-20b (21B A4B) | Nemotron Nano v2 (9B) | Solar Pro 2 |
|---|---|---|---|---|---|---|
| MMLU-Pro | 78.5 | 78.1 | 80.5 | 73.6 | 74.2 | 80.5 |
| GPQA Diamond | 70.8 | 66.8 | 70.7 | 61.7 | 64.0 | 68.7 |
| AIME 2025 | 84.4 | 81.4 | 56.3 | 61.7 | 69.7 | 61.3 |
| LiveCodeBench | 66.1 | 61.5 | 70.7 | 72.1 | 71.1 | 61.6 |
| Humanity's Last Exam | 8.9 | 8.2 | 9.8 | 8.5 | 6.5 | 7.0 |
| TAU-Bench v2 (avg) | 43.7 | 36.8 | 35.7 | 43.2 | 34.9 | 38.7 |
| Average | 58.7 | 55.5 | 54.0 | 53.5 | 53.4 | 53.0 |
Key Improvements Over aquif-3
- Simplified Naming: Clear size-based nomenclature for easier model selection
- Enhanced MoE Support: Multiple MoE configurations across different model sizes
- Reasoning Capabilities: Dedicated thinking models for complex problem-solving
- Extended Context: Up to 128k context window for long-form applications
- Multilingual by Default: Native multilingual support across all variants
- Performance Gains: 5-15% improvement across benchmarks compared to aquif-3
Usage Recommendations
- aquif-3.5-A0.6B: Experimental applications, resource-constrained environments
- aquif-3.5-3B: General-purpose applications, balanced performance/efficiency
- aquif-3.5-7B: Multilingual applications, long-context tasks
- aquif-3.5-8B-Think: Complex reasoning, scientific analysis
- aquif-3.5-A4B-Think: Advanced reasoning with efficiency optimization
Technical Specifications
All models support:
- BF16 and FP16 precision
- Standard transformer architecture optimizations
- Efficient attention mechanisms
- Multi-head attention with optimized KV caching
Acknowledgements
- Qwen Team: Base architecture for 7B, 8B, and 12B-A4B models
- Meta Llama Team: Base architecture for 3B and 2.6B-A0.6B models
- Hugging Face: Model hosting infrastructure and training libraries
License
This project is released under the Apache 2.0 License. See LICENSE file for details.
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