Datasets:
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license: cc-by-4.0
language:
- ta
- en
pretty_name: TamilBench
size_categories:
- n<1K
task_categories:
- question-answering
tags:
- tamil
- tanglish
- code-mixed
- romanized
- beam
- build-with-beam
- token-efficiency
configs:
- config_name: parallel
data_files: data/parallel.jsonl
- config_name: native
data_files: data/native.jsonl
- config_name: beam_runs
data_files: results/beam_runs.jsonl
---
# TamilBench v1
**How well does Beam reason in Tamil, and what does it cost?**
- `parallel` (50 items): the same question in **English**, **Tamil script** and colloquial **Tanglish** (romanized Tamil). Categories: math word problems with Indian context (20), logic (8), Tamil culture/knowledge (15), everyday (7). Short gold answers plus aliases.
- `native` (22 items): questions that only exist in Tamil: Tamil number words, ancient Tamil numerals (௲ ௱ ௰), letter counting by Tamil grammar (எழுத்து), Sangam/Thirukkural literature, grammar, the Tamil calendar.
- `beam_runs` (516 rows): every response from `reflection/Beam-1.0-501B-A23B` (HF Inference Providers → Baseten) with output, reasoning trace, prompt/reasoning/completion tokens, latency, correctness and cost.
## Headline results (Beam-1.0-501B-A23B, Oct 2026)
| Language | Accuracy | Prompt tok | Reasoning tok | $ per 1k queries | $ per 1k at `reasoning_effort=minimal` |
|---|---|---|---|---|---|
| English | 100% | 90 | 106 | 0.178 | 0.150 |
| Tamil script | 100% | 231 | 161 | 0.301 | 0.254 |
| Tanglish | 100% | 104 | 214 | 0.313 | 0.246 |
- Beam's tokenizer spends **8.0 tokens per Tamil word** (0.92 chars/token) vs 1.5 for English: the same question costs **3.75× more input tokens** in Tamil script.
- Tanglish is cheap to read (2.4 tokens/word) but Beam **thinks 2× longer** (4× on logic puzzles): it translates internally first.
- Net: Tamil and Tanglish queries cost **~1.7×** English. `reasoning_effort="minimal"` kept 100% accuracy and cut cost 16–21%.
- Native Tamil split: **98.5%** (65/66); the one miss was the last word of Kural 1.
Scoring: last number for numeric answers; alias match for words. Prices: Baseten list $0.40/1M input, $1.20/1M output (reasoning billed as output). The parallel set is saturated for Beam, so read accuracy as a sanity floor; the token/cost findings are the contribution.
Interactive explorer + live Beam tokenizer: https://huggingface.co/spaces/esscor/TamilBench
Built by Mohamed Adib for the Hugging Face × Reflection *Build with Beam* challenge.
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