---
license: cc-by-sa-4.0
language: [en]
task_categories: [text-classification]
tags: [routing, orchestration, llm-router, multi-agent, model-selection]
pretty_name: Shaktimaan Routing Data v1.0
size_categories: [1K<n<10K]
---
# Shaktimaan Routing Data v1.0

The training and evaluation data behind [Shaktimaan v1.0](https://huggingface.co/podstack/Shaktimaan), a 0.5B
LLM router. Each row is one task that **every capable agent in a six-model pool attempted**, with every answer
automatically graded, plus the cost-aware routing label derived from those outcomes. Useful for training or
benchmarking LLM routers, model-selection policies and cascades.

Published: 4720 train / 523 test rows (5243
tasks). The model was additionally trained and evaluated on 300 ChartQA rows that are **not** published here (see
Licensing).

## Agent pool
| agent | model | context | image input (in this pool) | relative cost / token |
|---|---|---|---|---|
| `gemma-3-1b` | `google/gemma-3-1b-it` | 32k | no | 0.02 |
| `kimi-vl` | `Kimi-VL-A3B-Thinking` | 8k | yes | 0.05 |
| `nemotron-lightning` | `nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4` | 128k | no | 0.1 |
| `glm-5.3-flash` | `zai-org/GLM-5.3-Flash` | 32k | no | 0.2 |
| `qwen3.8-27b` | `Qwen/Qwen3.8-27B` | 32k | no | 0.5 |
| `nemotron-ultra` | `nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4` | 256k | no | 1.0 |

"Relative cost" is a per-token price weight used to compute routing cost; it reflects each model's relative serving
cost (roughly active parameters), not a vendor price.

## Sources
| source | category | train | test | grading | license |
|---|---|---|---|---|---|
| [GSM8K](https://huggingface.co/datasets/openai/gsm8k) | math word problems | 895 | 105 | numeric match | MIT |
| [MATH](https://huggingface.co/datasets/EleutherAI/hendrycks_math) | competition math | 894 | 106 | normalised LaTeX / numeric match | MIT |
| [MMLU-Pro](https://huggingface.co/datasets/TIGER-Lab/MMLU-Pro) | knowledge MCQ (10 options) | 1080 | 120 | option letter | MIT |
| [ARC-Challenge](https://huggingface.co/datasets/allenai/ai2_arc) | science MCQ | 725 | 75 | option letter | CC-BY-SA-4.0 |
| [MBPP](https://huggingface.co/datasets/google-research-datasets/mbpp) | Python coding | 633 | 67 | unit tests executed in a network-less sandbox | CC-BY-4.0 |
| [HumanEval](https://huggingface.co/datasets/openai/openai_humaneval) | Python coding | 146 | 18 | unit tests executed in a network-less sandbox | MIT |
| [LongBench-v2](https://huggingface.co/datasets/THUDM/LongBench-v2) | long-context MCQ (up to ~260k tokens) | 347 | 32 | option letter | Apache-2.0 |
| [ChartQA](https://huggingface.co/datasets/HuggingFaceM4/ChartQA) | chart images | 269 | 31 | relaxed match (numbers within 5%) | GPL-3.0 (**not published here**) |

Long LongBench-v2 contexts are stored truncated to what the router reads (first 5,000 and last 1,500 characters);
`est_tokens` keeps the original size.

## How it was built
1. **Solve** - every agent answered every task once (temperature 0.6, up to 8,192 output tokens). Agents that cannot
   take an input (no image support, context window too small) are recorded as failures without being called
   (`skipped`: `no_vision` / `context`).
2. **Grade** - automatically, per source (see table). Code is graded by running the dataset's unit tests against the
   model's code in a throwaway container with no network, 512 MB RAM and a 15 s limit.
3. **Label** - the cheapest agent that solved the task, counting a win only if that agent is reliable on the source
   (solve rate >= 70%), because single-sample wins by weak agents are mostly luck a router cannot predict.
   `gemma-3-1b` is never a label. If no eligible agent solved the task, the label is an ensemble of the capable strong
   agents (`glm-5.3-flash`, `qwen3.8-27b`, `nemotron-ultra`, aggregated by the last), or the strongest capable agent
   alone when fewer than two can take the input.

### Agent solve rates (single attempt, all 5543 tasks incl. ChartQA)
| source | `gemma-3-1b` | `kimi-vl` | `nemotron-lightning` | `glm-5.3-flash` | `qwen3.8-27b` | `nemotron-ultra` |
|---|---|---|---|---|---|---|
| GSM8K | 26.3% | 78.0% | 95.0% | 96.9% | 96.8% | 95.6% |
| MATH | 18.4% | 41.9% | 70.7% | 75.8% | 75.9% | 71.6% |
| MMLU-Pro | 12.7% | 50.1% | 73.0% | 81.0% | 82.5% | 76.2% |
| ARC-Challenge | 44.9% | 91.4% | 97.9% | 97.8% | 98.0% | 98.2% |
| MBPP | 50.4% | 67.0% | 90.9% | 94.0% | 88.4% | 93.6% |
| HumanEval | 41.5% | 79.3% | 92.7% | 92.1% | 93.3% | 97.0% |
| LongBench-v2 | 5.3% | 0.0% | 25.9% | 10.3% | 12.1% | 54.6% |
| ChartQA | 0.0% | 80.0% | 0.0% | 0.0% | 0.0% | 0.0% |
| **all** | **25.2%** | **60.8%** | **75.8%** | **78.1%** | **77.9%** | **79.3%** |

### Label distribution (all 5543 tasks incl. ChartQA)
| route label | tasks | share |
|---|---|---|
| `glm-5.3-flash` | 1854 | 33.4% |
| `nemotron-lightning` | 1509 | 27.2% |
| `kimi-vl` | 1373 | 24.8% |
| `glm-5.3-flash,qwen3.8-27b,nemotron-ultra (ensemble)` | 365 | 6.6% |
| `nemotron-ultra` | 329 | 5.9% |
| `qwen3.8-27b` | 113 | 2.0% |

## Fields
| field | description |
|---|---|
| `id`, `source`, `domain`, `kind` | task id, source dataset, domain, grading kind (`num`, `math`, `mc`, `code`, `relaxed`) |
| `prompt` | the task (LongBench contexts truncated, see above) |
| `gold` | reference answer; for code, the unit tests |
| `has_image`, `est_tokens`, `prompt_truncated` | input metadata (`est_tokens` is a conservative chars/3 estimate) |
| `router_input` | exactly what the router reads: a size / image header plus the start and end of the task |
| `agent_results` | JSON: per agent `correct`, `completion_tokens`, `latency` (s), `skipped` |
| `route` | JSON label: `domain`, `difficulty`, `strategy` (`single` / `ensemble`), `agents` |
| `messages` | chat-format SFT example (system, user = `router_input`, assistant = `route`) |

## Licensing
Released under **CC-BY-SA-4.0**, as required by the ARC-Challenge share-alike terms; the other included sources are
MIT, CC-BY-4.0 and Apache-2.0. The 300 ChartQA rows used for training are excluded because ChartQA is GPL-3.0;
`gen.py` in the model repository regenerates them. Please also cite the original datasets.