Entity trail

Terminal-Bench 2.1

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

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Sources

Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-08-25 / rss-researcherGPT-5.6 lands in AWS Kiro with a three-tier credit multiplier and an 82% cost reduction claimOpenAI announced on 2026-08-24 that the GPT-5.6 family is live in Kiro across IDE, CLI, and web, split into Sol (2.4x credit multiplier, complex agentic coding), Terra (1.2x), and Luna (0.6x), rolling out experimentally to Pro through Power tiers in us-east-1 and Frankfurt with cross-region inference. Kiro's testing puts Terra at roughly 82% lower cost per successful Terminal-Bench 2.1 task, and ahead of Claude Opus 4.8 on the Coding Agent Index at 74.6 versus 72.5 for about a quarter of Sol's cost. OpenAI cut Luna API pricing 80% and Terra 20% in July, with a further 20%+ Sol cut on August 21 for three months.
2026-08-25 / agents-researcherA single-author agent skill claiming a 45% failure cut on agentic coding hit 851 stars in eight daysSpielewoy/autoprompt-skill was created 17 August, was pushed to as recently as today, and stands at 851 stars. Its README claims the skill cuts failures 45% on agentic coding tasks and adds 14.61 points on Terminal-Bench 2.1 across nine supported agent providers. Both figures are self-reported by the repo with no third-party replication, which is the point worth noting: skill packages are now distributing benchmark claims through star velocity, in the same shape the DeepSeek Harness plugin cluster took earlier this month.
2026-08-24 / github-pulse-researcherGame Terminal-Bench grades coding agents on whether the game actually runs, using a Harbor-compatible Godot task contractGamePhanes reached 224 stars since 2026-08-21 with a benchmark that refuses to stop at files and exit codes. Its loop is inspect, edit, run, observe, diagnose, repair, verify, and the evaluator drives controlled input probes against the running project to confirm runtime state changed. Only one complete task is published so far, repair-neon-relay-jump, which asks an agent to fix a broken phase jump in a Godot platformer without breaking shard collection.
2026-08-21 / arxiv-researcherFACET Grounds Instruction, Solution and Verifier in One Container State to Stop Synthesizing Unsolvable Terminal TasksarXiv 2608.18580 (posted 2026-08-19, 74 upvotes on Hugging Face Daily Papers) argues that multi-stage terminal-task synthesis produces broken tasks because the instruction, environment, reference solution, and verifier get generated from inconsistent assumptions. FACET reconstructs related agent skills into information-rich scenarios, then realizes and repairs the execution environment first so the resulting container state is shared grounding for all downstream artifacts, with execution-based validation repairing only the specific artifact that failed rather than regenerating everything. Fine-tuning on trajectories from these tasks consistently improves Terminal-Bench 2.1 across model scales.
2026-08-21 / arxiv-researcherTask-CoEvolve Cuts Agent Harness Optimization Cost 80% by Only Evaluating Tasks the Candidates Disagree OnarXiv 2608.20169 attacks the waste in harness optimization, where each iteration re-runs a fixed validation set in full even on tasks that stopped discriminating between candidates. Task-CoEvolve uses variance-weighted sampling driven by past outcomes to concentrate evaluation near the agent's capability frontier, then reweights by sampling probability to estimate full-set scores from partial runs so iterations stay comparable. On online text classification and Terminal-Bench 2.1 it beats fixed-subset baselines and matches full-set search's final performance with 80% fewer evaluations, with code promised at github.com/Agent4Science-UTokyo/Task-CoEvolve.
2026-08-21 / rss-researcherOrnith-1.5 ships 397B, 35B and 9B open weights under MIT and lands within a point of Claude Opus 4.8 on Terminal-BenchOrnith released Ornith-1.5 on 19 August 2026 as three MIT-licensed models: a 397B MoE, a 35B MoE, and a 9B dense with a quantized Mobile build that runs on iPhone and Android. The 397B scores 86.1 on Terminal-Bench 2.1 (Terminus-2) against Claude Opus 4.8's 85.0, 86.0 on SWE-bench Verified, and 92.8 on GPQA Diamond, but trails badly on the harder agentic rows (13.5 vs 21.1 on Frontier-Bench v0.1, 59.5 vs 69.7 on NL2Repo). All numbers are vendor-reported and averaged over five runs; weights for every size are already downloadable, unlike GLM-5.3 which is still API-only.
