AI · alternatives · 2026
24 alternatives to CivBench a long-horizon AI benchmark for multi-agent games
Hey HN! I built ClashAI to be an open agent scoreboard where frontier models play against each other in environments like Civilization and other strategy games. Every match is streamed live with the AI thinking fully observable. The agent rankings will be continually updated and reflected as we add environments. Brief notes on CivBench Season #001: - 200 turn limit - Starting with 8 of the top 42 agents we’ve tested in a standardized harness - 90s reasoning timeout (timed with thinking config per model card) - live benchmark, still growing sample size What’s been interesting so far: Models…
CivBench is a benchmark that tests frontier AI models against each other in long-horizon strategy games like Civilization. Models compete in streamed matches with fully observable reasoning, and their rankings update… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.
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Hi HN, I built TetrisBench, a benchmark that tests LLMs on real-time code generation and reasoning through Tetris. Live: https://tetrisbench.com/ *How it works:* Each model starts with an initial optimization function for evaluating Tetris moves. As the game progresses, the model sees the current board state and updates its algorithm—adapting its strategy based on how the game is evolving. The model continuously refines its optimizer: - Board getting too high? Prioritize clearing lines. - Hole forming? Adjust penalties. - Safe stack? Build for a Tetris. The model generates…
Jan 2026 · tetrisbench.com · its alternatives →
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GraphBit▲437We built GraphBit to solve the reliability gap in AI frameworks. It’s built on a Rust execution core with a Python layer for accessibility — deterministic orchestration, lock-free concurrency, and built-in observability. We’d love feedback from the HN community, especially from those running multi-LLM or agentic workflows at scale.
2025 · graphbit.ai · its alternatives →
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Scored 65.2% vs google's official 47.8%, and the existing top closed source model Junie CLI's 64.3%. Since there are a lot of reports of deliberate cheating on TerminalBench 2.0 lately (https://debugml.github.io/cheating-agents/), I would like to also clarify a few things 1. Absolutely no {agents/skills}.md files were inserted at any point. No cheating mechanisms whatsoever 2. The cli agent was run in leaderboard compliant way (no modification of resources or timeouts) 3. The full terminal bench run was done using the fully open source version of the agent, no…
Apr 2026 · github.com · its alternatives →
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We built a computer use agent once and got it benchmarked at the 2nd position in the biggest benchmark for this field but in the process, we realized one thing, how outdated and inefficient these benchmarks are and how they don't truly capture how these tasks went. So we thought a community led benchmarking arena would be the way to go about. https://coarena.ai/. You just have to put in a task and two side by side windows with hidden models will show up and do the same task and at the end you vote which one did better(or none). We have a leaderboard where we rank all the…
Aug 2026 · coarena.ai · its alternatives →
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oqoqo▲325Most benchmarks today exist in curated environments and do not translate well to the real world. We built Oqoqo to bridge this gap. Oqoqo makes it super simple to build realistic evals and custom benchmarks for tasks users actually care about. Oqoqo can: - Reliably measure how agent friendly your product surfaces are against Codex, Claude Code, OpenClaw, Hermes, Pi, Opencode, Cursor, GitHub Copilot - Regression test MCP, CLI, skills, SDK, and any agent facing interface (we are continuously using Oqoqo to dogfood and improve our own MCP/CLI) - Create and share custom benchmarks for how…
Aug 2026 · oqoqo.ai · its alternatives →
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Hi HN! We’re the team behind Fiddler, and we recently built an Agent Cache feature. It works by capturing an LLM request/response pair and, once caching is enabled, serving subsequent matching calls locally so duplicate requests never hit the provider. It’s meant to reduce three pains that show up together in agent development: token cost, feedback-loop latency, and output non-determinism. We’d love to hear your thoughts.
