BrainPlay is Bittensor subnet 117 (SN117), a project in the Other: LLM Benchmarking and Evaluation lane. LLM benchmarks, but make them games
Forget abstract leaderboards. BrainPlay benchmarks AI models by putting them head-to-head in real games, turning dry evaluation metrics into something anyone can watch, understand, and actually compare.
It is an educational deep dive, not a recommendation to stake. For live price, pool depth, and performance, always open the live subnet page.
What is BrainPlay (SN117)?
BrainPlay is a Bittensor subnet that evaluates language models through competitive gameplay. Rather than scoring models on abstract math benchmarks, it runs them through games like Codenames, 20 Questions, and Super Mario, producing results that are both technically meaningful and visually interpretable.
In plain English: It's like Elo chess ratings for AI, except the match is a game of Codenames or a Mario speedrun instead of a chess game.
Centralized reference points: LMSYS Chatbot Arena
How miners and validators work
Like every Bittensor subnet, SN117 is a two-sided market: miners produce work, validators score it, and emissions follow those weights under the network’s dTAO economics.
- Miners deploy language models to Targon (SN4), a serverless inference layer. Their model is their entry.
- Validators create game rooms, assign pairs of miners to compete, score outcomes, and set weights based on performance.
Mechanism and design
BrainPlay v2.0 integrates with Targon (SN4) as its inference backbone. Miners don't run their own servers. Instead, they deploy models via Targon's TVM (Targon Virtual Machine) and receive a serverless endpoint. Validators create shared game rooms and query those endpoints to run matches. Both miners and validators require a Targon API key, and miners need sufficient Targon credits to participate.
Three games are currently live, per the official repo: Codenames (language, social deduction), 20 Questions (language, logical inference), and SuperMario (vision, policy control). Codenames and 20 Questions run through the LLM weight group (mechid 0); SuperMario runs through the vision weight group (mechid 1). Emissions are split equally between the two groups.
Reward logic: winning teams receive a normalized score of 1.0; losing teams score proportionally lower. When no valid games complete in a round, the validator publishes burn weights for that group rather than preserving stale miner scores. This keeps the scoring system clean but also means no emissions reach miners during inactive periods.
Price has gained 16.5% over 7 days against a backdrop of modest net inflows (~289 TAO over 7 days). Market cap stands at approximately 7,097 TAO. The root proportion is 0.32, meaning roughly two-thirds of the liquidity pool reflects organic demand rather than protocol subsidy. That's a reasonable signal of genuine staker interest for a subnet at this stage.
The most notable data point right now: on-chain data shows zero active miners. With no miners deploying models, validators produce burn weights and no benchmarks run. Given recent commit activity (April 20, 2026), this looks like a build-first phase rather than stagnation, but it's worth watching.
Why the project exists
- The problem it solves: Standard benchmarks like MMLU or HellaSwag are hard to interpret. A jump from 72.3% to 73.1% accuracy tells you almost nothing about how a model actually behaves. Game outcomes are comparatively intuitive.
- The opportunity: A shared, decentralized benchmark layer where results come from adversarial competition rather than curated test sets that models can overfit. That's a real issue in model evaluation today.
- The Bittensor advantage: Running many instances of many models simultaneously at scale requires infrastructure. Centralized providers charge per query. Bittensor's incentive layer makes that scale economically feasible.
- Traction signals: 410 commits across 6 contributors, with the last commit on April 20, 2026. Active development. Minimal social presence was found in current data, suggesting the project is building before marketing.
What stakers should check before buying alpha
Subnet storytelling is cheap. On-chain and product checks are not. Use the same framework as our subnet metrics guide:
| Check | Why |
|---|---|
| Real product / docs / repo | Separates shipping teams from narrative-only slots |
| Pool depth (TAO) | Your exit door: thin pools amplify slippage |
| Net flows (7d / 30d) | Under flow-based emissions, capital votes with its feet |
| Emission share | Whether the market is funding this subnet right now |
| Ownership concentration | Large holders can move a small alpha market |
| Immunity / deregistration | Post-immunity weak slots can lose the race (see immunity guide) |
Pull the live numbers on BrainPlay · SN117 rather than trusting any static screenshot in a blog post.
Risk factors
- No active miners: On-chain data shows zero active miners. Without participants deploying models to Targon, no benchmarks are being produced. This is the subnet's most pressing operational gap.
- Targon dependency: Miners require both a Targon API key and sufficient Targon credits. This creates a secondary cost barrier on top of standard Bittensor registration, which may limit participation relative to subnets with fewer prerequisites.
- Thin liquidity: Root in pool is approximately 2,889 TAO. Slippage is a real consideration for any meaningful position change.
- Early community: No meaningful Twitter or social activity was found in current data. The project appears to be pre-marketing, which limits visibility and discovery.
How this fits a TAO portfolio
Many holders keep a root vs subnet split: root for calmer TAO exposure, subnets like BrainPlay for thematic upside (and higher variance). If you use TaoScale, you can size SN117 from your wallet without handing over custody. See non-custodial staking.
- Live market: SN117 on TaoScale
- Browse and stake: /invest
- More subnet explainers: /blog/subnets
FAQ
What does BrainPlay do on Bittensor?
It's like Elo chess ratings for AI, except the match is a game of Codenames or a Mario speedrun instead of a chess game.
What is the SN117 alpha token?
When you stake TAO into subnet 117, you receive that subnet’s alpha. Its price versus TAO moves with the subnet’s pool (see what is dTAO).
How do I research BrainPlay before staking?
Read the mechanism above, open the official repo/docs when available, then verify pool depth, flows, and activity on SN117. Cross-check with the metrics checklist.
Is this financial advice?
No. TaoScale publishes educational explainers and live tools. Decisions to stake are yours alone.
Not financial advice. Educational overview of BrainPlay (SN117) compiled from public project materials and ecosystem documentation. Mechanisms and market structure change, so verify live data and official sources before acting.
