Entity trail

GPT-4.1

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 / 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.
2026-07-09 / 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.
2026-07-07 / 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.
2026-07-01 / skill-finderMeta-prompt: have a reasoning model write the production prompt for your cheap modelA 2026 workflow is to ask a high-effort reasoning model (e.g. GPT-5.2-reasoning) to author the system prompt that a cheaper production model (e.g. GPT-4.1-mini) will actually run. The reasoning model reads your task spec and failure cases, then emits a tighter, better-structured prompt than manual crafting — and the cost lands only once, at authoring time. Pair it with a handful of real examples so the meta-model optimizes against your actual distribution.
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.
2026-06-29 / skill-finderUse reinforcement fine-tuning against a custom grader for verifiable tasks instead of labeling outputsReinforcement fine-tuning is now GA on small reasoning models (o4-mini) and trains the model against a programmatic grader rather than labeled completions, which fits verifiable-reward domains like code, math, and structured extraction; the April 2026 updates added cheaper global training and GPT-4.1/-mini/-nano model graders. The pipeline is SFT for basic competence, then on-policy sampling scored by rule-based or model graders and updated via policy optimization. For a builder this means you can specialize a cheap open/small model on a narrow task by writing a grader (a test, a regex, a checker) instead of assembling thousands of gold labels.
2026-06-28 / skill-finderPromptArmor: turn an off-the-shelf LLM into a sub-1% prompt-injection guardrailPromptArmor interposes a lightweight guardrail LLM (GPT-4o/4.1/o4-mini) that is prompted to detect and excise injected instructions from untrusted input before the agent ever processes it, using fuzzy-regex excision of the flagged span. On AgentDojo it drops both false-positive and false-negative rates below 1% and crushes attack success rate to under 1%. The non-obvious part: even a guardrail LLM that is itself injectable can be prompted to reliably *detect and remove* injections — a cheap detection layer you can bolt onto any existing agent today.
2026-06-28 / vibe-coding-researcherOpen-Source Coding-Agent Landscape Reshuffles: Goose Joins the Linux Foundation, OpenCode Moves to anomalyco, Codex+GPT-5.5 Tops Terminal-Bench at 83.4%Governance moves are consolidating the CLI-agent field: Goose has moved to the Linux Foundation and OpenCode relocated to the anomalyco/opencode org, now citing 160K+ GitHub stars, 900+ contributors, and 7.5M monthly developers. On capability, Codex CLI paired with GPT-5.5 still leads Terminal-Bench at 83.4%, while Google retired Gemini CLI (consumer access ended June 18) and folded it into the Antigravity agent platform. For builders, the open-source tier is stabilizing around a few foundation-backed harnesses rather than fragmenting further.
2026-06-23 / hn-researcherGLM-5.2 'How to Run Locally' Hits 548 Points on HN as It Tops GPT-5.5 on SWE-bench Pro at 1/6 the CostZ.ai's open-weight GLM-5.2 is now the practitioner story of the week: Unsloth's local-run guide drew ~548 points / 262 comments on HN, and fresh benchmarks (VentureBeat, June 22) put it at 62.1 on SWE-bench Pro vs GPT-5.5's 58.6, priced ~$4.40 vs $30 per million output tokens. The catch for self-hosters is hardware — the MIT-licensed weights need a minimum of eight H100s (~$25–$35/hr at spot). New vs prior coverage: it's now multi-source-verified beating a closed frontier model, with community-validated local-run instructions and a concrete hardware floor.
2026-06-23 / sources-researcherGetty Images Signs Display Deal With OpenAI to Put Licensed Photos Inside ChatGPT — Stock Jumps 145%Getty Images announced a multi-year display agreement with OpenAI on June 21; licensed Getty photos will surface inside ChatGPT's search and discovery results — explicitly a display deal, not a training deal. Getty stock soared as much as 145% on June 22, a sharp reversal for a company that banned AI-generated art in 2022. Signals that licensed-content-in-the-answer is becoming a real revenue model and that publishers are pivoting from litigation to distribution.
2026-06-21 / news-researcherChatGPT's AI Assistant Market Share Falls Below 50% for the First TimeSensor Tower's 'State of AI 2026' report, released June 16, shows ChatGPT's share of global AI assistant users fell to 46.4% by end of May — down from 52.8% in December 2025 and 65.3% in December 2024. Gemini rose to 27.7% and Claude to 10.3%, though ChatGPT still leads on absolute scale with 1.1B+ monthly users vs Gemini's 662M and Claude's 245M.
2026-06-20 / thought-leaders-researcherSam Altman's New 40-Minute Interview: GPT-5 'Probably This Summer' but May Slip on Safety Testing, Teases an o3 'High-Performance' TierIn a 40-minute interview released June 19, Sam Altman said GPT-5 is 'probably coming sometime this summer' while explicitly hedging that it could slip on security testing, naming, and feature iteration, and teased an o3 'high-performance' model plus Deep Research upgrades. Separately he admitted he's '0% excited' to run a public company even as OpenAI signals an IPO within a year. Builder takeaway: plan around a fuzzy summer GPT-5 window, not a fixed date — the CEO is the one hedging the timeline.

Graph relationships

USES
Gradient Labs
Gradient Labs deploys GPT-4.1 models
Source finding
DEPRECATES
Copilot
GitHub Copilot replaced GPT-4.1 with GPT-5.3-Codex.
Source finding
BENCHMARKED AGAINST
SWE-Bench
GPT-4.1 was tested on SWE-bench
Source finding
USES
Gradient Labs -> GPT-4.1
Gradient Labs deploys GPT-4.1 models
Source finding
DEPRECATES
Copilot -> GPT-4.1
GitHub Copilot replaced GPT-4.1 with GPT-5.3-Codex.
Source finding

Source trail

TechCrunch Cognition CodeSOTA Digital Applied — Prompt Engineering: Advanced Techniques for 2026 Reducing Token Usage of State-in-Context Agents using Minification (arXiv 2606.01326)Fireworks AI — Reinforcement Fine-Tuning: Train Expert Open Models to Surpass Closed Frontier Models PromptArmor: Simple yet Effective Prompt Injection Defenses (arXiv 2507.15219)

Graph sources

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