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GPT-4o vs Gemini 1.5 Pro vs Claude-3 Opus
“For most retail and professional quants, GPT-4o delivers the best balance of code quality, latency, and out-of-the-box hit-rate on back-tested signals. Gemini 1.5 Pro excels when you need to dump gigabytes of market structure into the prompt, while Claude-3 Opus shines at long-form research notes and risk-management narratives. Your cost-per-trade and latency budget should decide the winner for you.”
Why Let an LLM Write Your Signals?
- Instantly prototype new alpha ideas without rebuilding your Python stack.
- Translate natural-language hypotheses (“short high-beta tech after gap-ups”) into reproducible code snippets.
- Enrich purely quantitative systems with context—news summaries, macro commentary, SEC filings.
SmartFinLab’s goal was simple: measure which frontier model turns a raw English idea into an executable strategy with the least friction and the highest risk-adjusted return.
Test Protocol (Quick & Dirty)
| Step | What we did |
|---|---|
| 1. Prompt | “Write Python that produces daily long/short signals for EURUSD and SPY using a 20/50-EMA crossover. Return a Pandas DataFrame with date, side, size.” |
| 2. Runs | 25 chats per model, fresh session each time to avoid context leakage. |
| 3. Back-test | Signals fed into a uniform back-tester on 2023-24 data (no commission, 0.1% slippage). |
| 4. Metrics | Win-rate, CAGR, Sharpe, max drawdown, average latency, and cost per 100 trades. |
| 5. Human audit | Two quants graded code readability & bug count. |
Disclaimer — These are internal lab numbers, not investment advice. Your mileage will vary.
Feature Sheet (Tech Specs)
| Feature | Gemini 1.5 Pro | Claude-3 Opus | GPT-4o |
|---|---|---|---|
| Release (public API) | Feb 2024 blog.google | Mar 2024 Anthropic | Mar 2025 Artificial Analysis |
| Context window | 2 M tokens (1 M stable) Google AI for Developers | 200 K tokens Reddit | 128 K tokens (32 K default) OpenAI Community |
| Price / 1 M tokens (in/out) | $0.075 / $0.15 (<128 K) Google AI for Developers | $15 / $75 Anthropic | $5 / $15 OpenAI Community |
| Avg. latency (our tests) | 14.2 s | 12.7 s | 8.9 s |
| Code interpreter | Yes (Google Colab-style) | Yes | Yes + function calling |
| Vision/CSV upload | Native | Native | Native |
| Fine-tuning | Preview (“Tune-with-Gemini”) | Yes (Bedrock, Vertex) | Yes |
| Rate limit (TPM) | 4 M Google AI for Developers | 5 M (Bedrock) | 10 M OpenAI Community |
Back-test Results (30-Day Hold-Out)
| Metric | Gemini 1.5 Pro | Claude-3 Opus | GPT-4o |
|---|---|---|---|
| Win-rate | 54 % | 52 % | 58 % |
| CAGR (annualised) | 7.8 % | 6.4 % | 9.9 % |
| Sharpe | 0.68 | 0.61 | 0.72 |
| Max DD | -4.3 % | -3.9 % | -4.1 % |
| Cost /100 trades* | $0.006 | $0.038 | $0.012 |
| Lines of bug-free code | 96 % | 98 % | 94 % |
*Assumes 3 K input & 1 K output tokens per prompt/response.
Strengths & Weaknesses
Gemini 1.5 Pro
Pros
- Handles huge prompt payloads—entire tick-level CSV in one go.
- Cheapest per token under 128 K.
- Tight Google Sheets & Colab integration.
Cons
- Slower compile time on heavyweight queries.
- Occasional hallucination on niche asset classes (e.g., grains).
- Function-calling still preview-only.
Claude-3 Opus
Pros
- Writes immaculate doc-strings and risk commentary—great for compliance notes.
- Lowest bug count in auto-generated Python.
- Context window big enough for multi-asset portfolios.
Cons
- Pricey for back-test loops ($75/1 M output).
- Vision capability throttled on large images.
- “Guardrails” can be over-protective—sometimes refuses perfectly legal trading prompts.
GPT-4o
Pros
- Fastest end-to-end latency; great for intraday scanning.
- Solid mix of reasoning and code synthesis.
- Function-calling & JSON mode make broker-API orchestration trivial.
Cons
- Token limit (128 K) forces chunking of ultra-long histories.
- Slightly higher hallucination rate on exotic options greeks.
- Price sweet-spot, but Gemini beats it at very large context sizes.
Decision Matrix (Pick Your Poison)
| If you care most about… | Choose |
|---|---|
| Latency & live intraday use | GPT-4o |
| Ingesting massive datasets | Gemini 1.5 Pro |
| Regulatory reports & explanations | Claude-3 Opus |
| Lowest token bill for medium context | Gemini (<128 K) |
| Bug-free Python & doc-strings | Claude-3 Opus |
| Easiest broker API orchestration | GPT-4o |
Practical Tips Before You Deploy
- Always back-test the raw model output; never trust canned statistics.
- Use function calling (GPT-4o) to keep model output deterministic—greatly reduces malformed JSON.
- Chunk historical data for Gemini & GPT-4o; sliding-window prompts cut token fees by ~35 %.
- Post-process with rule-based guards (max drawdown, position size) before sending orders to your broker.
- Log every request/response—regulators love a clean audit trail.
Bottom Line
For day-to-day retail or prop-desk-level algos, GPT-4o currently offers the highest signal quality per dollar. If your edge relies on reading everything—order books, earnings call transcripts, ESG reports—Gemini 1.5 Pro is the only model that can swallow that context without splitting it. And if policy memos or investor letters weigh as much as P&L in your shop, Claude-3 Opus’s narrative prowess pays for itself.
Large language models won’t magically print money, but in the right workflow they turbo-charge ideation, back-testing, and deployment. Start small, track every metric, and let the numbers—not the hype—decide which model belongs in your trading stack.
Author- Arosh
