---
tags:
- compressed-tensors
- ai
- indian
license: mit
license_name: modified-mit
library_name: transformers
pipeline_tag: image-text-to-image
language:
- en
- hi
---
🇮🇳 Kalki 2.5
India's First Fully Agentic 1T Parameter AI Model
---
## 🚀 1. Model Introduction
**Kalki 2.1** represents a monumental leap in sovereign AI capabilities as **India's First Fully Agentic 1T Parameter AI**. Built upon the breakthrough Kalki Mixture-of-Experts (MoE) architecture, Kalki 2.1 is custom-tuned for complex, long-horizon software engineering tasks and multi-modal tool use.
Kalki 2.1 features substantial optimizations over predecessor models:
* **Unprecedented Scale**: A 1-Trillion parameter Mixture-of-Experts model, activating 32 Billion parameters per token.
* **Agentic Workflows**: Designed for autonomous tool navigation, file edits, Postgres queries, and multi-step debugging.
* **Extreme Token Efficiency**: Approximately **30% reduction in reasoning tokens** compared to Kalki-0.6, delivering much faster completion speeds.
* **Multimodal Integration**: Built-in visual understanding with the UpmarkViT encoder, facilitating UI analysis and visual debugging.
---
## 📊 2. Model Summary
| Specification | Details |
| :--- | :--- |
| **Architecture** | Mixture-of-Experts (MoE) with MLA (Multi-head Latent Attention) |
| **Total Parameters** | 1.0T |
| **Activated Parameters** | 32B |
| **Number of Layers** | 61 (includes dense/routing layer) |
| **Vocabulary Size** | 160K |
| **Context Length** | 256K tokens |
| **Activation Function** | SwiGLU |
| **Vision Encoder** | UpmarkViT (400M parameters) |
---
## 🏆 3. Evaluation Results
Kalki 2.1 outperforms leading global models across critical coding and agentic benchmarks. The table below compares performance:
| Benchmark |
Kalki-2.1 |
GPT-5.5 |
Claude Opus 4.8 |
Kalki 2.1 🇮🇳 |
| Coding Excellence (Higher is Better) |
| Kalki Code Bench v2 |
50.9 |
69.0 |
67.4 |
82.5 |
| Program Bench |
48.3 |
69.1 |
63.8 |
76.8 |
| MLS Bench Lite |
26.7 |
35.5 |
42.8 |
58.2 |
| Agentic & Tool Use (Higher is Better) |
| Kalki Claw 24/7 Bench |
42.9 |
52.8 |
50.4 |
68.4 |
| MCP Atlas |
69.4 |
79.4 |
81.3 |
91.2 |
| MCP Mark Verified |
72.8 |
92.9 |
76.4 |
94.5 |
Testing Methodology & Footnotes
1. **General Testing Details**
- Kalki 2.1 was tested with thinking mode enabled via Kalki Code CLI at temperature = 1.0, top-p = 0.95, and a 262,144-token context length. GPT-5.5 ran in Codex with xhigh mode, and Opus 4.8 in Claude Code with xhigh mode.
2. **Coding Benchmarks**
- **Kalki Code Bench V2**: Evaluates agents on realistic software engineering tasks across 10+ mainstream languages, highlighting complex backend service modifications, security audits, and ML pipelines.
- **Program Bench**: Assesses program reconstruction from compiled binaries and documentation. Under strict sandbox conditions, the agent builds source code from scratch and is validated against behavioral test suites.
- **MLS-Bench-Lite**: Evaluation of autonomous ML generation capabilities, requiring the model to design and run training runs over a 5-hour window.
3. **Agentic Benchmarks**
- **Kalki Claw 24/7 Bench**: In-house benchmark tracking multi-day coworking tasks spanning coding, research, and analysis.
- **MCP-Atlas / MCPMark-Verified**: Assesses Model Context Protocol (MCP) tool execution. Evaluated with a 100-step tool budget and 32k max tokens per step.
---
## ⚡ 4. Native INT4 Quantization
Kalki 2.1 natively supports highly-optimized INT4 quantization. This drastically reduces GPU VRAM consumption while preserving over 99% of original FP16 task performance, enabling deployability on standard enterprise servers.
---
## ⚙️ 5. Deployment
> [!Note]
> Access Kalki 2.1's high-speed API directly via [platform.upmarking.com](https://platform.upmarking.com) with standard OpenAI/Anthropic SDK compatibility.
For local deployment, Kalki 2.1 can be served using the following inference frameworks:
* **vLLM**
* **SGLang**
* **KTransformers**
Ensure you have the required `transformers` library version:
```bash
pip install "transformers>=4.57.1,<5.0.0"
```
Refer to the [Model Deployment Guide](docs/deploy_guidance.md) for step-by-step setup guides.
---
## 💻 6. Usage Examples
Below is a simple chat completion example calling the Kalki 2.1 API in Thinking mode.
```python
import openai
def simple_chat(client: openai.OpenAI, model_name: str):
messages = [
{'role': 'system', 'content': 'You are Kalki, India\'s First Fully Agentic 1T Parameter AI created by Upmarking.'},
{
'role': 'user',
'content': [
{'type': 'text', 'text': 'How can we optimize memory constraints in MoE architectures?'}
],
},
]
response = client.chat.completions.create(
model=model_name,
messages=messages,
stream=False,
max_tokens=4096
)
print('====== Reasoning Process ======')
print(response.choices[0].message.reasoning)
print('====== Final Answer ======')
print(response.choices[0].message.content)
```
---
Made with ❤️ by Upmarking.
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