Even more Terminal-Bench!

We've pushed a version update to the Terminal-Bench dataset and leaderboard. Terminal-Bench 4.0 calibrates task resources (time, cpu, memory), implements task fixes, and removes saturated tasks.

Very excited about this new direction for Terminal-Bench!

🚀New Paper arxiv.org/abs/2606.28551 Everyone obsesses over VLM architectures & training recipes. But what about the data? Presenting the latest work in the DataComp-series: a testbed for VLM data curation with 1,000+ controlled experiments and some surprising lessons 👀 🧵👇

Honored to have Terminal-Bench-Science included in Slingshots // THREE, alongside such a strong lineup of researchers and projects. Building a benchmark to evaluate AI agents on computational workflows across the natural sciences — authored and verified by real domain experts. Grateful for the incredible support from @LaudeInstitute & @bradenjhancock, and to all our contributors making this happen. ⚛️🧪 Check out the current progress on our brand-new task submission dashboard: stevendillmann.github.io/tb-…

Very excited to release the next project in the DataComp / OpenThoughts line of research! Like OpenThoughts we worked on post-training data, this time with a focus on agentic models.

Introducing Terminal-Bench Challenges! A new capability has emerged at the frontier: agents completing large-scale projects autonomously. To test this capability, we felt another flavor of benchmark was needed. Terminal-Bench Challenges are long-horizon, token-intensive, single-task benchmarks. Today we are releasing our first 3 challenges.

📣 Announcing Terminal-Bench Science: benchmarking AI agents on real scientific workflows – now open for task contributions👇 tbench.ai/news/tb-science-an… @AnthropicAI, @OpenAI, and @GoogleDeepMind use Terminal-Bench to evaluate AI on coding tasks. We're now extending it to scientific workflows. 1/6🧵

Personal update: I've joined Anthropic. I think the next few years at the frontier of LLMs will be especially formative. I am very excited to join the team here and get back to R&D. I remain deeply passionate about education and plan to resume my work on it in time.

Excited to welcome Andrej to the Pretraining team! He'll be building a team focused on using Claude to accelerate pretraining research itself. I can’t think of anyone better suited to do it — looking forward to what we build together!

We're releasing Terminal-Bench 2.1 to patch 28 of the 89 tasks in Terminal-Bench 2.0 TB2.1 includes • recalibrated limits • fixed solutions • realigned verifiers Per-task breakdowns in 🧵 We'll continue to support TB2 and TB2.1 leaderboards (new submission process 🔜)

How much of SQLite, FFmpeg, PHP compiler can LMs code from scratch? Given just an executable and no starter code or internet access. Introducing ProgramBench: 200 rigorous, whole-repo generation tasks where models design, build, and ship a working program end to end. 🧵

Announcing Talkie: a new, open-weight historical LLM! We trained and finetuned a 13B model on a newly-curated dataset of only pre-1930 data. Try it below! with @AlecRad and @status_effects 🧵

New work with @AlecRad and @DavidDuvenaud: Have you ever dreamed of talking to someone from the past? Introducing talkie, a 13B model trained only on pre-1931 text. Vintage models should help us to understand how LMs generalize (e.g., can we teach talkie to code?). Thread:

A statement from Anthropic CEO Dario Amodei: anthropic.com/news/where-sta…

A statement on the comments from Secretary of War Pete Hegseth. anthropic.com/news/statement…

Releasing the official SkyRL + Harbor integration: a standardized way to train terminal-use agents with RL. From the creators of Terminal-Bench, Harbor is a widely adopted framework for evaluating terminal-use agents on any task expressible as a Dockerfile + instruction + test script. This integration extends it: the same tasks you evaluate on, you can now RL-train on. Blog: novasky-ai.notion.site/skyrl… 🧵

Terminal-Bench is a leading benchmark for agents. Unfortunately it’s hard: most small coding agents get very low scores on TB2, so training/system ablations look flat - you can't tell what's working. Announcing OpenThoughts-TBLite - 100 curated TB2-style tasks, difficulty-calibrated so even 8B models can make progress. It's designed to give researchers measurable signal during development, providing faster feedback for experimental iteration while closely tracking true TB2 performance🧵

The Terminal-Bench paper is here! Read it to learn where frontier models still fail and the secrets of how we sourced hundreds of high quality environments from our open source community. 🧵