AI · alternatives · 2026

24 alternatives to TokenPath – token-level citations for LLM output, read from attention

One API to ground every generated token in its source — for output from any model. TokenPath measures attention directly, not the LLM's self-report, to return machine-readable, token-level citations your product can act on.

Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Prices are shown for the 1 we have checked on their own sites, 1 of them free or with a free tier.

1
Token price calculator for 400+ LLMs▲268
Hey HN! Tokencost is a utility library for estimating LLM costs. There are hundreds of different models now, and they all have their own pricing schemes. It’s difficult to keep up with the pricing changes, and it’s even more difficult to estimate how much your prompts and completions will cost until you see the bill. Tokencost works by counting the number of tokens in prompt and completion messages and multiplying that number by the corresponding model cost. Under the hood, it’s really just a simple cost dictionary and some utility functions for getting the prices right. It also accounts for…
2024 · github.com · its alternatives →
2
Tokenomy.ai▲105
See your LLM token bill before you hit send.
2025 · tokenomy.ai · its alternatives →
3
Tokenwise▲143
A smart LLM proxy that shows where you're overpaying
Jun 2026 · tokenwisehq.com · its alternatives →
4
Weave▲237
Hey HN, We’re building Weave: an ML-powered tool to measure engineering output, that actually understands engineering output! Why? Here’s the thing: almost every eng leader already measures output - either openly or behind closed doors. But they rely on metrics like lines of code (correlation with effort: ~0.3), number of PRs, or story points (slightly better at ~0.35). These metrics are, frankly, terrible proxies for productivity. We’ve developed a custom model that analyzes code and its impact directly, with a far better 0.94 correlation. The result? A standardized engineering output…
2025 · app.workweave.ai · its alternatives →
5
A MitM proxy to see what your LLM tools are sending▲218
I built this out of curiosity about what Claude Code was actually sending to the API. Turns out, watching your tokens tick up in real-time is oddly satisfying. Sherlock sits between your LLM tools and the API, showing you every request with a live dashboard, and auto-saved copies of every prompt as markdown and json.
Jan 2026 · github.com · its alternatives →
6
TokenCounter▲117
Count tokens and estimate costs for any AI model
2024 · tokencounter.co · its alternatives →
7
LLM Attention Visualizationfree▲176
I often need to convert times between time zones, so I built this tool to convert between them quickly and efficiently. 1. Add the cities you want to convert between (this is saved in the browser). 2. Drag on the map until the time on the source city is the time you want. 3. Look at the time at the other city. It's even more helpful to use on a phone since it doesn't require typing to convert between time zones or look up the time in another city (assuming you already added the cities you care about). If you need to convert a time far in the future, you can input the date and time in the…
Sep 2026 · ishamf.dev · its alternatives →
8
Breadcrumb▲163
Open-source LLM tracing for agent visibility
Mar 2026 · breadcrumb.sh · its alternatives →
9
Fully client-side GPT2 prediction visualizer▲153
Hi HN! I've found this visualization tool immensely helpful over the years for getting an intuition for how an LLM "sees" some piece of text, and with a bit of elbow grease decided to move all compute to client side so I could make it publicly available. I've found it particularly useful for - Understanding exactly how repetition and patterns affect a small LM's ability to predict correctly - Understanding different tokenization patterns and how it affects model output - Getting a general sense of how "hard" different prediction tasks are for GPT-style models Known problems (that I probably…
2023 · perplexity.vercel.app · its alternatives →
10AT
A tool to benchmark LLM APIs (OpenAI, Claude, local/self-hosted)▲55
I recently built a small open-source tool to benchmark different LLM API endpoints — including OpenAI, Claude, and self-hosted models (like llama.cpp). It runs a configurable number of test requests and reports two key metrics: • First-token latency (ms): How long it takes for the first token to appear • Output speed (tokens/sec): Overall output fluency Demo: https://llmapitest.com/ Code: https://github.com/qjr87/llm-api-test The goal is to provide a simple, visual, and reproducible way to evaluate performance across different LLM providers, including…
2025 · llmapitest.com · its alternatives →
11
A new benchmark for testing LLMs for deterministic outputs▲60
When building workflows that rely on LLMs, we commonly use structured output for programmatic use cases like converting an invoice into rows or meeting transcripts into tickets or even complex PDFs into database entries. The model may return the schema you want, but with hallucinated values like `invoice_date` being off by 2 months or the transcript array ordered wrongly. The JSON is valid, but the values are not. Structured output today is a big part of using LLMs, especially when building deterministic workflows. Current structured output benchmarks (e.g., JSONSchemaBench) only validate…
