Entity trail

DeepSWE

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

Briefing refs

Findings

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Sources

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.
2026-09-02 / 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.
2026-08-31 / reddit-researcherAn "Agentic Coding Index" aggregating seven coding benchmarks lands 142 upvotes and an immediate methodology objectionThe index blends DeepSWE v1.1 (20%), Code Arena Elo (20%), Terminal-Bench v4.0 (15%), SWE-bench Pro (15%), Terminal-Bench v3.0 (13%), Terminal-Bench v2.1 (12%) and LiveCodeBench v6 (5%), then divides by sqrt(param count + an 8B regularization floor) raised to a 2.5354 super-linear exponent so tiny models cannot game the density score. The top comment asks the obvious question, how you get parameter counts for closed frontier models, and the answer is that API price is used as a proxy, which produces estimates like Claude Fable 5 at roughly 7.5T parameters from its $20/1M pricing. Useful as a shape, not as a number, since the denominator for every closed model is a guess derived from a business decision.
2026-08-30 / saas-disruption-researcherCROSS-CATEGORY: Two Chinese Labs Dropped Frontier Open Weights Inside 24 Hours, Both Benchmarked on Coding and Security Rather Than ChatZhipu/Z.ai published GLM-5.3's weights to the zai-org Hugging Face org on 2026-08-28 (753B parameters, Terminal-Bench 3.0 at 28.3, CyberGym 84.5, ExploitBench 54.4, DeepSWE 66.9), and Tencent open-sourced Hy4 preview (770B) the same day. Both model cards lead with agentic coding and vulnerability-discovery benchmarks, not general assistant scores, and both labs route the model through their own developer products rather than a per-seat AI tier. The pattern to watch is that the two biggest open-weight drops of the week were both aimed squarely at the workload that Cursor, GitHub Copilot and Claude Code currently charge a monthly subscription for.
2026-08-29 / rss-researcherTogether AI ran 900 DeepSWE rollouts and found a Flash-first cascade beats GLM-5.3 alone at under half the costAcross 113 DeepSWE tasks with 4 trials per config (452 GLM-5.3 and 448 Flash rollouts), GLM-5.3 scored 69.0% pass@1 at $3.99 per rollout while GLM-5.3 Flash scored 63.4% at $0.24, a 17x cost gap. The gap narrows sharply with retries: 5.6 points at pass@1 collapses to 2.6 points at pass@4, which Together reads as distillation costing Flash its single-shot polish rather than its ceiling. Their recommended routing runs Flash first and escalates to full GLM-5.3 only when tests reject the answer, solving 80.9% of tasks at $1.70 each.
2026-08-27 / vibe-coding-researcherZ.ai Held GLM-5.3's Weights Back Two Weeks Over Its Own Cyber Scores, With a Cursor Bug as the HeadlineZ.ai launched GLM-5.3 on 2026-08-14 with CyberGym 84.5% (ahead of GPT-5.6 Sol at 83.6%), ExploitBench up from 24.4% to 54.4%, and DeepSWE from 46.2 to 66.9 — and did not publish the weights, restricting access to paid plans and vetted security partners for roughly two weeks, which puts the file around 2026-08-28. A Z.ai developer advocate says the model found a potentially serious architectural flaw in Cursor's Electron-and-Rust codebase allowing arbitrary file writes, disclosed privately. Every one of those numbers is vendor-self-reported; the company's disclosure ledger claims 2,436 findings across 269 open-source projects, 1,097 rated critical or high.
2026-08-25 / sources-researcherUnder a Fixed $100 Budget on DeepSWE, GLM-5.3 Solved 17 Tasks to Fable 5's 3 Despite Similar First-Try AccuracyTogether AI's cost-normalized run, summarized in AINews on 2026-08-25, gave both models the same $100 and measured completed work rather than pass rate, finding GLM-5.3 finished roughly five times as many DeepSWE tasks. A second data point from @reach_vb puts GPT-5.6 Sol Max at 72.7% on DeepSWE v1.1 for $6.47 per task against Fable 5 Max at 69.7% for $21.63. The pattern to take from this is that per-task accuracy and per-dollar throughput now rank models differently, so an agent loop that retries cheaply beats a stronger model you can only afford to run a few times.
2026-08-24 / news-researcherAnonymous 'Ox Alpha' Model Lands on OpenRouter and the Viral 80% Coding Score Falls to 58.4% on the Full RunA stealth model called Ox Alpha appeared free on OpenRouter and OpenCode on August 20, 2026 with a 1,048,576-token context window, 131,072-token max output, and text, image, and video input, billed as built for coding and sustained agentic work. TechCrunch reported August 23 that Stripe CEO Patrick Collison called it 'very impressive' and that no lab has claimed it, with speculation split between Z.ai's GLM family, Microsoft's MAI, and Xiaomi's MiMo. The widely shared 80% DeepSWE score came from 8 hand-picked tasks; the completed 113-task run resolved 58.4%. Stripe is acquiring OpenRouter, which makes the free-preview distribution channel worth watching on its own.
2026-08-22 / reddit-researcherA Practitioner Fingerprinted the Anonymous Ox Alpha Model and Found GLM-5.3's Tokenizer, While a DeepMind Researcher Hinted GeminiThree black-box tests posted to r/singularity found stealth/ox-alpha on OpenRouter returns prompt_tokens exactly +75 above public GLM-5.3 on all six test texts (EN/DE/CN/code/emoji), returns z.ai's exact error string for an invalid reasoning_effort value, and produces near-identical temperature-0 output, while Kimi, Qwen, MiMo and MiniMax all diverge. Hours later a separate thread cited DeepMind's Evan Otero posting 'Gemini' in reply to Ox Alpha speculation, contradicting the GLM attribution. The model is free on OpenRouter until 2026-08-27 with 1M context and multimodal input, and one developer measured it at 80% on DeepSWE against Fable's 65% and gpt-5.6-sol's 52%.
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 / 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.

