Which AI model should you choose for your application? Claude excels at reasoning, GPT-4 has broad capabilities, Gemini offers multimodal strengths, and open-weights models like Llama run anywhere. But how do you compare them directly? You could read individual benchmark papers, parse leaderboard results, or trust marketing materials. None of these show you the full picture on a single screen.
NeoSignal Model Comparison with radar chart and score table
NeoSignal Model Comparison solves this with visual comparison. Select up to 4 models from the sidebar, and immediately see a radar chart overlaying their performance across five dimensions: Intelligence, Reasoning, Code, Math, and Instruction Following. Below the chart, a score breakdown table shows exact numbers for each dimension, highlighting the leader in each category. Claude Opus 4.5 leads in Code at 96.0 while Gemini 3 Pro scores 93.0—see it at a glance. The tool integrates with NeoSignal AI Chat, so you can ask follow-up questions about the models you're comparing.
The benefit: visual model comparison in seconds instead of hours of research. Select models, see the chart, understand tradeoffs, make your decision.
Detailed Walkthrough
The Model Comparison Problem
Comparing AI models is harder than it should be:
Benchmark Fragmentation: Each model gets evaluated on different benchmarks at different times. MMLU scores don't directly translate to HumanEval results. LMArena ELO captures human preference but not mathematical capability.
No Side-by-Side View: Model providers publish their own benchmark results. You can't easily see Claude vs GPT-4 vs Gemini on the same chart with the same methodology.
Dimension Trade-offs: Every model has strengths and weaknesses. One leads on reasoning, another on code generation. Understanding these trade-offs requires synthesizing multiple data sources.
NeoSignal Model Comparison centralizes the data, normalizes the methodology, and presents it visually.
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Interface Design
The comparison tool uses a split-panel layout for efficient interaction:
Left Panel: Model Selection
- List of available models with search filtering
- Click the + icon to add a model (up to 4)
- Selected models appear at the top with color indicators
- Each model shows its NeoSignal composite score
- Click X to remove a model from comparison
Right Panel: Comparison View
- Radar chart showing all dimensions
- Score breakdown table with rankings
- Links to individual model detail pages
- Data attribution to Epoch AI
The layout ensures you can quickly adjust your selection while seeing results update in real-time.
The Radar Chart
The radar chart plots each model's performance across NeoSignal's five scoring dimensions:
| Dimension | What It Measures | Why It Matters |
|---|---|---|
| Intelligence | Overall reasoning and task completion | General capability indicator |
| Reasoning | Multi-step logical reasoning | Complex problem solving |
| Code | Code generation, understanding, debugging | Development applications |
| Math | Mathematical reasoning and problem solving | Technical and scientific use |
| Instruction Following | Ability to follow complex instructions | Reliability in production |
Each model gets a color-coded polygon. Where polygons overlap, models are similar. Where they diverge, you see the trade-offs. A model with a larger overall area has higher aggregate capability. A model with spikes in certain directions has specialized strengths.
The chart uses a consistent 0-100 scale across all dimensions, normalized from the underlying benchmark data. This means you can compare dimensions directly—a score of 95 on Code is genuinely higher than a score of 90 on Math.
Score Breakdown Table
Below the radar chart, the score breakdown table provides exact numbers:
Column Structure
- Dimension name (leftmost)
- One column per selected model
- Color indicator matching the radar chart
- Numerical score to one decimal place
Winner Highlighting For each dimension, the highest score appears in accent color. This makes it immediately clear which model leads where:
- Claude Opus 4.5 might lead on Code (96.0)
- Gemini 3 Pro might lead on Math (94.0)
- GPT-4o might lead on Intelligence (97.0)
Overall Score Row The bottom row shows each model's composite NeoSignal score, with the overall leader highlighted. This aggregates the dimension scores using NeoSignal's weighted formula:
Score = (Intelligence × 0.30) + (Math × 0.20) + (Code × 0.20) +
(Reasoning × 0.15) + (Instruction Following × 0.15)
Model Selection
The selection panel supports efficient model discovery:
Search: Type to filter the model list. Search matches model names and descriptions.
