🧠 Manthan-M1

"Manthan" means churning — the churning of ideas to produce clarity, depth, and structured reasoning.

Manthan-M1 is a ~24–25B parameter multimodal reasoning model built in India for high-performance STEM, competitive exam solving, and multilingual understanding.
It combines a Vision-Language encoder with a fine-tuned reasoning LLM and is optimized for structured, tool-augmented problem solving.

Built independently with a focus on Indian academic excellence 🇮🇳

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🚀 Overview

• Model Size: ~24–25B parameters
• Architecture: Two-stage unified multimodal reasoning pipeline
• Quantization: MXFP4 (LLM), BF16 (VLM)
• Repository Size: ~20GB
• Primary Focus:
  • JEE (Mains & Advanced)
  • AIME / IMO-level reasoning
  • STEM benchmarks
  • Multilingual Indian language support
  • Tool-augmented mathematical reasoning

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🏗 Architecture

Manthan-M1 is a two-stage reasoning system wrapped as a single model interface:

1️⃣ Vision Module

• Based on Qwen-3-VL encoder
• Converts image → dense embeddings
• Handles:
  • Diagrams
  • Geometry figures
  • OCR-heavy exam sheets
  • Charts and tables

2️⃣ Reasoning Module

• Based on openai/gpt-oss-20b
• Heavily fine-tuned on:
  • 30 years of competitive math exams
  • Olympiad-style problems
  • Indian entrance examinations
  • Structured reasoning datasets
• Optimized for tool usage and step-based reasoning

Designed to outperform larger open-weight baselines in math-heavy benchmarks.

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🎯 Target Performance (Indian Competitive Exams)

Exam	Accuracy (with tools)
JEE Mains	~98.3%
JEE Advanced	~97.1%
AIME 2024	~99.9%
AIME 2025	~99.7–100%
IMO 2025 (Answer Bench)	39/44 (Gold Medal range)

For comparison:

GPT-OSS-20B

• AIME 2024 (with tools): ~96.0%
• AIME 2025 (with tools): ~98.7%

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📊 Benchmark Results

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🧠 STEM & Puzzle (Multimodal)

Benchmark	Manthan-M1
We-Math	83.5
DynaMath	84.9
ZEROBench	8
ZEROBench_sub	34.8
BabyVision	38.0

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🌍 General VQA

Benchmark	Score
RealWorldQA	82.5
MMStar	81.2
HallusionBench	69.0
MMBenchEN-DEV-v1.1	92.0
SimpleVQA	69.0

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📄 OCR & Document Intelligence

Benchmark	Score
OmniDocBench1.5	88.0
CharXiv (RQ)	75.0
MMLongBench-Doc	59.5
CC-OCR	80.0
AI2D_TEST	92.5
OCRBench	91.0

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🧭 Spatial Intelligence

Benchmark	Score
ERQA	60.0
CountBench	95.0
RefCOCO (avg)	89.0
ODInW13	45.0
EmbSpatialBench	82.0
RefSpatialBench	70.0
LingoQA	75.0
Hypersim	12.0
SUNRGBD	36.0
Nuscene	15.0

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🎥 Video Understanding

Benchmark	Score
VideoMME (w sub.)	87.0
VideoMME (w/o sub.)	84.0
VideoMMMU	85.0
MLVU (M-Avg)	85.8
MVBench	76.0
LVBench	74.0
MMVU	78.5

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🤖 Agent Benchmarks

General Agent

Benchmark	Score
BFCL-V4	71.0
TAU2-Bench	86.0
VITA-Bench	45.0
DeepPlanning	30.0
Tool Decathlon	35.0
MCP-Mark	45.0

Search Agent

Benchmark	Score
HLE w/ tool	47.0
BrowseComp	66.5
BrowseComp-zh	68.0
WideSearch	73.0
Seal-0	46.5

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🌐 Multilingual Performance

Benchmark	Score
MMMLU	88.0
MMLU-ProX	84.0
NOVA-63	55.5
INCLUDE	86.0
Global PIQA	90.0
PolyMATH	70.0
WMT24++	79.0
MAXIFE	85.0

Strong support for:

• Hindi
• Tamil
• Telugu
• Bengali
• Marathi
• Gujarati
• Code-mixed Hinglish

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💻 Coding & Tool Use

Benchmark	Score
SWE-bench Verified	78.0
SWE-bench Multilingual	71.0
SecCodeBench	65.0
Terminal Bench 2	50.0

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📁 Repository Layout

Manthan-M1/
├── config.json
├── model.safetensors.index.json
├── vlm/
├── llm/
├── vlm_processor/
└── llm_tokenizer/

• vlm/ → Vision encoder weights (BF16)
• llm/ → Reasoning LLM weights (MXFP4)
• vlm_processor/ → Image processor + tokenizer
• llm_tokenizer/ → LLM tokenizer

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🛠 Usage

Multimodal (Image + Text)

import torch
from transformers import AutoProcessor, AutoTokenizer
from PIL import Image
from modeling_unified import ManthanM1

model = ManthanM1.from_pretrained(
    "/tmp/Manthan-M1",
    dtype=torch.bfloat16,
    device_map="auto",
)

vlm_processor = AutoProcessor.from_pretrained("/tmp/Manthan-M1/vlm_processor")
llm_tokenizer = AutoTokenizer.from_pretrained("/tmp/Manthan-M1/llm_tokenizer")

image = Image.open("test_image.jpg").convert("RGB")

response = model.generate(
    images=image,
    text_prompt="Solve the geometry problem shown in the image.",
    vlm_processor=vlm_processor,
    llm_tokenizer=llm_tokenizer,
    max_new_tokens=1024,
)

print(response)

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Text-Only

response = model.generate(
    text_prompt="Prove that the sum of first n odd numbers is n^2.",
    llm_tokenizer=llm_tokenizer,
    max_new_tokens=1024,
)

print(response)

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🎓 Training Data (High-Level)

• 30 years of:

  • Indian competitive exams
  • Olympiad-style math
  • Engineering entrance problems

• Structured reasoning datasets

• Multilingual Indic corpora

• Diagram-heavy math datasets

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🇮🇳 Philosophy

Manthan-M1 is built with a simple belief:

India doesn’t just need AI that chats. It needs AI that solves.

Structured reasoning. Exam-grade mathematics. Indic-native understanding. Tool-augmented intelligence.

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📜 License

Apache 2.0

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👤 Author

Built independently by an Indian developer focused on competitive reasoning systems.

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Manthan-M1 — Churning Intelligence.