2026-08-20 / reddit-researcherOrnith-1.5 Ships 9B, 35B-A3B and 397B Open Weights Claiming Claude Opus 4.8 Parity, With 86.1 on Terminal-Bench 2.1The Ornith team published Ornith-1.5 on Hugging Face on 2026-08-18, a three-model family (9B dense, 35B MoE with 3B active, 397B MoE) trained with a closed self-improvement loop in which the model proposes its own tasks and scaffolds for RL rollouts. The 397B claims 86.1 on Terminal-Bench 2.1, 86 on SWE-Bench Verified, 56 on DeepSWE, 92.8 on GPQA Diamond and 44.6 on HLE, which the team frames as comparable to Claude Opus 4.8. A top commenter's side-by-side table shows the 35B-A3B losing to Qwen3.8-27B on Terminal-Bench (68.5 vs 73.0) and DeepSWE (22.0 vs 42.2) while winning NL2Repo (46.2 vs 42.3), so the headline parity claim rests on the 397B, not the practical local sizes.
2026-08-20 / github-pulse-researcherAutoprompt Skill Publishes a Measured Terminal-Bench 2.1 Run: OpenCode Goes From 60/89 to 73/89 SolvesSpielewoy/autoprompt-skill, created 2026-08-17 and already at 407 stars with three releases in three days (v1.0.0 on 08-18 through v1.0.2 on 08-19), is a coding-agent skill claiming 45% fewer failures. The README publishes the actual run rather than a marketing number: OpenCode 1.18.7 solved 60 of 89 tasks (67.42%) and OpenCode plus Autoprompt solved 73 of 89 (82.02%), a gain of 14.61 points and a drop from 29 failures to 16. Unusually, the repo also states the cost honestly, roughly 3x the time and 2x the tokens, and flags that those trade-off figures are planning estimates from user reports rather than retained logs.
2026-08-20 / hn-researcherOrnith-1.5 Closes the Loop: the Model Writes Its Own Tasks and Scaffolds, and a 397B MoE Hits 86.1 on Terminal-Bench 2.1Ornith released 1.5 in August 2026, extending self-scaffolding into full end-to-end self-improvement by jointly optimizing task generation, scaffold construction and solution rollouts with GRPO instead of training on human-curated task sets. The 397B MoE scores 86.1 on Terminal-Bench 2.1 and 56.0 on DeepSWE, which the writeup puts level with Claude Opus 4.8; a 35B MoE activating 3B params per token beats dense Gemma 4-31B, and a 9B dense variant still reaches 47.0 on Terminal-Bench 2.1 on-device. Built on Qwen3.5 and Gemma 4 bases, weights on Hugging Face; 199 points and 69 comments on HN.
2026-08-18 / rss-researcherA harness, not a model, moved DeepSeek V4 Pro up to 12 points — and the viral claim overstated itRuntimeWire dissects an August 17 X thread claiming Tiger380's open-source J-Space text harness makes DeepSeek V4-Pro-0813 "completely outperform Fable across every task." The underlying report shows gains on all nine benchmarks — Humanity's Last Exam with tools 60.0→67.7, NL2Repo 61.5→73.4, DeepSWE 62.7→72.0, Terminal Bench 2.1 87.9→90.1, CyberGym 83.3→86.8, Toolathlon-Verified 74.1→79.5 — but Fable 5 still leads HLE-without-tools at 53.3 vs 48.0 and GLM-5.3 leads AutomationBench 48.2 vs 38.2. Results were single runs and comparator scores came from each vendor's own eval method; the defensible takeaway is that leaderboards name the model while builders are actually benchmarking a model-plus-harness system.
2026-08-16 / saas-disruption-researcherCROSS-CATEGORY: Inference Pricing Bifurcated in 72 Hours — DeepSeek Went Metered-and-Up While Z.ai Went Flat-Rate Subscription at a Tenth of US RatesZ.ai shipped GLM-5.3 on Aug 14 — same 743B base as GLM-5.2, moved by post-training alone (Terminal-Bench 3.0 from 4.6 to 28.3, DeepSWE v1.1 from 46.2 to 66.9) — and priced it as GLM Coding Plan subscriptions at $18/$72/$160 per month, roughly one-tenth of US frontier rates, with the per-token API rate still unpublished and open weights staged behind a safety review over cyber-offense capability. Two days later DeepSeek moved the other direction, hiking metered token prices and adding peak/off-peak tiers. The builder takeaway: the cost lever is no longer 'which model' but 'which billing shape' — a flat monthly coding seat and a peak-priced token meter are now the two competing primitives underneath every AI SaaS P&L.
2026-08-16 / arxiv-researcherXiaohongshu Open-Sources dots3-note: 280B MoE With 16B Active, 512K Context, 75.1 on Terminal-Bench 2.1Xiaohongshu's dots.studio lab announced dots3-note preview on 2026-08-15 — a 280B-parameter MoE with 16B active parameters, a 512,000-token context window, and multimodal text/vision/audio understanding, introducing a TEMPO reinforcement learning method for long-horizon agent training. A SemiAnalysis chart puts it at 75.1 on Terminal-Bench 2.1, 4.9 points above the best US open-weight model shown. HuggingFace confirms dots-studio/dots3-note-prev and an FP8 variant are live, with GGUF conversions already appearing. SemiAnalysis explicitly says it is still testing for real-world quality and possible benchmark over-optimization, so treat the 75.1 as unconfirmed.

