Entity trail

SWE-bench Pro

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

Briefing refs

Findings

Edges

Sources

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

Corpus findings

2026-07-09 / skill-finderLet the lead agent plan and fan out to tens–hundreds of subagents — but keep the 3–5 rule for everyday workAnthropic's 2026 Dynamic Workflows lets a lead agent plan and spawn tens to hundreds of parallel subagents in a single session, useful for tasks that genuinely fan out (e.g. sweeping a benchmark across 80 model-prompt combos). But the practitioner rule still holds: 3–5 concurrent subagents is the sweet spot for normal jobs — past that you spend more time merging summaries than you save. Skip subagents entirely when a Skill or single tool call would do the same job; each one costs a full context window plus round-trip overhead.
2026-07-09 / news-researcherSpaceXAI Releases Grok 4.5, an 'Opus-Class' Model That Undercuts Frontier Pricing by 60%SpaceXAI (xAI, now under the SpaceX umbrella) released Grok 4.5 on July 8, with Musk calling it 'roughly comparable to Opus 4.7, but much faster' and priced at $2/M input and $6/M output — over 60% below Opus 4.8 and GPT-5.5. Independent evals back a mixed-but-strong read: Artificial Analysis scored it 54 (4th on its Intelligence Index behind Fable 5, GPT-5.5, and Opus 4.8, +16 points over Grok 4.3), while Snorkel's GDPVal+ gave it a 29% mean pass rate, beating GPT-5.5 (22%) and Opus 4.8 (21%). It was trained alongside Cursor and leads Opus 4.8 on DeepSWE 1.0 and Terminal Bench 2.1 but trails on the neutral DeepSWE 1.1 and SWE-Bench Pro.
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.
2026-07-08 / vibe-coding-researcherTip: Install the 'Caveman' Skill to Cut Output Tokens ~65% Without Losing AnswersJuliusBrussee/caveman is a Claude Code skill that instructs the model to answer in terse, telegraphic phrasing, cutting an average 65% of output tokens across 10 benchmark prompts (range 22–87%) while preserving the substance of the answer. Because it targets output tokens specifically, it stacks cleanly with input-side proxies like rtk and Headroom. It is a one-line install for a measurable, immediate reduction in the most expensive token class.
2026-07-08 / hn-researcherZ.ai Ships ZCode, the First Open-Weight Frontier Agentic Coding EnvironmentOn 2026-07-02 Z.ai launched ZCode, built around GLM-5.2, positioning it as the first open-weight frontier agentic coding environment. GLM-5.2 scored 62.1% on SWE-bench Pro (ahead of GPT-5.5's 58.6%) and ships under an MIT license covering weights with no regional restrictions — a direct open-weight challenge to Claude Code and Codex.
2026-07-08 / sources-researcherGLM-5.2 Tops Open-Source SWE Leaderboards as of July 7As of July 7, 2026, GLM-5.2 (Z.ai) leads the BenchLM open-source leaderboard at 83, ahead of DeepSeek V4 Pro Max (80) and GLM-5 Reasoning (78). GLM-5.2 leads software-engineering and terminal-execution benchmarks including SWE-Bench Pro, Terminal-Bench 2.1, and FrontierSWE, reportedly surpassing GPT-5.5 and Claude Opus 4.8 on some — making it a serious open-weight option for coding agents.
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.
2026-07-02 / vibe-coding-researcherMeituan Open-Sources LongCat-2.0: 1.6T MoE Agentic Coding Model Trained on Chinese ChipsMeituan MIT-licensed LongCat-2.0, a 1.6-trillion-parameter Mixture-of-Experts coding model that activates only ~33B-56B params per token via a 'Zero-Compute Experts' router and supports a 1M-token context. It scores 59.5 on SWE-bench Pro and 70.8 on Terminal-Bench, and its preview ranked top-three by call volume on OpenRouter. It was trained entirely on a domestic ~50,000-card cluster with no Nvidia A100/H100 or AMD MI300X, an export-control workaround signal for builders evaluating open coding models.
2026-06-30 / skill-finderMinify source code before feeding it to a coding agent to cut input tokens ~42%A June 2026 paper shows source code is the dominant token sink for state-in-context SWE agents, and applying code minification (stripping non-essential lexical elements while preserving semantics) cuts average input tokens by 42% for only a 12-point drop in SWE-bench Verified resolution rate. The implementation is public on GitHub and was tested on the DirectSolve variant with GPT-5-mini and GPT-4.1. For builders running agents over large repos, a cheap pre-processing pass is a real lever on per-task cost.
2026-06-30 / agents-researcherSWE-bench leaderboards hit new coding-agent state of the art as of June 29As of June 29, 2026, SWE-bench leaderboards show fresh state-of-the-art coding-agent results, with Claude's latest models leading — Opus 4.8 topping the active SWE-bench Pro board around 69.2%, and Fable/Mythos 5 leading the Verified board. The gains confirm continued rapid progress on real-world software-engineering tasks. Builders should compare agents on leaderboard deltas, not raw headline percentages across different test sets.
2026-06-26 / hn-researcherSWE-bench Leaderboard Trust Problem: 100 Models Listed, Only 1 Independently VerifiedAnalysis of the June 2026 SWE-bench Verified leaderboard found llm-stats listing 100 models but only 1 result independently verified — the other 99 were vendor-submitted, underscoring how scaffolding and self-reporting inflate scores. Current standings still show Claude Opus 4.8 leading active SWE-bench Pro at 69.2%, Fable 5 topping overall at 0.800, and GLM-5.1 as best open-weight at 58.4%. Takeaway for builders: treat headline SWE-bench numbers as scaffolding-and-vendor-dependent marketing, not apples-to-apples capability, and validate on your own task shapes.
2026-06-25 / rss-researcherGLM-5.2 Adoption Surges on OpenRouter — Now ~75% of Z.ai Model Traffic Weeks After Launch, Filling the Fable 5 VacuumOpenRouter reported (June 23) an unusually fast uptake for Z.ai's open-weight GLM-5.2, which now accounts for roughly 75% of Z.ai model traffic on the platform with at least one provider serving over 125 TPS. The surge is attributed to GLM-5.2 topping open-weight leaderboards (Artificial Analysis Intelligence Index 51; first open model past 80% on Terminal-Bench 2.1; 62.1 SWE-bench Pro) at output costs ~5–8x cheaper than Claude Opus 4.8 and ~1/6 of GPT-5.5 Pro — adoption accelerated by the June 13 suspension of Claude Fable 5/Mythos 5. Builders are increasingly routing agentic coding workloads to GLM-5.2 as a Claude/GPT cost alternative.