Aug 2026 · telerik.com · its alternatives →
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PinchBench▲305Find the best AI model for your OpenClaw
Mar 2026 · pinchbench.com · its alternatives →
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Open-source collaborative agentic workspace for data teams
Sep 2026 · alkera.ai · its alternatives →
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After training calculator agent via RL, I really wanted to go bigger! So I built RL infrastructure for training long-horizon terminal/coding agents that scales from 2x A100s to 32x H100s (~$1M worth of compute!) Without any training, my 32B agent hit #19 on Terminal-Bench leaderboard, beating Stanford's Terminus-Qwen3-235B-A22! With training... well, too expensive, but I bet the results would be good! *What I did*: - Created a Claude Code-inspired agent (system msg + tools) - Built Docker-isolated GRPO training where each rollout gets its own container - Developed a multi-agent…
2025 · github.com · its alternatives →
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- 11BW
I remember watching the AlphaGo documentary in 2017. What stood out to me was that the model got drastically better when it started competing against itself. GANs clicked for me similarly: a generator and discriminator competing, and somehow the competition is what produces something remarkable. I've been curious whether this principle generalizes to today's agents. So mehulkalia and I built Browser Brawl at the YC / BrowserUse hackathon last weekend and won first place. It is a fun experiment in which an attacker agent tries to complete tasks on live websites while a defender agent…
Mar 2026 · browser-brawl.com · its alternatives →
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APIEval-20▲121An open benchmark for AI agents that test APIs
May 2026 · resources.kusho.ai · its alternatives →
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PokerBench is my attempt at a new LLM benchmark wherein frontier models play Texas Hold'em in an arena setting. It also features a simulator to view individual games and observe how the different models reason about poker strategy. Opus/Haiku, Gemini Pro/Flash, GPT-5.2/5 mini, and Grok 4.1 Fast Reasoning have all been included. All code -> https://github.com/JoeAzar/pokerbench
Jan 2026 · pokerbench.adfontes.io · its alternatives →
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Hey HN, We built a different kind of AI benchmark for UI generation. Instead of static leaderboards or curated screenshots, you can watch multiple models generate the same design live, side-by-side, and decide which output is actually better. Under the hood, we call AI models from Anthropic (Opus), OpenAI (GPT), Google (Gemini), and Moonshot AI (Kimi). Each model generates a real, editable project using Tailwind CSS (not screenshots or canvas exports). You can export it for Next.js, Laravel (Blade), Symfony (Twig), WordPress, or plain HTML. What we noticed building this: * Popular benchmarks…
Feb 2026 · shuffle.dev · its alternatives →
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I’ve been using AI to generate some repetitive frontend (guilty), and while most outputs felt vibe-coded, some results were surprisingly good. So I cleaned it up and made a ranking game out of it with friends, and you can check it out here: https://www.designarena.ai/vote /vote: Your prompt will be answered by four random, anonymous models. You pick the one you prefer and crown the winner, tournament-style. /leaderboard: See the current winning models, as dictated by voter preferences. /play: Iterate quickly by seeing four models respond to the same input and…
2025 · designarena.ai · its alternatives →
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We build runtime security for AI agents. The playground started as an internal tool that we used to test our own guardrails. But we kept finding the same types of vulnerabilities because we think about attacks a certain way. At some point you need people who don't think like you. So we open-sourced it. Each challenge is a live agent with real tools and a published system prompt. Whenever a challenge is over, the full winning conversation transcript and guardrail logs get documented publicly. Building the general-purpose agent itself was probably the most fun part. Getting it to reliably use…
Mar 2026 · github.com · its alternatives →
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I'm an "ideas person" who messes around with AI on a low budget. I got tired of watching my tokens vanish and context windows filling up while agents fumbled around trying to find the right thing. Agents don't flail like they used to with shell tools, but there are still weak/blind spots and back-and-forth episodes — especially when using tools in combination/sequence. So I built "tilth" today. Or rather, AI built it — every line is Opus 4.6. I spent a lot of my precious tokens getting it to "not shit" (at least several of the different vendors' AI overlords assure me it's not…
Feb 2026 · github.com · its alternatives →
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An open-source cost controller for AI agent loops — stops a loop when it's actually converged and rolls back before it degrades, instead of running to a fixed max_iterations cap. Real-time loop-gain (Aβ) bands + best-so-far rollback. Adapters for LangGraph, CrewAI, AutoGen, LangChain, OpenAI Agents,
Jul 2026 · github.com · its alternatives →
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A vendor-neutral Kubernetes incident benchmark for testing AI investigation tools across detection, diagnosis, and mitigation. Deploy faults, capture investigations, and compare results with a configurable AI judge. - edgedelta/project-arena
15d ago · github.com · its alternatives →
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Hey HN, Automated research is the next big step in AI, with companies like OpenAI aiming to debut a fully automated researcher by 2028 (https://www.technologyreview.com/2026/03/20/1134438/openai-i...). However, there is a very real possibility that much of this corporate research will remain closed to the general public. To counter this, we spent the last month building Enlidea---a machine-to-machine ecosystem for open research. It's a decentralized research hub where autonomous agents propose hypotheses, stake bounties, execute code, and perform automated…
Mar 2026 · enlidea.com · its alternatives →
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τ-Bench is an open benchmark for evaluating AI agents on grounded, multi-turn customer service tasks with verifiable outcomes. It's been great to see the community adopt it since launch — this is now the third iteration. With τ³-Bench, we're extending it to two new settings: knowledge-intensive retrieval and full-duplex voice. τ-Knowledge: agents must navigate ~700 interconnected policy documents to complete multi-step tasks. Best frontier model (GPT-5.2, high reasoning) hits ~25%. The surprising part: even when you hand the model the exact documents it needs, performance only reaches ~40%.…
Mar 2026 · 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 →