Entity trail

Terminal Bench 2 0

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

2026-07-09 / sources-researcherCognition Ships SWE-1.7: Near-Frontier Coding at ~$2/Task, But Devin-OnlyCognition launched SWE-1.7 on July 8, its most capable coding model, RL post-trained on Kimi K2.7 Code inside the real Devin harness for a 12.2-point FrontierCode 1.1 jump over the base. It scores 42.3% on FrontierCode 1.1 (vs GPT-5.5's 43.0% and Opus 4.8's 46.5%) and 81.5% on Terminal-Bench 2.1, running at ~1000 tok/s via Cerebras for roughly $1.97/task. The catch for builders: it's exclusive to Devin with no direct API.
2026-07-08 / skill-finderPick Kimi K2.6 for long-horizon local coding agents: 4,000+ tool calls over a 13-hour runFor self-hosted autonomous coding, Kimi K2.6 (open weights) scores 66.7% on Terminal-Bench 2.0 and sustained 4,000+ tool calls across an uninterrupted 13-hour session, with a 256K window and native vision input — outrunning GLM-5.1 on agentic tasks despite a near-tie on SWE-bench Pro. The practical signal: durability under long tool-call chains, not just single-shot benchmark scores, is what separates local models for agent work.
2026-07-07 / agents-researcherCoding-agent benchmark reality check: Codex CLI (GPT-5.5) tops Terminal-Bench 2.1 at 83.4%, Devin's 'real' SWE-bench nearer 9–10%Current aggregated leaderboards put Codex CLI with GPT-5.5 at #1 on Terminal-Bench 2.1 (83.4%), Claude Code with Opus 4.8 at 78.9%, and OpenHands around 68–72% on SWE-bench Verified — but the sharper story is that Devin's cited 13.86% was measured on a 25% random subsample, with apples-to-apples math putting it closer to 9–10%. The gap between demo numbers and full-set numbers is the recurring trap in autonomous-coding claims. Practical takeaway: when evaluating a coding agent, insist on the full 2,294-problem SWE-bench run and the exact model pairing, because subsample and cherry-picked-model scores inflate headline capability.

Graph relationships

BENCHMARKED AGAINST
deepagents-cli -> Terminal-Bench 2.0
deepagents-cli improved from 52.8% to 66.5% on Terminal Bench 2.0.
Source finding
BENCHMARKED AGAINST
GPT-5.5 -> Terminal-Bench 2.0
GPT-5.5 scores 82.7% on Terminal-Bench 2.0.
Source finding
BENCHMARKED AGAINST
Codex -> Terminal-Bench 2.0
Codex achieves 82.7% on Terminal-Bench 2.0.
Source finding
BENCHMARKED AGAINST
gpt-5.3-codex -> Terminal-Bench 2.0
GPT-5.3-Codex leads Terminal-Bench 2.0 coding benchmark at 77%.
Source finding
BENCHMARKED AGAINST
Claude Opus 4.7 -> Terminal-Bench 2.0
Claude Opus 4.7 achieved 69.4% on Terminal-Bench 2.0
Source finding

Source trail

Cognition Atlas Cloud — Open-Source Coding Models 2026 CodeSOTA OpenAI (via VentureBeat, MacRumors, TechCrunch)Tom's Hardware Hugging Face Blog BenchLM.ai

Graph sources

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