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Testing GLM 5.2 with FP8 quantization and FP8 KV cache on H200 yields a score of 79.8% on Terminal-Bench 2.1, with one timeout not rerun.
Maka's Harness project improved the self-check mechanism, enabling DeepSeek Flash V4 to achieve evaluation results close to GLM-5.2 on the terminal-bench sample set, completing 10 programming agent tasks with only 4 RMB and a 97.5% cache hit rate.
Alex Atallah highlights that cost per task is more meaningful than price per token, citing Terminal-Bench results where Haiku is 10x the cost of Opus.
New Sonnet 5 model achieves Opus 4.8 level performance on Terminal-Bench at less than half the cost, with improved refusal of prompt injection attacks, now available in Cline.
The Orinth-1.0-35b MoE outperforms Qwen 3.6 35b on Terminal-Bench 2.1 and SWE Atlas benchmarks.
GLM-5.2 matches Claude Opus on 45 coding-agent tasks at lower cost, with 43 of 45 tasks having identical outcomes.
Ai2 and the University of Washington released a paper titled Tmax, proposing the strongest open-source terminal agent RL training recipe to date. A 9B parameter model outperforms larger models on Terminal-Bench 2.0, with the key being low-cost generation of vast amounts of verifiable training data, not model size or algorithm.
GLM-5.2 is the first open-weights model to exceed 80% on Terminal-Bench, surpassing all other open models and even Gemini, making it a frontier-level model at a fraction of the cost.
Sentra's Code Memory system boosts GPT-5.5 to 88.31% on Terminal-Bench 2.1 at a quarter of the cost, outperforming Anthropic's restricted Mythos 5 model. The memory layer reduces input tokens by 52% and costs by 72.6% while improving task success rates.
Nex-AGI releases Nex-N2, an open-source agentic model series (Nex-N2-Pro and Nex-N2-mini) with an Agentic Thinking framework that unifies reasoning, tool use, and environment execution, achieving top-tier performance on agentic and coding benchmarks.
Ante is a lightweight, self-contained terminal agent harness written in Rust, designed to be fast and dependency-free. It topped Terminal Bench 2.0 and remains highly responsive to user feedback despite being in preview and not yet open-sourced.
A Meta paper shows that coding agents improve significantly when they reuse short summaries of past attempts instead of raw logs, achieving strong gains on SWE-Bench and Terminal-Bench with Claude 4.5 Opus.
Qwen3.6-35B-A3B and Qwen3.5-9B models are officially on the Terminal-Bench 2.0 leaderboard, with little-coder achieving 24.6% on the 35B variant, surpassing Gemini 2.5 Pro and Qwen3-Coder-480B, while the 9B model shows that sub-10B local models can compete on hard agentic benchmarks.
HALO uses RLMs to optimize AI agent harnesses by analyzing execution traces and suggesting improvements, achieving 10%+ gains on several benchmarks like Terminal-Bench and AppWorld.