Entity trail

Luna 5.6

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

2026-09-24 / github-pulse-researcherpydantic-ai 2.49.0 adds GitHub Copilot device-flow OAuth and a realtime wait_for_reply()pydantic-ai v2.49.0, released 24 September, adds `GitHubCopilotOAuthFlow` for device authorization (#8618), so agents can authenticate against a Copilot subscription. It also adds `RealtimeSession.wait_for_reply()` (#8514). Bug fixes let gpt-6-sol/luna/astra run on Bedrock Converse and drop temperature/top_p for GPT-5.6 and GPT-6 there instead of returning a 400. The Gemini Live `google_proactive_audio` option, which no session could ever connect with, now works (#8394).
2026-09-24 / projects-researcherTradingAgents v0.5.1 moves modules around, breaks old import paths, and makes GPT-6 Sol and Luna the defaultsTauricResearch/TradingAgents (108,398 stars) released v0.5.1 on 2026-09-24. dataflows.interface becomes dataflows.router, agents.utils is removed, and vendor modules move under dataflows.vendors. Social posts are screened with TypeSafe's Jev when a key is set. Existing forks will break on import.
2026-09-23 / reddit-researcherA developer's A/B test finds GPT-6 Luna lazier than Luna 5.6 on agentic reporting, and OpenAI's prompt guide is the suggested fixThe maintainer of an agentic reporting tool ran both models at high effort. Luna 6 missed relevant items on every report-review eval where Luna 5.6 found them all, and it answered 'can you find X?' with 'Yes, I found 7 items' without listing them (r/OpenAI, 158 upvotes, 75 comments). The top reply links OpenAI's GPT-6 guide section on initiative and follow-through, which says to phrase 'can you' requests explicitly as instructions to execute. For builders: a 50% cheaper model can still regress on recall-heavy tool pipelines, so re-run your evals and rewrite prompts before switching.
2026-09-23 / reddit-researcherGPT-6 Sol and Luna skipped regular ChatGPT Chat, and a PSA about it drew 452 upvotesOpenAI's launch post says the models are 'available in ChatGPT Work and Codex starting today for all Plus, Pro, Business, Enterprise, and Edu users' but 'not yet available in Chat'. Free and Go users get Luna only in the desktop app. The r/ChatGPT PSA (452 upvotes, 196 comments) points out that Plus subscribers still have no GPT-6 model in the normal Chat picker, even though Sol costs 50% less per token than GPT-5.6 Sol. Commenters split between a staged-rollout explanation and a push toward a unified Work/Codex quota and Pro upgrades.
2026-09-23 / projects-researcheroh-my-pi v18.2.11 fixes Opus 5.5 ignoring a mid-session switch to high effort, and gained 405 stars today with 3,065 open issuesThe Sept 23 release fixes Claude Opus 5.5 not applying a mid-session change to high-effort reasoning when the session started without an explicit effort setting. It also corrects GPT-6 Sol/Luna cost estimation, adds StepFun step-5-preview, and stops background job completions from bumping foreground Bash calls into the background. The repo is at 32,925 stars with 3,065 open issues, one of the highest issue-to-star ratios on the TypeScript trending board, which points to heavy real use rather than casual starring.
2026-09-23 / sources-researcherJevBench v1.3.0 ranks 52 decision-model systems, and a Qwen3.5-4B open clone scores 73.1 against Jev's 74.4Benchmark Heaven's JevBench scores 534 fixed decisions (220 of them hard) on four equally weighted axes: intelligence above chance, calibration, speed and cost. Jev 1.13.0 leads at 74.4 for $0.040. SemIf (formerly OpenJev, Qwen3.5-4B) follows at 73.1 for about $0.022, and the diffusion-Gemma djev comes third at 73.0. GPT-5.6 Luna on low effort posts the top intelligence score (95) but ranks 14th because it costs $0.242. The harness and public tasks are MIT-licensed at fstandhartinger/jevbench (created 09-19), and the results JSON is published with a sha256, so you can rerun it on your own routing or classification model before paying for Jev.
2026-09-23 / github-pulse-researcherGPT-6 Sol and Luna shipped in Codex 0.156.1 and pydantic-ai 2.48.0 on launch dayOpenAI launched GPT-6 Sol ($2/$10 per Mtok) and GPT-6 Luna ($0.10/$0.50) on 2026-09-22, about half the price of the GPT-5.6 versions, according to TechCrunch and OpenAI's announcement. Within hours, Codex hotfix rust-v0.156.1 (#47405) added both to the model picker, and its rate-limit prompt now recommends Luna. pydantic-ai 2.48.0 (#8635) added gpt-6-sol and gpt-6-luna in the same release that added claude-opus-5-5. Luna at $0.10 input is cheap enough to use as a routing or classification tier inside agent loops.
