Developer Considers Switching from DeepSeek to Grok for Finance AI Agent
Finance AI Agent Performance Issues and Potential Switch
A developer has built a finance AI web app in FastAPI/Python that functions similarly to Perplexity but for stocks. The application runs a parallel pipeline before the LLM processes queries, including live stock quotes from several finance APIs, live web search from finance search APIs, and earnings calendar data. All this structured context gets injected into the system prompt, with the model handling only reasoning and formatting while facts come from APIs, making hallucination rates less relevant for this use case.
Current Model Performance Problems
The developer is currently using DeepSeek V3.2 Reasoning and reports significant performance issues:
- TTFT (Time to First Token): ~70 seconds
- Output speed: ~25 tokens per second
- Streaming experience described as "terrible"
- Stream start timeout set to 75 seconds to avoid constant timeouts
Application Requirements
The finance AI agent has two main features:
- Chat stream: Perplexity-style finance analysis with inline source citations
- Trade check stream: Trade coach that outputs GO/NO-GO/WAIT with entry, stop-loss, target, and R:R ratio
Model requirements include:
- Fast performance with low TTFT and high tokens per second for streaming UX
- Low cost for a small project
- Smart enough for multi-step trade reasoning
- Good instruction following for strict output formats in trade checks
Considering Grok 4.1 Fast Reasoning
The developer is considering switching to Grok 4.1 Fast Reasoning based on these comparisons:
- TTFT: ~15 seconds (vs DeepSeek's ~70s)
- Output speed: ~75 tokens per second (vs DeepSeek's ~25 t/s)
- AA intelligence score: 64 vs DeepSeek's 57
- Input cost: $0.20 vs $0.28 per million tokens
Other Models Considered
The developer has also looked at Minimax 2.5, Kimi K2.5, new Qwen 3.5 models, and Gemini 3 Flash, but notes most are relatively expensive and not better for their specific use case.
📖 Read the full source: r/LocalLLaMA
👀 See Also
Testing OpenClaw for Multi-Country Trip Planning with MoLOS Integration
A developer tested OpenClaw with MoLOS to plan a China-Japan trip, generating day-by-day itineraries, flight/hotel suggestions, and 50+ automated tasks while identifying limitations in transport times and attraction validation.
Using Claude Code to Build a Satellite Image Analysis Pipeline for Retail Predictions
A developer used Claude Code to build a complete satellite imagery analysis pipeline that pulls Sentinel-2 optical and Sentinel-1 radar data via Google Earth Engine, processes parking lot boundaries from OpenStreetMap, and calculates occupancy metrics to predict retail earnings outcomes.
Building a Proactive Monitoring Agent on OpenClaw: The LLM Wiki Pattern in Production
An OpenClaw-based agent called Oogway proactively monitors jobs, investigates anomalies, and updates its own wiki with root causes and fixes. The key insight: persistent memory compounds knowledge, reducing re-derivation.
Comparing PRD Execution: Bash Loop vs. Agent Teams in Claude Code
A developer benchmarked PRD execution with Claude Code using both a bash loop and the Agent Teams feature. The Agent Teams approach was found to be significantly faster, although it had some coordination overhead.