AI · alternatives · 2026

24 alternatives to LLMs consume 5.4x less mobile energy than ad-supported web search

The standard AI energy debate compares server-side LLM inference to a server-side Google query. I think this misses most of what actually happens on a mobile device during a real search session. I built a parametric model of the full end-to-end mobile search session: 4G/5G radio energy, SoC rendering cost for a 2.5MB page, programmatic advertising RTB auctions running in the background, and network transmission costs for both sides. Then compared it to an equivalent LLM session. Main finding across 10,000 Monte Carlo draws: on mobile, a standard LLM session uses on average 5.4x less…

This is a parametric energy consumption model comparing mobile LLM sessions to ad-supported web search. It accounts for end-to-end mobile factors including 4G/5G radio energy, device rendering costs for typical web… 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
Why write code if the LLM can just do the thing? (web app experiment)▲436
I spent a few hours last weekend testing whether AI can replace code by executing directly. Built a contact manager where every HTTP request goes to an LLM with three tools: database (SQLite), webResponse (HTML/JSON/JS), and updateMemory (feedback). No routes, no controllers, no business logic. The AI designs schemas on first request, generates UIs from paths alone, and evolves based on natural language feedback. It works—forms submit, data persists, APIs return JSON—but it's catastrophically slow (30-60s per request), absurdly expensive ($0.05/request), and has zero UI…
Nov 2025 · github.com · its alternatives →
2
LLM Stats▲293
I was overspending on GPT-4o. It was really hard to compare different models I could switch to, so I built this LLM comparison tool. It shows leaderboards, pricing, and performance data across 100+ LLMs (including all major providers and open-source models). Key features: - Live pricing comparisons - Benchmark Scores (MMLU, HumanEval, GPQA, etc.) - Context length vs cost analysis - Speed/throughput tests across providers - Quality vs price visualizations - Open source (all data verifiable) Try it out: https://llmstats.com I'd like to know your opinion :) Tech stack: Next.js,…
Oct 2025 · llm-stats.com · its alternatives →
3
I've built a locally running Perplexity clonefree▲669
The video demo runs a 7b Model on a normal gaming GPU. I think it already works quite well (accounting for the limited hardware power). :)
2024 · github.com · its alternatives →
4
Humans vs LLMs▲138
Hi HN, We just launched [AI Page Ready](https://aipageready.com) — a tool to check if your web pages are discoverable and understandable by AI tools like ChatGPT, Claude, Gemini, and Perplexity. It runs 40+ checks across six areas: Discoverability & Crawlability, Structured Data & Semantics, Content Quality, Answer-Ready Formatting, Accessibility & Rendering, and Deep AI LLM-specific checks. Would love your feedback — especially if you work in eCommerce, SEO, content, SaaS, or are trying to get traffic from AI tools. (Also open to ideas on what else to add to the tool!)
2025 · aipageready.com · its alternatives →
5
LLM Pricing▲178
LLMs price comparison tool developed and updated by LLM
2024 · ailesson.io · its alternatives →
6
RunAnywhere▲119
Ollama but for mobile, with a cloud fallback
2025 · runanywhere.ai · its alternatives →
7RC
Reduce ChatGPT costs 10x with distributed cache for LLMs▲22
Hello HN! We're building a caching solution for LLMs (ChatGPT, Claude). By combining cutting-edge approaches, such as edge computing, prompt compression, vectorization, and others - it can reduce your AI bills by up to 10x and significantly lower response times. Key Features: - cost efficiency: our system stores frequent queries, reducing the number of upstream (paid) API calls - fast responses: with various nodes globally, we reduce latency by serving data from the nearest location - scalability: designed to handle increasing loads and data sizes without degrading performance. The cache…
2024 · edgematic.dev · its alternatives →
8AT
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 →
9AL
An LLM purpose built for data annotation, outperforms GPT-3.5▲9
Try it out here: https://labs.refuel.ai/playground Refuel LLM (84.2%) outperforms trained human annotators (80.4%), GPT-3-5-turbo (81.3%), PaLM-2 (82.3%) and Claude (79.3%) across a benchmark of 15 text labeling datasets. It is a Llama-v2-13b base model, trained on over 2500 unique datasets (5.24B tokens) spanning categories such as classification, entity resolution, matching, reading comprehension and information extraction.
2023 · its alternatives →
10
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 →
11NL
New LLM outperforming GPT-3.5▲6
Refuel LLM (84.2%) outperforms trained human annotators (80.4%), GPT-3-5-turbo (81.3%), PaLM-2 (82.3%) and Claude (79.3%) across a benchmark of 15 text labeling datasets. It is a Llama-v2-13b base model, trained on over 2500 unique datasets (5.24B tokens) spanning categories such as classification, entity resolution, matching, reading comprehension and information extraction. Here is the interactive demo: https://labs.refuel.ai/playground. Pretty fun to play with!
2023 · its alternatives →
12
Narev▲7
