Ra

Ranking LLMs by Usage over Time

Hacker News

Ranking LLMs by Usage over Time

Hey HN, We've been building a multi-model API for LLMs called OpenRouter since we launched the Window AI extension in April this year [0]. The events of the last week make it clear the LLM landscape is unpredictable, so LLM aggregators are picking up interest. Unlike others, we're making a router on top of a public, explorable dataset. We recently built a way to rank and visualize this data, including the token counts we see going to and from different models, both open-sourced (like Llama, Mistral, finetunes, and variants) and closed-sourced (OpenAI, Anthropic). The API supports - 50+ different models: [0] - Consolidated payments for all models - OAuth, so users can pay for usage directly - Upstream latency/throughput tracking - Multiple providers per model, for redundancy (downtime happens!) - Prompt compression, so you don't have to worry as much about context length Docs: [1] We support 11 model hosts, including our own, based on vLLM, which we've just open-sourced: [2] Some users have opted-in to sharing their prompts, which will soon allow us to show which models are best for different tasks. Let us know your feedback, and if you've worked on a similar problem before! Alex and Louis [0] https://news.ycombinator.com/item?id=35481760 [1] https://openrouter/models [2] https://openrouter.ai/docs [3] https://github.com/OpenRouterTeam/openrouter-runner

Share card

Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, new · Missing: mac, agents, macos
96%96% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: supports, including · Missing: reddit linkedin, podcasting, created
90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: llama, ide, io · Missing: https docs, excited, just released
63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: visualize, users, way · Missing: mobile apps, ios, personal
50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host, soon, users · Missing: plus, platform, intuitive
36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

Similar products

Ra
RankFight – We're Ranking Everything50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

RankFight – We're Ranking Everything

Hacker News6
Co
Covid-19 infection ranking adjusted by population45%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Covid-19 infection ranking adjusted by population

Hacker News5
MissedQueries
MissedQueries57%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

You're ranking for searches you never wrote about.

Indie Hackers1analytics
Ra
Ranking Data Sets for AI on Ethereum36%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Ranking Data Sets for AI on Ethereum

Hacker News1
Ra
RankIt - Private leagues w/ranking for tabletennis, shuffleboard, dart47%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

RankIt - Private leagues w/ranking for tabletennis, shuffleboard, dart

Hacker News2
Ol
Olympics Ranking Adjusted by Population (Evening Project)53%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Olympics Ranking Adjusted by Population (Evening Project)

Hacker News74
Ra
Ranking Universities with Professor Reviews42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Ranking Universities with Professor Reviews

Hacker News1
site ranking
site ranking36%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

site ranking

Indie Hackers
SE Ranking
SE Ranking53%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Improve your site's SEO ranking

AppSumo
GP
GPTCache – Redis for LLMs69%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

GPTCache – Redis for LLMs

Hacker News7