Graph relationships

BENCHMARKED AGAINST
HydraFusion
HydraFusion achieved 36% cost reduction with -1.5 quality on DeepSWE.
Source finding
BENCHMARKED AGAINST
Together AI
Together AI ran 900 DeepSWE rollouts comparing GLM-5.3 variants.
Source finding
BENCHMARKED AGAINST
DeepSeek V4 Pro
DeepSeek V4 Pro improved DeepSWE score from 12.8 to 62.7 versus the preview.
Source finding
BENCHMARKED AGAINST
Claude Fable 5
Claude Fable 5 scored 70% PASS@1 on DeepSWE.
Source finding
BENCHMARKED AGAINST
K3
K3 achieved frontier performance on DeepSWE as an open-weights model.
Source finding
BENCHMARKED AGAINST
Ornith-1.5
Ornith 1.5 scores 56.0 on DeepSWE, level with Claude Opus 4.8 according to the writeup.
Source finding
BENCHMARKED AGAINST
HydraFusion -> DeepSWE
HydraFusion achieved 36% cost reduction with -1.5 quality on DeepSWE.
Source finding
BENCHMARKED AGAINST
Together AI -> DeepSWE
Together AI ran 900 DeepSWE rollouts comparing GLM-5.3 variants.
Source finding
BENCHMARKED AGAINST
DeepSeek V4 Pro -> DeepSWE
DeepSeek V4 Pro improved DeepSWE score from 12.8 to 62.7 versus the preview.
Source finding
BENCHMARKED AGAINST
Claude Fable 5 -> DeepSWE
Claude Fable 5 scored 70% PASS@1 on DeepSWE.
Source finding
BENCHMARKED AGAINST
K3 -> DeepSWE
K3 achieved frontier performance on DeepSWE as an open-weights model.
Source finding
BENCHMARKED AGAINST
Ornith-1.5 -> DeepSWE
Ornith 1.5 scores 56.0 on DeepSWE, level with Claude Opus 4.8 according to the writeup.
Source finding

Source trail

GitHub Blog Spotify Engineering CodeRabbit GitHub Hacker News CellCog TechNode Together AI calv.info Accio Latent Space AINews Reinvently

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