README.md

metadata

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, 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	math word problems	895	105	numeric match	MIT
MATH	competition math	894	106	normalised LaTeX / numeric match	MIT
MMLU-Pro	knowledge MCQ (10 options)	1080	120	option letter	MIT
ARC-Challenge	science MCQ	725	75	option letter	CC-BY-SA-4.0
MBPP	Python coding	633	67	unit tests executed in a network-less sandbox	CC-BY-4.0
HumanEval	Python coding	146	18	unit tests executed in a network-less sandbox	MIT
LongBench-v2	long-context MCQ (up to ~260k tokens)	347	32	option letter	Apache-2.0
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.