🚀 1. Model Introduction
Kalki 1.5 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 1.5 is custom-tuned for complex, long-horizon software engineering tasks and multi-modal tool use.
Kalki 1.5 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 1.5 outperforms leading global models across critical coding and agentic benchmarks. The table below compares performance:
- General Testing Details
- Kalki 1.5 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.
- 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.
- 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 1.5 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 1.5's high-speed API directly via platform.upmarking.com with standard OpenAI/Anthropic SDK compatibility.
For local deployment, Kalki 1.5 can be served using the following inference frameworks:
- vLLM
- SGLang
- KTransformers
Ensure you have the required transformers library version:
pip install "transformers>=4.57.1,<5.0.0"
Refer to the Model Deployment Guide for step-by-step setup guides.
💻 6. Usage Examples
Below is a simple chat completion example calling the Kalki 1.5 API in Thinking mode.
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)