Score Display: Each model shows its NeoSignal score, so you can identify high-performers before selecting.
Selection Limit: Maximum 4 models for clarity. More than 4 makes the radar chart unreadable and the table too wide.
Selection Persistence: Your selections persist during the session. Refresh or close the tab to reset.
Exclude Benchmark-Only Models: Models without full dimension data (benchmark-tier) are filtered out to ensure all selected models have comparable data.
Chat Integration
Model Comparison integrates with NeoSignal AI Chat through context-aware smart prompts:
Automatic Context: When you have models selected, the chat knows what you're comparing. Ask "How do these models differ in reasoning ability?" without listing the model names.
Suggested Prompts: The chat panel suggests relevant questions:
- "Which model is best for code generation?"
- "Compare benchmark performance across these models"
- "How do these models differ in reasoning ability?"
Follow-Up Analysis: After reviewing the comparison, ask the chat to explain specific differences: "Why does Claude score higher on instruction following?" The response draws from NeoSignal's knowledge base with citations.
Data Sources
Model Comparison uses the same data that powers NeoSignal's scoring system:
Primary Sources
- LMArena ELO for human preference
- Artificial Analysis Intelligence Index
- MATH benchmark for mathematical reasoning
- HumanEval for code generation
- MMLU-Pro for broad knowledge
- IFEval for instruction following
Normalization All scores are normalized to a 0-100 scale based on the performance range across all tracked models. A score of 95 means the model performs in the top 5% for that dimension.
Update Frequency Data updates as new benchmark results are published. Score recalculations happen automatically.
Real-World Usage Patterns
New Project Evaluation: Starting a new application? Compare the leading models in your priority dimension. If code generation matters most, filter for high Code scores. If reasoning is critical, focus there.
Migration Decisions: Considering switching from one model to another? Compare them directly. See exactly where the new model improves and where it might regress.
Open vs Closed Comparison: Comparing Llama 3.1 405B against Claude or GPT-4? See how open-weights models stack up against proprietary alternatives across all dimensions.
Team Discussions: Need to justify a model choice to your team? Screenshot the comparison showing your preferred model's strengths in the dimensions that matter for your use case.
Capability Gap Analysis: Identify where models are catching up. If Gemini 3 Pro nearly matches Claude on reasoning but trails on code, that gap might close in future versions.
Mobile Experience
Model Comparison works on mobile devices with an adapted layout:
Stacked Panels: On narrow screens, the selection panel appears above the comparison view rather than alongside.
Touch Targets: All buttons meet minimum 44px touch target size for comfortable interaction.
Responsive Chart: The radar chart scales to fit the available width while maintaining readability.
Horizontal Scroll: The score table supports horizontal scrolling when comparing 4 models on narrow screens.
Limitations and Considerations
Dimension Coverage: Some models lack data for certain dimensions. Missing values appear as dashes in the table and are excluded from the radar chart.
Score Recency: Benchmark results may lag model releases. Newly announced models might not have complete dimension scores immediately.
Aggregate Nature: The composite score weights dimensions equally within categories. Your application might weight dimensions differently—use the breakdown table to apply your own priorities.
Benchmark Selection: NeoSignal uses specific benchmarks for each dimension. Different benchmark choices could yield different rankings.
From Comparison to Decision
NeoSignal Model Comparison transforms model evaluation from a research project into a visual exercise. Instead of reading multiple papers, parsing leaderboard tables, and building spreadsheets, you select models and see the comparison instantly.
The radar chart shows you the shape of each model's capabilities. The score table shows you the exact numbers. Together, they make trade-offs visible. Claude leads here, GPT-4 leads there, Gemini is strong across the board but not the leader in any single dimension.
That's the NeoSignal approach: aggregate the data, normalize the methodology, present it visually. You focus on which model fits your needs. The tool handles the comparison.