Hey! I want to get your feedback on our A/B testing platform for LLMs. Giving a 100 day Pro trial to anyone who signs up this week. The premise: in 10 minutes compare a bunch of model/parameter/prompt combos to find the best point on the quality-latency-cost tradeoff. Enter your data manually or sync from your tracing platform. ~300 models to select from. Here is a 1 minute Loom: https://www.loom.com/share/39b7cd24166c4e1fafd5e7fbd12a9d4d?...
2025 · narev.ai · its alternatives →
13
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 →
14BA
Butter, a muscle memory cache for LLMs▲23
Hi HN, Erik here. Today we launch Butter, an OpenAI-compatible API proxy that caches LLM generations and serves them deterministically on revisit. Since April, we’ve been working on this concept of “muscle memory,” or deterministic replay, for agent systems performing automations. You may recall our first post in May, launching a python package called Muscle Mem: https://news.ycombinator.com/item?id=43988381 Since then, the product has evolved entirely, now taking the form of an LLM Proxy. For a deep dive into this process, check out:…
2025 · docs.butter.dev · its alternatives →
15
A benchmark + latency sim for LLM db queries: ClickHouse / Postgres▲12
Performance comparison of ClickHouse vs PostgreSQL using LLM-style query patterns on realistic aircraft tracking data - GitHub - 514-labs/LLM-query-test: Performance comparison of ClickHouse vs PostgreSQL using LLM-style query patterns on realistic aircraft tracking data
2025 · github.com · its alternatives →
16IB
I built an on-device LLM based chatbot for iPhones▲20
I’ve been playing around with local LLMs for the past couple of months and decided to build something that can run on an iPhone. It’s a universal app built with SwiftUI and the excellent ggml library. The model is an SFT fine tuned and 4 bit quantised version of the RedPajama-INCITE-Chat-3B-v1 OSS LLM. It works reasonably well on recent-ish (~3 year old) iPhones, iPads and Macs. It was launched on the App Store yesterday[1] and Product Hunt today[2]. It seems to be reasonably ok at natural language interactions, but given its size, does pretty badly at coding and reasoning. Also, it…
2023 · its alternatives →
17
SemanticGuard▲7
Cuts your LLM API costs by 40-70%. One line of code.
May 2026 · semanticguard.dev · its alternatives →
18
AnyLLM▲20
10+ LLMs at 10x speed. Think we're kidding. Test it now!
2025 · its alternatives →
19
Minimal LLM Post-Training Experiments on an 8GB GPU (SFT, DPO, GRPO)▲21
Minimal, readable LLM post-training experiments on one 8GB GPU. Measures forgetting, seed variance, and RL emergence. - pochenai/nano-llm-posttraining
Aug 2026 · github.com · its alternatives →
20TF
The fastest way to run Mixtral 8x7B on Apple Silicon Macs▲18
I’d originally launched my app: Private LLM[1][2] on HN around 10 months ago, with a single RedPajama Chat 3B model. The app has come a long way since then. About a month ago, I added support for 4-bit OmniQuant quantized Mixtral 8x7B Instruct model, and it seems to outperform Q4 models at inference speed and Q8 models at text generation quality, while consuming only about 24GB of RAM[3] at 8k context length. The trick is: a) to use a better quantization algorithm and b) to use unquantized embeddings and the MoE gates (the overhead is quite small). Other notable features include many more…
2024 · its alternatives →
21
I open-sourced a $1M engine for closing loops in embedding space▲8
Hey folks, I’m the creator of WFGY — a semantic reasoning framework for LLMs. After open-sourcing it, I did a full technical and value audit — and realized this engine might be worth $8M–$17M based on AI module licensing norms. If embedded as part of a platform core, the valuation could exceed $30M. Too late to pull it back. So here it is — fully free, open-sourced under MIT. --- ### What does it solve? Current LLMs (even GPT-4+) lack *self-consistent reasoning*. They struggle with: - Fragmented logic across turns - No internal loopback or self-calibration - No modular thought units - Weak…
2025 · github.com · its alternatives →
22LS
Llm.sql – Run a 640MB LLM on SQLite, with 210MB peak RSS and 7.4 tok/s▲8
Hi HN, I built llm.sql, an LLM inference framework that reimagines the LLM execution pipeline as a series of structured SQL queries atop SQLite. The motivation: Edge LLMs are getting better, but hardware remains a bottleneck, especially RAM (size and bandwidth). When available memory is less than the model size and KV cache, the OS incurs page faults and swaps pages using LRU-like strategies, resulting in throughput degradation that's hard to notice and even harder to debug. In fact, the memory access pattern during LLM inference is deterministic - we know exactly which weights are needed…
Apr 2026 · its alternatives →
23
Vector Cache▲9
A Python Library for Efficient LLM Query Caching
2024 · github.com · its alternatives →
24
I built a tool to check if your computer can run LLMs locally▲8
Built a simple web app that tells you which open-source LLMs will work on your hardware. It auto-detects your specs, shows compatible models from Hugging Face, gives realistic performance estimates (tokens/sec), and recommends quantization settings. You can also manually input specs to see "what if I upgraded my RAM?" Made this after wasting time downloading giant models only to find they crawled on my hardware. Hope it saves you some frustration!
2025 · caniusellm.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 →