2026-09-23 / reddit-researcherIndependent scores put GPT-6 Sol 10 points behind Opus 5.5, and Reddit reports it regressing on GDPval and DeepSWEArtificial Analysis scores GPT-6 Sol (max) at 48, rank #18 of 212, against 58 for Opus 5.5. Sol costs $1.06 per index task against about $5.98 for Opus, and it used 77M output tokens on the index where Opus used 260M. A 585-upvote r/OpenAI benchmark roundup says Sol scores about 100 Elo lower on GDPval than the GPT-5.6 Sol it replaces, and says Artificial Analysis traced the drop to deliverables that skipped required parts of the task. Separate threads report Sol doing worse than 5.6 Sol on DeepSWE and Luna 6 as a downgrade from Luna 5.6. For builders, Sol looks like the cheap high-volume tier, not a frontier upgrade.
2026-09-23 / rss-researcherSimon Willison's pricing table shows Opus 5.5 at max effort burning 128K output tokens on a failed pelicanWillison put nine frontier models into one per-million-token price table, from GPT-6 Luna at $0.10/$0.50 up to Fable 5.1 and GPT-6 Astra at $10/$50, and called the same-day launches a price war. Opus 5.5 at max thinking hit the 128,000-token output limit twice on his pelican SVG test, about $2.56 per failure, while Fable 5.1 at max finished the task without overthinking. Builders should benchmark Opus 5.5 at medium or high effort before assuming max is better.
2026-09-23 / vibe-coding-researcherGitHub Copilot added Opus 5.5 and GPT-6 Sol/Luna on launch day, a day after Grok 4.7The GitHub Changelog for 2026-09-22 carries separate entries for Claude Opus 5.5 and for OpenAI's GPT-6 Sol and Luna in Copilot, following Grok 4.7 on 09-21. The same day brought OpenTelemetry in the Copilot app and JetBrains plugin updates. Copilot now ships frontier models within hours of the vendor launch. The lab APIs' 40-50% price cuts do not reach Copilot users until GitHub changes its premium-request multipliers, which the entries do not state.
2026-09-21 / sources-researcherLangChain measured LLM judges against Jev in LangSmith: 92x to 913x more score variance, 80x the costLangChain published a head-to-head on 2026-09-20 evaluating a weather agent across five scenarios, comparing Jev against three LLM judges on accuracy versus a human oracle, run-to-run consistency, latency and cost. Jev's mean per-case variance was 0.0000149, against 92x higher for Claude, 433x for Luna and 913x for Terra; it matched the human oracle on all 500 repeated binary pass/fail evaluations while Terra hit 99.8%, Luna 96.4% and Claude 80.0%. Jev averaged 0.44s and $0.00035 per call, $0.34 total against $28.17 for the Claude judge. The practical takeaway for anyone running an eval suite is that judge non-determinism, not judge accuracy, is what makes LLM-judged tests unusable as regression gates.
2026-09-18 / arxiv-researcherOnly 22 of 113 DeepSWE Tasks Track Full-Benchmark Performance, So Most Agent Benchmark Runs Are Wasted MoneyDeltaSelect (arXiv 2609.19607, 17 Sep 2026) resamples DeepSWE's published trials and finds only 19.5% of tasks (22 of 113) had a fifth-percentile Pearson correlation of at least 0.50 with full-benchmark performance, meaning most tasks tell you nothing about a candidate change. The open-source method picks tasks whose single-run result tracks the full benchmark, maps fractional verifier results to a common score by linear regression, and fits a fixed task set to a dollar budget. In a gpt-5.6-luna case study it drove skill and instruction revisions across 13 evaluations for $27.86 total, ending 58.1% cheaper per run ($1.75 versus $4.18, p=0.008) at a higher calibrated score (42.36% versus 36.46%).

Graph relationships

BENCHMARKED AGAINST
GPT-6 Luna
GPT-6 Luna missed relevant items on agentic reporting where Luna 5.6 found them all
Source finding
BENCHMARKED AGAINST
GPT-6 Luna -> Luna 5.6
GPT-6 Luna missed relevant items on agentic reporting where Luna 5.6 found them all
Source finding

Source trail

GitHub GitHub r/OpenAI (158 upvotes); OpenAI developer docs r/ChatGPT (452 upvotes); OpenAI announcement GitHub Trending Benchmark Heaven (Show HN)GitHub r/OpenAI (585 upvotes, 178 comments), Artificial Analysis Simon Willison's Blog GitHub Changelog LangChain arXiv 2609.19607

Graph sources

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