AI · alternatives · 2026

24 alternatives to LLM Token Visualizer – How big is 128k token input

Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.

1PT
Predictive text using only 13kb of JavaScript. no LLM▲207
2024 · adamgrant.info · its alternatives →
2
Tokensift, an open-sourced token-efficiency linter for LLM prompts▲6
Token-efficiency linter for LLM prompts and payloads - ritenv/tokensift
Aug 2026 · github.com · its alternatives →
3
Tokenwise▲143
A smart LLM proxy that shows where you're overpaying
Jun 2026 · tokenwisehq.com · its alternatives →
4
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 →
5
Tiktokenizer▲6
Tokenization visualization tool for llms
2025 · its alternatives →
6
Reducing LLM input tokens by 70%▲56
Run GPT-OSS, Qwen, Llama, and DeepSeek through one OpenAI-compatible API with streaming and clear usage-based pricing.
May 2026 · adola.app · its alternatives →
7
Byte Size Text Visualizer▲5
2024 · unli.xyz · its alternatives →
8
Badge that shows how well your codebase fits in an LLM's context window▲88
Small codebases were always a good thing. With coding agents, there's now a huge advantage to having a codebase small enough that an agent can hold the full thing in context. Repo Tokens is a GitHub Action that counts your codebase's size in tokens (using tiktoken) and updates a badge in your README. The badge color reflects what percentage of an LLM's context window the codebase fills: green for under 30%, yellow for 50-70%, red for 70%+. Context window size is configurable and defaults to 200k (size of Claude models). It's a composite action. Installs tiktoken, runs ~60 lines of inline…
Feb 2026 · github.com · its alternatives →
9AS
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 →
10
An unmetered LLM API–$6/month, no token tracking, no limits▲12
Hi HN, I was once given the advice: Don't waste expensive frontier model credits (GPT/Claude/etc.) on bulk work. Send the boring, repetitive, high-volume jobs to a smaller model, and save the expensive prompts for when you actually need frontier-level reasoning. I complained and told my manager that I shouldnt have to think about using certain models for certain coding tasks, and that one model should handle everything. Well, here we are anyway. If anyone needs a place to absolutely abuse an LLM with high-volume tasks, come beat ours up at https://yolo-auto.com. Here are…
Jul 2026 · yolo-auto.com · its alternatives →
11
Token Forecaster▲65
Forecast how long an LLM reply will run, before Enter
28d ago · github.com · its alternatives →
12AT
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 →
13
Long term Memory and 50M token window for LLM▲6
Join the discussion on this paper page
14d ago · huggingface.co · its alternatives →
14
Byte-Pair Encoding tokenizer for training LLMs on large datasets▲5
Byte-Pair Encoding tokenizer for training large language models on huge datasets - jmaczan/bpe-tokenizer
2024 · github.com · its alternatives →
15
LLM Thought Visualization▲24
To know what models don't say out loud. Contribute to ninjahawk/Subtext development by creating an account on GitHub.
Jul 2026 · github.com · its alternatives →
16
Tokens API▲9
Input token counts and costs for over 160 AI models.
2024 · zatomic.ai · its alternatives →
17
I implemented evals metrics for LLMs that runs locally on your machine▲22
The LLM Evaluation Framework. Contribute to confident-ai/deepeval development by creating an account on GitHub.
2023 · github.com · its alternatives →
18
A text format for UI wireframes – comparing token costs across 4 format▲5
I've been exploring how to describe UI layouts to LLMs efficiently. The problem: When you ask an AI to generate or modify UI, how do you describe the current state? - Natural language ("header on top, form below") is ambiguous - ASCII art breaks when edited (alignment issues) - HTML is precise but verbose I ran some measurements. For a simple login form: - Natural language: 102 tokens - ASCII art: 84 tokens - HTML: 330 tokens I experimented with a grid-based text format using Excel-like cell references: grid: 4x3 A1..D1: { type: txt, value: "Login" } A2..D2: { type: input, label: "Email" }…
Feb 2026 · github.com · its alternatives →
19
TokScope▲8
Interactive Token Embedding Visualization Tool of LLMs
2025 · its alternatives →
20
LLM Token Counter▲3
Estimate prompt cost before you call the LLM API
May 2026 · singhajit.com · its alternatives →
21
`tc` like `wc` but for LLM tokens▲8
Little tool that I made to understand how (un)reasonable my prompts are.
Jan 2026 · github.com · its alternatives →
22
Keep large tool output out of LLM context: 3x accuracy 95% fewer tokens▲10
LLM agents often place raw JSON tool outputs directly in the prompt. After a few tool calls, earlier results get compacted or truncated and answers become incorrect or inconsistent. I built Sift, a drop-in MCP gateway that stores tool outputs as local artifacts (filesystem blobs indexed in SQLite) and returns an `artifact_id` plus compact schema hints when responses are large or paginated. Instead of reasoning over full JSON in the prompt, the model runs a small Python query: def run(data, schema, params): return max(data, key=lambda x: x["magnitude"])["place"] Query code runs in a…
Mar 2026 · github.com · its alternatives →
23
A 6M-token movable window on a single 46GB GPU▲7
Abstract page for arXiv paper 2607.23806: A Frozen 12B Beats Frontier Models on Verified Work: 100% Accuracy, 0 Tokens, Bit-Exact, Forever
Jul 2026 · arxiv.org · its alternatives →
24
HtmlSlim▲6
Save 90% LLM tokens by optimized HTML input
2025 · its alternatives →

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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 →