NAURA Innovation LAB · Data Team
LLM Selection
— Data Team
Evaluating the best AI providers for our daily operations
Round 1 — US Providers
✦ Pros
Leader in "System 2" thinking — highly reliable for architectural planning and identifying edge cases in distributed systems.
✦ Cons
Code style more rigid compared to Claude. Smaller context window limits full-repo analysis.
✦ Pros
Gold standard for code — 80.8% SWE-bench Verified. MCP is the industry standard for agent integrations. Excels at complex refactors and long-horizon tasks.
✦ Cons
Narrower context window vs Gemini. Less native tooling ecosystem than Google/OpenAI.
✦ Pros
The "Context King" — 2M+ token window ingests entire repos. Unparalleled for cross-file dependency analysis and legacy code migration.
✦ Cons
Code quality trails Claude on complex refactors. Agent/tooling ecosystem less mature than Claude's MCP.
Round 1 — US Providers
✦ Pros
Superior for multimodal data — analyzes 10-hour technical recordings or massive PDF datasets natively, extracting insights text-only models miss.
✦ Cons
Reasoning depth not on par with GPT-5.4 for abstract strategy and multi-step planning.
✦ Pros
Leads in strategic depth — multi-step "thought-loops" make it the best for technical roadmaps and abstract business strategy.
✦ Cons
No native multimodal ingestion at Gemini's scale. GPT-5.4 lock-in via API pricing.
✦ Pros
Focused on precision — preferred for legal and financial data where nuance and instruction-following are more critical than raw speed.
✦ Cons
Reasoning depth trails GPT-5.4 on abstract strategy. Less suited for multimodal data ingestion.
Round 1 — US Providers
✦ Pros
Known for the most "human" and professional prose. Artifacts allow real-time preview of dashboards and UI components — top pick for rapid prototyping.
✦ Cons
No native workspace suite integration. Artifacts great for prototyping but not a full presentation tool.
✦ Pros
Unbeatable integration — native connection to Google Workspace (Slides, Docs, Sheets) means it generates full presentation decks directly in your Drive.
✦ Cons
Weaker prose quality for polished deliverables. Code quality trails Claude on complex work.
✦ Pros
Canvas is the most robust collaborative environment for iterative editing of whitepapers and executive summaries.
✦ Cons
No native workspace suite integration like Gemini. Smaller context window limits large document ingestion.
Round 1 — US Providers
| Category | Weight | | | |
|---|---|---|---|---|
| Coding & Logic | 45% | 10 | 9 | 7 |
| Cross-Repo Context | 25% | 8 | 6 | 10 |
| Team Collaboration (Drive/Slides/Doc) | 20% | 7 | 7 | 10 |
| Data Analysis & Scripting | 10% | 7 | 10 | 8 |
| TOTAL SCORE | 100% | 8.75 | 8.15 | 8.45 |
Round 2 — Chinese Providers
✦ Pros
Leader in systems engineering — excels at understanding low-level hardware constraints and C++/Rust environments. Developed by Zhipu AI.
✦ Cons
Less proven on high-level application logic compared to MiniMax and DeepSeek. Smaller international community.
✦ Pros
Surprise contender — 80.2% SWE-bench rivals Claude Opus. Optimized for agentic tool use, highly effective for "self-healing" code pipelines.
✦ Cons
Single-agent logic can be outclassed by focused models. Less ecosystem maturity than US providers.
✦ Pros
Reasoning specialist — "Thinking" mode critiques plans and identifies logical fallacies in technical roadmaps.
✦ Cons
Not purpose-built for coding tasks. Slower response times due to complex reasoning chains.
Round 2 — Chinese Providers
✦ Pros
Strongest at structured "B-side" (enterprise) business logic — used by Chinese corporations for internal knowledge graph synthesis.
✦ Cons
Less versatile for general-purpose data analysis. Weaker on abstract strategic reasoning.
✦ Pros
Reasoning specialist — "Thinking" mode designed for critiquing plans and spotting logical fallacies. Handles extremely long-context reasoning (1M tokens) with high stability.
✦ Cons
Optimized for deep analysis — not ideal for quick, lightweight queries. Premium pricing for enterprise tier.
✦ Pros
Highly favored for math-heavy data science and algorithmic optimization due to strong STEM benchmark performance.
✦ Cons
Strong on quantitative findings but weaker on strategic interpretation and business storytelling.
Round 2 — Chinese Providers
✦ Pros
Leads in multimodal "flair" — best native text-to-video and text-to-audio integration. Favorite for high-impact multimedia collateral.
✦ Cons
Creative focus can overshadow formal accuracy. Less suited for dry, data-heavy executive reports.
✦ Pros
Only provider that outputs polished .docx and .pptx files natively. High professional finish for enterprise deliverables.
✦ Cons
Output tone tends to be overly formal — less suited for agile or casual cultures.
✦ Pros
Known for "creative strategy" — output style is less dry than DeepSeek, suitable for project proposals and marketing collateral.
✦ Cons
Creative style can feel out of place in formal, conservative settings. Less structured than GLM for enterprise formats.
Round 2 — Chinese Providers
| Category | Weight | | | |  |
|---|---|---|---|---|---|
| Software & Architecture | 40% | 9.0 | 8.5 | 10.0 | 8.5 |
| Data & Strategy | 30% | 9.5 | 9.5 | 8.0 | 8.5 |
| Deliverables & Collateral | 15% | 10.0 | 8.0 | 9.0 | 6.5 |
| Cost & Latency (TPS) | 15% | 6.5 | 7.5 | 9.0 | 10.0 |
| Total Score | 100% | 8.85 | 8.55 | 9.15 | 8.30 |
