Sakana: Fugu Ultra v2

Review this 847-page technical specification document for our new API and identify any inconsistencies in authentication flow descriptions across sections 3, 7, and 12.

The model would produce a detailed cross-reference analysis highlighting three key inconsistencies: Section 3.4 describes OAuth2 token refresh using a 15-minute window, while Section 7.2 specifies 30 minutes for the same operation. Section 12.1 introduces a third-party JWT validation step absent from the initial flow diagram in Section 3. Additionally, the error code taxonomy differs—Section 3 uses AUTH_001 through AUTH_015, but Section 12 references AUTH_100 series codes without defining them. The analysis would include page numbers, exact quote excerpts, and a reconciliation table showing which specification should be considered authoritative based on document versioning metadata.

This showcases the 1M token context window handling an entire technical document at once—a task that would require chunking and reassembly in smaller-context models. The $5/$30 pricing means this single query costs roughly $4.24 for input alone, making it expensive for casual use but viable for high-stakes document review where accuracy justifies cost.

Generate a Python script that processes these 15 CSV files (attached), normalizes column names across inconsistent schemas, and outputs a unified dataset with data quality metrics.

The model would produce a complete Python script using pandas, incorporating schema detection logic that maps variations like 'customer_id', 'CustomerID', and 'cust_id' to a single canonical field. The script would include error handling for encoding issues, generate a summary report showing row counts, null percentages, and data type mismatches per file, and write both the cleaned dataset and a JSON quality report. The code would be production-ready with logging, type hints, and a command-line interface for specifying output paths.

File upload support lets the model inspect actual data structure rather than working from descriptions, reducing back-and-forth clarification. The multimodal capability here means it can handle CSVs alongside images of data dictionaries or schema diagrams in the same prompt. However, at $30/Mtok output, a 500-line script costs about $0.015—negligible per query but accumulates quickly in iterative development workflows.

Explain the architectural decisions in this codebase (12 TypeScript files attached) and suggest three refactoring opportunities that would improve testability without breaking existing API contracts.

The model would provide a structured analysis beginning with an architecture overview: the codebase uses a layered service pattern with tight coupling between the data access layer and business logic, making unit testing difficult. It would identify three concrete refactoring opportunities: (1) extracting database queries into a repository pattern with interface definitions, enabling mock injection; (2) replacing direct environment variable access with a configuration object passed through constructors; (3) splitting the 450-line UserService class into domain-specific services while maintaining the existing public methods as a facade. Each suggestion would include before/after code snippets and migration steps that preserve backward compatibility.

This demonstrates the model's ability to reason across multiple files simultaneously and maintain awareness of API surface area—critical for real-world refactoring where breaking changes have downstream costs. The image modality support means developers could also include architecture diagrams or whiteboard photos for additional context, though text-only analysis remains the primary strength here.

When 1M tokens lets legal teams batch-review entire deal folders

A 4-person legal ops team at a Series B SaaS company needs to extract termination clauses from 80 vendor contracts before renewal season. Fugu Ultra v2's 1M-token window means they can load 15-20 full contracts in a single prompt and ask for a structured comparison table—no chunking, no context loss across documents. At $5/Mtok input, a 600k-token batch costs $3 to process, and the $30/Mtok output rate stays reasonable because the model returns concise tables, not full rewrites. The trade-off: without public benchmarks, you're flying blind on accuracy for nuanced legal reasoning. If your contracts have standard boilerplate and you can spot-check the first few outputs, this is a fast, cheap way to triage. If you're dealing with bespoke IP clauses or need courtroom-grade precision, wait for benchmark data or use a model with proven legal evals.

Why Fugu Ultra v2 struggles against faster, cheaper chat models here

A 12-person e-commerce support team fields 300 tickets/day and wants to auto-tag urgency and route to the right specialist. Fugu Ultra v2's $30/Mtok output pricing makes it expensive for high-volume classification: even short 200-token responses add up to $6/1k tickets. The 1M-token context is overkill—most tickets are under 500 tokens, and you don't need to reference a year of chat history to decide if someone's order is lost or delayed. Models like GPT-4o-mini or Claude Haiku run $0.60-$1.50/Mtok output and handle simple triage at 80%+ accuracy. Fugu Ultra v2 makes sense only if you're doing complex, multi-turn escalations where you need to pull in dozens of past tickets and knowledge-base articles in one context. For straightforward tagging at scale, you're paying 20x more than you need to.

When million-token context turns a 3-day audit into a 20-minute prompt

A 5-engineer startup wants to migrate a 40k-line Python monolith from Flask to FastAPI and needs a file-by-file change plan. Fugu Ultra v2's 1M-token window lets them upload the entire codebase—models, routes, tests, config—and ask for a dependency graph plus a sequenced refactor roadmap. At $5/Mtok input, the ~200k-token codebase costs $1 to ingest, and the detailed output plan runs another $3-5. The alternative is manually tracing imports for days or chunking the code across 20 prompts and stitching answers together. The risk: no public benchmarks means you can't verify if the model correctly parses complex decorators or async patterns. Run the first plan on a isolated module, validate the suggestions in CI, then scale up. If the model hallucinates imports or misses edge cases, fall back to a code-specialized model with proven HumanEval scores.