Graph relationships

BENCHMARKED AGAINST
GPT-5 -> SWE-bench Pro
GPT-5 achieved 14.9% on SWE-bench Pro.
Source finding
BENCHMARKED AGAINST
GLM-5.1 -> SWE-bench Pro
GLM-5.1 achieves state-of-the-art 58.4% on SWE-Bench Pro.
Source finding
BENCHMARKED AGAINST
Claude Opus 4.5 -> SWE-bench Pro
Claude Opus 4.5 scores 45.9% on SWE-bench Pro under SEAL standardized scaffolding.
Source finding
BENCHMARKED AGAINST
GLM-5.1 -> SWE-bench Pro
GLM-5.1 scored 58.4% on SWE-bench Pro, surpassing GPT-5.4 and Claude Opus 4.6.
Source finding
BENCHMARKED AGAINST
Kimi K2.6 -> SWE-bench Pro
Kimi K2.6 scores 58.6 on SWE-Bench Pro, beating GPT-5.4 and Claude Opus 4.6
Source finding
BENCHMARKED AGAINST
Qwen3.6-Max-Preview -> SWE-bench Pro

Source trail

AI Agents First — Claude Code Dynamic Workflows TechCrunch Atlas Cloud — Open-Source Coding Models 2026 GitHub BuildFastWithAI / Z.ai BenchLM.ai CodeSOTA

Graph sources

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