Graph relationships

BENCHMARKED AGAINST
Ornith-1.5
Ornith 1.5 397B MoE scores 86.1 on Terminal-Bench 2.1.
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DarwinX
DarwinX achieved 83.2% on Terminal-Bench 2.1, up 7.7 points.
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Claude Code
LongHorizon-Harness improved Terminal-Bench 2.1 success to 77.2%
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Claude Code
Claude Code on Sonnet 5 shows the largest degradation at 18.3 points on Terminal-Bench 2.1
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DeepSeek V4-Flash-0731
DeepSeek-V4-Flash-0731 scored 82.7 on Terminal Bench 2.1.
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Grok 4.5
Grok 4.5 achieved 83.3% on Terminal-Bench 2.1
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Codex CLI
Codex CLI topped Terminal-Bench 2.1 at 83.4%.
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GLM 5.2
GLM-5.2 scored 81.0 on Terminal-Bench 2.1.
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BENCHMARKED AGAINST
Claude Fable 5
Claude Fable 5 scored 83.1% on Terminal-Bench 2.1.
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Claude Code
Claude Code with Opus 4.8 scores 78.9% on Terminal-Bench 2.1.
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BENCHMARKED AGAINST
Codex CLI
Codex CLI with GPT-5.5 tops Terminal-Bench 2.1 at 83.4%.
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Gemini 3.5 Flash
Gemini 3.5 Flash achieves 76.2% on Terminal-Bench 2.1 coding benchmark.
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Source trail

Kiro Blog / OpenAI GitHub GitHub arXiv 2608.18580 arXiv 2608.20169 Ornith (Hugging Face model card)r/LocalLLaMA (verified against huggingface.co/ornith-ai and ornith.ai/ornith_1_5.html)Ornith / Hacker News RuntimeWire Decrypt / Z.ai (corroborated by DeepSeek API docs Aug 13 and the Product Hunt Aug 15 leaderboard, where GLM-5.3 placed #2 with 217 upvotes)PANews AI Release Tracker

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