AI · alternatives · 2026

24 alternatives to LLM Tree Navigation Benchmark

Measures the ability of various LLMs to navigate a fictional codebase via iterative directory tree expansion and observation. Each model's baseline ability is compared against combinations of various prompt engineering mods to quantify exactly how much they help or hinder the LLM. Interesting findings here: https://github.com/aiwebb/treenav-bench#interesting-findings

Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. LLM Tree Navigation Benchmark launched in 2024; newer entries below may have overtaken it.

1
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 →
2
Find the best local LLM for your hardware, ranked by benchmarks▲283
Find the local LLM that actually runs and performs best on your hardware. Ranked by real, recency-aware benchmarks, not parameter count. One command, run it instantly. - Andyyyy64/whichllm
May 2026 · github.com · its alternatives →
3
LocalScore – Local LLM Benchmark▲124
Hey Folks! I've been building an open source benchmark for measuring local LLM performance on your own hardware. The benchmarking tool is a CLI written on top of Llamafile to allow for portability across different hardware setups and operating systems. The website is a database of results from the benchmark, allowing you to explore the performance of different models and hardware configurations. Please give it a try! Any feedback and contribution is much appreciated. I'd love for this to serve as a helpful resource for the local AI community. For more check out: - Website:…
2025 · localscore.ai · its alternatives →
4
LLM Explorer▲152
Find your best LLM for a local inference
2023 · its alternatives →
5
finetune LLMs via the Finetuning Hub▲80
Hi HN community, I have been working on benchmarking publicly available LLMs these past couple of weeks. More precisely, I am interested on the finetuning piece since a lot of businesses are starting to entertain the idea of self-hosting LLMs trained on their proprietary data rather than relying on third party APIs. To this point, I am tracking the following 4 pillars of evaluation that businesses are typically look into: - Performance - Time to train an LLM - Cost to train an LLM - Inference (throughput / latency / cost per token) For each LLM, my aim is to benchmark them for…
2023 · github.com · its alternatives →
6AT
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 →
7
Deepmark AI▲97
LLM benchmarking tool for task-specific metrics on your data
2023 · github.com · its alternatives →
8
CodeRLM – Tree-sitter-backed code indexing for LLM agents▲81
I've been building a tool that changes how LLM coding agents explore codebases, and I wanted to share it along with some early observations. Typically claude code globs directories, greps for patterns, and reads files with minimal guidance. It works in kind of the same way you'd learn to navigate a city by walking every street. You'll eventually build a mental map, but claude never does - at least not any that persists across different contexts. The Recursive Language Models paper from Zhang, Kraska, and Khattab at MIT CSAIL introduced a cleaner framing. Instead of cramming everything into…
Feb 2026 · github.com · its alternatives →
9UD
Unify – Dynamic LLM Benchmarks and SSO for Multi-Vendor Deployment▲91
Hey HN! I’m the founder of Unify, and we’ve just released our Model Hub, which provides a collection of LLM endpoints with live runtime benchmarks all plotted across time: https://unify.ai/hub A key finding is that static tabular runtime benchmarks for LLMs simply do not work. It’s necessary to take a time-series perspective, and plot the variations through time. We currently have 21 models provided by: Anyscale, Perplexity AI, Replicate, Together AI, OctoAI, Mistral AI and OpenAI, with more on the roadmap. We test across different regions (Asia, US, Europe), with varied…
2024 · its alternatives →
10
I ported Tree-sitter to Go▲222
This started as a hard requirement for my TUI-based editor application, it ended up going in a few different directions. A suite of tools that help with semantic code entities: https://github.com/odvcencio/gts-suite A next-gen version control system called Got: https://github.com/odvcencio/got I think this has some pretty big potential! I think there's many classes of application (particularly legacy architecture) that can benefit from these kinds of analysis tooling. My next post will be about composing all these together, an exciting project I call…