Apr 2026 · interfaze.ai · its alternatives →
12
Context Compass▲16
I built a small Chrome extension for people who write long conversations in Claude and don’t want to unexpectedly hit the 200,000-token limit. Context Compass automatically counts words and estimates token usage as you write. It updates in real time, gives you a visible percentage bar, and helps power users manage sprawling chats without losing work or flow. I built it because I kept getting the “max context” error and realized there was no way to track usage in Claude. No signups, no tracking, just a simple install. Would love any feedback or ideas for improvements. Thanks!
2025 · chromewebstore.google.com · its alternatives →
13
LLM Docs▲12
Token-optimized minified popular docs
2025 · llm-docs.com · its alternatives →
14
Understand and reduce token usage with ContextSpy context profiler▲6
Most devs using AI coding tools don't even imagine how much information they put in the context window - tools, MCPs, tool responses, active skills - all of which take up some real estate. Sometimes updating a tool description or trimming api response can give significant savings - and profiler helps to measure that
Jun 2026 · github.com · its alternatives →
15
Using LLMs and Embeddings to classify application errors▲65
Hi Hacker News! We’re Vadim and Chris from Highlight.io [1]. We do web app monitoring and are working on using LLMs/embeddings to add new functionality to our error monitoring product. Given that there’s a lot of founders/engineers using LLMs in their products, we figured we’d share how we built the new functionality, their impact on our workflows, and how you can try it out. Our goal was to build two features: (1) tagging errors (e.g. deeming an error as “authentication error” or a “database error”); and (2) grouping similar errors together (e.g. two errors that have a different…
2023 · github.com · its alternatives →
16
Tokenflood – simulate arbitrary loads on instruction-tuned LLMs▲21
Hi everyone, I just released an open source load testing tool for LLMs: https://github.com/twerkmeister/tokenflood === What is it and what problems does it solve? === Tokenflood is a load testing tool for instruction-tuned LLMs hat can simulate arbitrary LLM loads in terms of prompt, prefix, and output lengths and requests per second. Instead of first collecting prompt data for different load types, you can configure the desired parameters for your load test and you are good to go. It also let's you assess the latency effects of potential prompt parameter changes before…
Nov 2025 · github.com · its alternatives →
17AS
A surprisingly effective way to predict token importance in LLM prompts▲14
We explored a novel method to gauge the significance of tokens in prompts given to large language models, without needing direct model access. Essentially, we just did an ablation study on the prompt using cosine similarity of the embeddings as the measure. We got surprisingly promising results when comparing this really simple approach to integrated gradients. Curious to hear thoughts from the community!
2023 · heatmap.demos.watchful.io · its alternatives →
18
Afford AI▲11
Track LLM usage, costs & tokens with real-time insights.
2025 · affordai.io · its alternatives →
19
Token Diet▲2
Free prompt token counter and optimizer - cut LLM API costs
Jun 2026 · ranker.co.za · its alternatives →
20
A tool to properly observe your LLM's context window▲8
TL;DR: You can’t fix what you can’t see. I built a tool that pulls apart LLM context into meaningful components that you can see, measure, and then...
Oct 2025 · blog.nilenso.com · its alternatives →
21
TokenUse▲2
AI Token and cost observability for developers
Aug 2026 · tokenuse.ai · its alternatives →
22
Context Budget Planner▲5
Calculate LLM tokens & costs before you send
Jan 2026 · context-budget-planner-qyosh00ed-thibauds-projects-31e2cfb7.vercel.app · its alternatives →
23
TokenDam▲2
A linter for LLM token spend — that gates cost in CI
Jul 2026 · tokendam.dev · its alternatives →
24
Alerting in realtime RAG: spot changes to LLM answers, using few tokens▲8
Hi I am Jan, CTO @ Pathway. A use case we have been working on with LLMs is to let people know when an answer to their query changes due to revisions of source documents. Obviously, we want to avoid periodically re-computing all queries for the LLM. Why I think it’s cool? - We don’t spin in a loop to repeat with the LLM. - Alerts are LLM-deduplicated - no spamming users with typo fixes - And the best - our framework, Pathway takes care of handling the updates, the example looks nearly like a regular, static RAG chatbot. More context + GIF of how it works for Google Drive document alerts:…
2023 · github.com · its alternatives →

Also compare

Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →