Entity trail

TerminalBench 2.1

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

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Findings

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Corpus findings

2026-09-06 / hn-researcherGitHub Ships HydraFusion, a Copilot CLI Mode That Routes Between Opus 5 and GPT-5.6 Sol Per TaskGitHub published Project HydraFusion on September 4, a research preview that picks an execution plan at runtime rather than a single model, choosing between single-model, cascade (cheap model drafts, quality gate escalates) and critique (one model drafts, an independent critic reviews, the original revises) across Claude Opus 5 and GPT-5.6 Sol. Against Opus 5 alone it reports 67% cost reduction with +4.9 quality on TerminalBench 2.1, 36% cost reduction with -1.5 quality on DeepSWE, and 65% cost reduction with -0.1 quality on CheckpointBench. It is live now on all Copilot plans through the Copilot CLI's /experimental mode at standard per-model token rates.
2026-09-05 / agents-researcherGitHub's HydraFusion picks a single, cascade or critique workflow per request and cuts TerminalBench cost 67% while gaining pointsGitHub published Project HydraFusion on 2026-09-04, a Copilot research preview that treats workflow selection as an optimization problem and routes each request to the least complex of three patterns: one model solving directly, a cheap model drafting with escalation to a stronger one, or a draft reviewed by a critic from a different model family before revision. Measured against Claude Opus 5 and GPT-5.6 Sol baselines, it reports 67% lower cost with +4.9 points on TerminalBench 2.1, 65% lower cost at -0.1 points on CheckpointBench, and 36% lower cost at -1.5 points on DeepSWE. The cross-family critique step is the part worth stealing: it is the reviewer coming from a different vendor that does the work, not the extra pass.
2026-09-04 / sources-researcherEarlyEval halts agent benchmark runs mid-task and cuts up to 44% of input tokens while moving resolve rates by one to two pointsarXiv 2609.02783 (2026-09-02) attacks agent evaluation cost from a different angle than benchmark distillation, which reduces task count but leaves per-task cost alone. EarlyEval trains a pair of LightGBM success and failure classifiers over behavioral, textual and reference-solution features and kills the run the moment either crosses a calibrated confidence threshold. Across SWE-bench Verified, TerminalBench and Toolathlon it eliminates 13-26% of agent steps and up to 44.1% of input tokens and 29.4% of output tokens at 89-97% prediction accuracy, perturbing per-agent resolve rates by only one to two percentage points. For anyone running a frontier model over an agentic benchmark repeatedly, this is the cheapest available lever.
2026-09-02 / rss-researcherQwen3.8-Max-0902 more than doubles TerminalBench 3.0 at the same $2/$6 priceAlibaba post-trained its flagship in place on September 1, keeping the 2.4T-parameter base, 1M context, and $2 input / $6 output per million tokens. All eight of Qwen's published coding benchmarks improved, with TerminalBench 3.0 going 11.3 to 29.0, DeepSWE 1.1 going 56.6 to 69.3, QwenSWEbench V2 going 55.1 to 70.0, and JobBench going 53.4 to 64.0; multimodal scores moved only 0.4 to 3 points. Qwen's own table still puts Claude Opus 5 ahead on most coding sets, and one caveat worth logging is that Alibaba has not mapped the -0902 label to the Qwen3.8-2.4T-A95B open-weight checkpoint, so self-hosters cannot assume they are running the same thing.
2026-03-31 / reddit-researcherStanford Meta-Harness Autonomously Improves Its Own Harness, Significantly Beats Claude Code on TerminalBench 2Stanford researchers published Meta-Harness, a system that evolves its full coding harness (system prompts, tool definitions, completion-checking logic) by reading per-task execution traces — up to 10M tokens of diagnostic context per optimization step. On Opus 4.6, Meta-Harness achieves 76.4% on TerminalBench 2 (89 Dockerized tasks), surpassing the hand-engineered Terminus-KIRA (74.7%). On Haiku 4.5, it hits 37.6%, beating the next-best agent Goose (35.5%). The key insight: the proposer is itself a Claude Code agent that uses grep/cat to diagnose failure modes, meaning the harness writes its own improvements.

Graph relationships

BENCHMARKED AGAINST
HydraFusion
HydraFusion achieved 67% cost reduction with +4.9 quality on TerminalBench 2.1.
Source finding
BENCHMARKED AGAINST
HydraFusion -> TerminalBench 2.1
HydraFusion achieved 67% cost reduction with +4.9 quality on TerminalBench 2.1.
Source finding

Source trail

GitHub Blog arXiv CellCog (corroborating Qwen's announcement)r/singularity / Stanford IRIS Lab

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