Feb 2026 · github.com · its alternatives →
11
BenchLLM by V7▲133
Test-driven development for LLMs
2023 · benchllm.com · its alternatives →
12
ChainForge, a visual tool for prompt engineering and LLM evaluation▲177
Hi HN! We’re been working hard on this low-code tool for rapid prompt discovery, robustness testing and LLM evaluation. We’ve just released documentation to help new users learn how to use it and what it can already do. Let us know what you think! :)
2023 · chainforge.ai · its alternatives →
13
LLM Verified with Monte Carlo Tree Search▲102
This is a weekend hack that I'd like to further develop as it's working surprisingly well. Using MCTS, we can explore a space of possible verified programs with an LLM. We check the partial programs at each step, and so steer towards programs that pass the verifier. https://github.com/namin/llm-verified-with-monte-carlo-tree-...
2023 · github.com · its alternatives →
14
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 →
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 →
16
Improve LLM Performance by Maximizing Iterative Development▲104
I have been working in AI space for a while now, first at FAANG with ML since 2021, then with LLM in start-ups since early 2023. I think LLM Application development is extremely iterative, more so than any other types of development. This is because to improve an LLM application performance (accuracy, hallucinations, latency, cost), you need to try various combinations of LLM models, prompt templates (e.g., few-shot, chain-of-thought), prompt context with different RAG architecture, different agent architecture, and more. There are thousands of possible combinations and you need a process…
2024 · github.com · its alternatives →
17
Parsimony – an open-source benchmark for how much code AI models add▲77
An open-source benchmark of how much code AI models add.
8d ago · parsimony.lasu.dev · its alternatives →
18
LLM, a Rust Crate/CLI for CPU Inference of LLMs (LLaMA, GPT-NeoX, etc.)▲45
G'day, HN! I'm one of the maintainers of `llm`. I've been working alongside a trusty group of contributors to bring this project to life, and we're now at a point where we're ready to share it with the world. Large language models (LLMs) are taking the computing world by storm due to their emergent abilities that allow them to perform a wide variety of tasks, including translation, summarization, code generation, and even some degree of reasoning. However, the ecosystem around LLMs is still in its infancy, and it can be difficult to get started with these models. `llm` is a one-stop shop for…
2023 · github.com · its alternatives →
19
Cheddar-bench – unsupervised benchmark for coding agents▲9
I built a small benchmark to test CLI coding agents on blind bug detection. A challenger agent injects bugs and writes ground truth (`bugs.json`). A different reviewer agent audits the repo without seeing ground truth, and an LLM matcher scores bug-to-finding assignments. Current run: 50 repos, 150 challenges, 450 reviews, 2,603 injected bugs. Weighted detection: Claude 58.05%, Codex 37.84%, Gemini 27.81%. LLM-judge benchmarks are easy to get wrong, so I’d really appreciate critical feedback on benchmark fairness, scoring/matching methodology, and obvious failure modes I’m missing. Full…
Feb 2026 · github.com · its alternatives →
20
Open Source LLM Performance Tracker▲28
An open source Next app template to monitor your AI apps
2025 · github.com · its alternatives →
21
Llm-benchmark – Benchmarks LLM-optimized code across multiple providers▲8
Contribute to thomasdavis/llm-benchmark development by creating an account on GitHub.
2025 · github.com · its alternatives →
22
New Benchmark from SWE-bench team is 0% solved▲24
ProgramBench evaluates whether language models can rebuild programs from scratch.
May 2026 · programbench.com · its alternatives →
23
Open LLM Benchmark▲1
Reproducible benchmarks for evaluating AI models
Aug 2026 · github.com · its alternatives →
24
LLM Playground, compare results side by side▲14
Test and compare results from Large Language Models (LLMs) simultaneously, including Anthropic's Claude 1.2, OpenAI's GPT-4, GPT-3.5 turbo, and Cohere's CommandXL Nightly. Analyze the results side-by-side in a single UI on top of LLM APIs.
2023 · retool.com · 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 →