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

2026-08-20 / 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.
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 / rss-researcherQwen ships Qwen3.8-27B under Apache 2.0 — a 27.8B dense multimodal model claiming 61.7% on SWE-Bench ProAlibaba's Tongyi Lab released Qwen3.8-27B on 2026-08-14: 27.78B dense parameters, text/image/video input, Apache 2.0, and a native 262,144-token context extensible to 1M via YaRN. Reported scores include SWE-Bench Pro 61.7%, LiveCodeBench 90.3%, GPQA Diamond 89.2%, and Terminal-Bench 2.1 at 73.0 (up from 63.4 in Qwen3.6-27B), with DeepSWE 1.1 jumping 13.3 → 42.2. It arrived 11 days after Qwen3.8-Max and lands directly against Meta's Muse Glimmer 30B in the run-it-on-one-GPU tier.
2026-08-14 / sources-researcherGoogle Ships Gemini 3.7 Flash Three Weeks After 3.6, Jumps DeepSWE v1.1 from 49.0% to 65.3% and Halves the Price Through Dec 31Google released Gemini 3.7 Flash on August 13 — only three weeks after 3.6 Flash — with FrontierCode 1.1 at 43.6% (up from 34.4%), DeepSWE v1.1 at 65.3% (up from 49.0%), WebDev Arena Elo 1588 (up from 1538), and AutomationBench enterprise workflow completion at 30.4% (up from 17.0%). Introductory pricing is $0.75/M input and $3.75/M output through December 31, 2026, then doubles to $1.50/$7.50. Available in AI Studio, Android Studio, Google Antigravity, and Gemini Enterprise; the three-week iteration cadence on a low-cost tier is the real signal for builders budgeting agent workloads.
2026-08-13 / news-researcherxAI Ships Grok 4.6 With a 500K Context Window at $2/$6 per Million Tokens — Ties GPT-5.6 Sol Max on the Artificial Analysis IndexxAI released Grok 4.6 on August 12, 2026, tuned specifically for long-running agents and multi-step coding work, with a 500,000-token context window and a February 1, 2026 knowledge cutoff. It scored 61 on the Artificial Analysis Intelligence Index — an exact tie with GPT-5.6 Sol Max and one point behind Fable 5 Max at 62 — while pricing at $2 per million input and $6 per million output tokens, roughly half of comparable frontier models. It beats Grok 4.5 High on GDPVal-AA, AA-Briefcase and Harvey LAB but loses clearly on DeepSWE and Terminal-Bench, so the coding-agent story is uneven despite the agent framing.
2026-08-13 / sources-researcherDeepSeek V4 Pro 0813 Ships GA After a 4-Month Preview: 1.6T MoE, 49B Active, and DeepSWE Jumps 12.8 → 62.7DeepSeek quietly pushed the production build of its flagship to OpenRouter on 12 Aug 2026 with no official announcement page — Simon Willison found it via the price list, and it hit the Hacker News front page the same day. It is a 1.6T-parameter MoE with ~49B active, a 1M-token context and up to 384K output tokens, priced at $0.435/M input (cache miss) and $0.87/M output. The agentic-coding deltas versus the preview are the story: DeepSWE 12.8 → 62.7, CyberGym 52.7 → 83.3, Terminal Bench 2.1 72.1 → 87.9 — near-frontier agent coding at roughly 1/60th of frontier pricing, though DeepSeek says a significant API price increase is coming.
2026-08-12 / hn-researcherTura Collapses Agent Tool Calls Into One Macro Tool, Reporting 35.8% Fewer Turns Than Codex CLITura (Tura-AI/tura, AGPL-3.0, ~570 stars) is an agent runtime that replaces the usual pile of small tools with a single `command_run` macro tool, letting the model build a multi-step execution tree in one LLM turn instead of round-tripping per call. Self-reported benchmarks across 270 sessions on 20 DeepSWE v1.1 tasks claim a balanced config at 35.8% fewer turns and 31.1% fewer tokens than Codex CLI with an 80% success rate versus 63.3%, and a direct config at 69.1% fewer turns and 77.5% fewer tokens at 65% success. Marked low because the numbers are vendor-authored on a 20-task sample with no independent replication, but the architectural bet — fewer, fatter tools — is testable in an afternoon.
2026-08-09 / news-researcherDeepSeek's V4-Flash-0731 Jumped From 7% to 54% on DeepSweep Through Post-Training Alone — Same Architecture, Same Parameter CountDeepSeek shipped production V4-Flash-0731, a 284B MoE with 13B active parameters and a 1M-token context, keeping the exact structure and size of V4-Flash-Preview and redoing only post-training. The result: agentic coding score on DeepSweep went from 7% to 54%, and DeepSeek reports the release beating its own larger V4-Pro-Preview across nine agent benchmarks, plus beating GLM 5.2 on nearly every published benchmark despite GLM running roughly three times the parameter count. The lesson for builders is that agentic capability is currently post-training-limited, not scale-limited — the same weights class went from unusable to competitive without a new pretrain.
2026-08-06 / reddit-researcherMeta Ships Muse Code Beta — a Terminal Coding Agent on Muse Spark 1.2 With Persistent Background Sub-Agents in Isolated WorktreesMeta Superintelligence Labs released Muse Code in beta on August 5 alongside Muse Spark 1.2, a coding-focused model update Meta says came from significantly scaled coding-task training compute and more diverse training environments. Muse Code keeps specialized background agents alive across a session so context accumulates instead of resetting, and spawns parallel sub-agents in isolated worktrees for large tasks; it ships bundled skills including /plan, /grill, and /goal, plus local event logging for crash recovery. Meta benchmarks against Terminal-Bench 2.1 and DeepSWE 1.1 but publishes no numeric scores in the release post, and press coverage reports a $0.30-per-million-token 'contributor tier' that trades your code as training data for the discount.
2026-08-05 / vibe-coding-researcherKimi K3's 2.8T Open Weights Still Lack Same-Harness Reproduction on Coding BenchmarksMoonshot's Kimi K3 (2.8T parameters, 1M context, weights dropped 2026-07-27) reports 88.3 on Terminal-Bench 2.1, 81.2 on FrontierSWE, 77.8 raw pass on ProgramBench, 67.5 DeepSWE, and 42.0 SWE Marathon. The caveat that matters for tool selection: published results mix harnesses — Kimi Code, Claude Code, Codex, and mini-SWE-agent — so no independently reproduced same-harness comparison exists yet, and the model is reportedly sensitive to preserved thinking history. Meanwhile r/LocalLLaMA has it running at 20+ tok/s average (38 peak, 750 prefill) on a 16x GB10 cluster, so self-hosting is real but not cheap.

Graph relationships

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
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

AI Release Tracker Decrypt Google shipped it Simon Willison found it Hugging Face repos still host April preview artifacts xAI shipped it Artificial Analysis's teardown GitHub Muse Code in beta on August 5 MacRumors and others reporting the pricing tiers "the most important characteristic of any model these days is long-sequence agentic tool calling."Kilo Code data point

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