00 · MODEL RANKINGS # The models agents on Rensei use, week by week. Token usage across every agent session on the platform, by model, lab, serving provider, harness and kind of work. Rebuilt every day from production rollups. The full data set is free to download below. 01 · MODELS ## Usage by model Each model's share of tokens over time, and the full leaderboard. The 7-day change column compares the latest 7 days with the 7 days before, which is where a new checkpoint taking over from an older one shows first. FIG 1.1 *FIG 1.1 - Tokens by model. Share is each model's part of the trailing 7 days; volume is daily tokens, each model drawn from zero. The table ranks every model with tokens in the window.* 02 · MARKET SHARE ## Market share by lab Tokens rolled up to the lab that built each model, whichever route served the call. A model reached through a gateway and the same model reached directly both count to its lab. FIG 2.1 *FIG 2.1 - Tokens by model lab, with each lab's share and 7-day change.* 03 · SERVING PROVIDERS ## How agents on Rensei reach models Agents on Rensei reach a model in three ways: through an AI gateway such as Vercel AI Gateway or OpenRouter, directly from the lab on an API key, or on the customer's own subscription through the lab's CLI. This chart counts tokens by whoever served them. FIG 3.1 *FIG 3.1 - Tokens by serving provider: gateways, and labs serving their own models directly.* ### How each lab is reached For each lab, the split of its tokens by route over the last 90 days. 04 · HARNESSES ## Usage by harness The agent harness that ran each session, such as Claude Code, Codex, Pi or Muse. Rensei runs several harnesses side by side; the models each one runs are listed below the chart. FIG 4.1 *FIG 4.1 - Tokens by agent harness, with each harness's share and 7-day change.* ### Models per harness The three models carrying the most tokens in each harness over the last 90 days. 05 · BY TASK ## Top harnesses and models by task Every session carries a work type, such as development, QA or research. This is how tokens divide across those kinds of work, and which harnesses and models lead each one. FIG 5.1 *FIG 5.1 - Tokens by work type.* ### Leaders by task For each work type, the three harnesses and the three models with the largest share of its tokens over the last 90 days. 06 · TOKEN MIX ## Token mix Input, cache reads, cache writes and output as parts of all tokens. Agent sessions send long context on every turn, so the cache series show how much of that context providers served from cache. FIG 6.1 *FIG 6.1 - Tokens by kind. Reasoning tokens sit inside output.* 07 · METHODOLOGY ## What is counted, and the data - Tokens. Input, cache reads, cache writes and output, added together. Reasoning tokens are part of output and are not counted twice. OpenRouter and Vercel AI Gateway count tokens the same way for their rankings. - Scope. Every agent session on the Rensei platform, as platform totals. No organization, project or person is identified, and no price or spend is shown. - Labs. A model's lab comes from its canonical model identity, so the lab rollup does not depend on the provider that served the call. Models the platform cannot map to an identity are listed as Unattributed. - Serving providers. The provider that served the tokens: an AI gateway, or the lab itself. Direct access is split by how the call was authorized: an API key, or the customer's own subscription through the vendor's CLI. - Windows and change. The data covers the last 90 complete days in UTC; the current day is left out until it ends. Weeks end on the latest complete day. On the share view, each day shows a series' share of the 7 days ending that day. The 7-day change compares the latest 7 days with the 7 days before. New means the earlier week had less than 1% of the latest week's tokens, too little for a percentage to mean anything. - Series. Each chart draws up to eight series per window, chosen by their highest weekly share in that window, so a model that led any week keeps its own line even when a busier month follows. The rest are added together as Other. Lines between days are smoothed with a curve that never rises above or dips below the days it joins. - Sessions. Distinct agent sessions per day, added up across the week, so a session that runs past midnight counts on both days. - Updates. The page is rebuilt every day from the platform's production rollups. DOWNLOAD THE DATA [rankings.json](https://rensei.ai/rankings.json) The full daily snapshot every chart on this page is drawn from. [rankings.csv](https://rensei.ai/rankings.csv) One row per day for the platform total and for each model, lab, serving provider and harness. Published under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) . Reuse it freely with attribution: Rensei model rankings, rensei.ai/rankings . --- Canonical: https://rensei.ai/rankings Site index: https://rensei.ai/llms.txt Docs: https://rensei.ai/docs OpenAPI: https://rensei.ai/openapi.json