TokenGauge Names Bittensor’s SayGM 2026 Provider of the Year for LLM API Pricing

TokenGauge has named Bittensor’s SayGM (SN28) its 2026 Provider of the Year after finding that the gateway offered the lowest real-time price on 17 of 21 LLM routes in its Oct. 13 price snapshot.

The index compared SayGM against direct lab APIs, OpenRouter, Requesty, and Vercel AI Gateway using a blended price of 80% input tokens and 20% output tokens, with gateway fees included.

The site awarded SayGM its top provider label based on price per token, fees, and the verifiability of its privacy claims.

> “Our pick for 2026 is the provider that came out ahead on what this index measures: price per token, fees, and how much of its privacy claim can be checked rather than taken on trust,” TokenGauge wrote.

Rankings Overview

TokenGauge’s LLM API Price Index tracks real-time, uncached per-token pricing in U.S. dollars per million tokens. Its Oct. 13 snapshot found SayGM had the lowest median price against lab list rates for Anthropic, OpenAI, Google, and Moonshot models, while OpenRouter led on Z.ai and DeepSeek.

TokenGauge’s model-level examples include Claude Opus 5.5 at $6.33 per 1 million blended tokens on SayGM versus $7.20 at list price, GPT-6.1 Sol at $2.68 versus $3.60, and Gemini 3.1 Pro Preview at $2.60 versus $4.00.

SayGM’s Unique Approach

SayGM’s gateway is built on Bittensor, where independent providers compete to serve inference requests. TokenGauge’s gateway comparison describes how providers bid for requests, with winning rates capped at the model maker’s list price. That bidding model helps explain the lower prices in TokenGauge’s snapshot, where SayGM undercut several established routes to the same commercial models.

SayGM also provides native Anthropic-, OpenAI-, and Gemini-compatible APIs through one key. Developers can use provider-specific features such as tool use, prompt caching, and extended reasoning without sending every model through a single generic request format.

SayGM’s other differentiator is privacy. TokenGauge says the gateway runs inside an Intel TDX trusted execution environment with public remote attestation, meaning the system can publish evidence about the code running inside the protected environment. The comparison says SayGM keeps prompt and completion content out of logs, while recording metadata such as model, provider, token counts, price, and timing.

The privacy model has limits. Proprietary models from Anthropic, OpenAI, and Google still receive prompts when they are routed to those providers, similar to direct API use. TokenGauge says end-to-end confidential inference applies to confidential open-weight models that run inside the enclave, while frontier-provider requests leave the enclave and reach the model maker.

Buyers evaluating SayGM for sensitive workloads should weigh that distinction. The TDX architecture may reduce what the gateway operator can see, but it does not make third-party proprietary model calls private from the model provider itself.

A Win for Bittensor

SayGM’s recognition is a meaningful result for Bittensor, providing independent evidence that its decentralized approach to AI inference can compete with established commercial providers on price.

Instead of simply offering access to a different set of models, SayGM is making many of the same frontier models available at lower prices through a network of competing providers.

More broadly, it offers a concrete example of Bittensor's ability to turn decentralized competition into a commercially relevant advantage.