标签：#moe

In production we have ~1.5 TB of full-text court decisions and their vector embeddings, plus another ~550 GB of other legal data: registries, legislation, business entities, a Spanish case law corpus, EU-Lex. If we take this corpus and train an MoE model the size of DeepSeek V3, scaled to 860B parameters, on GCP — what comes out? We break down the dataset, architecture, compute cost, and the properties such a model would have on Ukrainian law. What's in the Dataset The entire corpus is what's already running in SecondLayer's production. No extra scrapes, no Common Crawl, no noise. EDRSR — the dataset core, ~1.5 TB. The Unified State Register of Court Decisions of Ukraine. 96.2 million full-text decisions (1,079 GB in PostgreSQL TOAST), 471 GB of vectors in Qdrant (voyage-3.5, 1024-dim), 28 GB of metadata (court, judge, date, case category, proceeding type, statute code). Breakdown by jurisdiction: civil 33.7M, administrative 14M+, criminal 12M+, commercial 6M+, misdemeanors 6M+. Largest annual cohort — 2024 (115 GB of TOAST text). OpenReyestr — 43 GB. Ukrainian public registries: 16.7M legal entities (EDR), ownership structures (beneficiaries, shareholders), debtors (State Enforcement Service), NAIS registries. This is the foundation for SneakyPiper — our due-diligence platform — but here it serves as raw corpus for the model. Legislation — ~40 GB. The Constitution, major codes (Civil, Criminal, Criminal Procedure, Civil Procedure, Commercial Procedure, Administrative Procedure, Labor, Tax, Customs), laws, and secondary legislation. All structurally annotated: articles, parts, clauses, revision dates with effective-date tracking. This isn't flat text: we know that Article 124 of the Constitution took effect on a specific date, carries particular references, and is cited in a precise number of decisions. Supreme Court review practices + lu_court_decisions — ~25 GB. SC plenary decisions, practice overviews, Grand Chamber rulings. This is the most valuable slice — the

The all-cash deal gives MoEngage access to technology that assigns AI agents to individual customers.

Step 3.7 Flash shipped on May 29, 2026 as a structural upgrade to 3.5 Flash: same OpenAI-compatible SDK, new vision encoder, new runtime escalation, and a compute-control flag you can set per request. The migration from 3.5 is two environment variables. One of them has to be exactly right — or every call returns a silent 401. What 3.7 brings that 3.5 didn't Step 3.7 Flash adds three net-new capabilities over 3.5 Flash: a native 1.8B-parameter ViT encoder that injects image representations directly into the language backbone without a separate model call , an automatic Advisor Mode that routes failure-prone subtasks to a larger model at runtime, and a reasoning_effort parameter (low / medium / high) as a first-class API flag rather than a prompt-engineering convention. The production-relevance number is variance: 3.5 Flash scores ranged from 43% to 73% across different harnesses ; 3.7 narrows that to 64.5–71.5% , which matters more for production scheduling than the raw score improvement. Quick Answer: Step 3.7 Flash is an OpenAI-SDK-compatible model — model string step-3.7-flash , base URL https://api.stepfun.ai/v1 (global) or https://api.stepfun.com/v1 (China region). New over 3.5: native vision input, automatic Advisor Mode escalation, and a reasoning_effort flag. The only breaking change from 3.5: base URL must match your account region exactly, or you get a 401 with no error body. The architecture is a 198B sparse MoE model with roughly 11B parameters active per forward pass — dense-10B compute cost at much larger capacity. SWE-Bench Pro improved to 56.3% from 51.3% ; Terminal-Bench 2.1 improved to 59.5% from 53.4% , suggesting the planning and shell-operation gains that matter for coding agents are consistent across benchmarks. Advisor Mode carries the headline cost claim from StepFun's internal harness: 97% of Claude Opus 4.6's coding performance at $0.19 vs. $1.76 per task . That's a vendor figure on a first-party SWE-Bench Verified run — treat it as directio