NeoSignal Model Cards: Intelligence Profiles for Every AI Model

Which model should you use? Claude scores higher on reasoning, GPT-4 has broader capabilities, Llama 3.1 is open-weights and runs anywhere. Each benchmark tells a different story. MMLU measures knowledge, HumanEval measures code, LMArena ELO measures human preference. Comparing models means juggling dozens of benchmarks, each with different methodologies and scales. By the time you've built a spreadsheet, a new model has dropped and the rankings have shifted.

NeoSignal Model Cards showing scored AI models with metrics

NeoSignal Model Cards synthesize the complexity. Each card displays a composite score (0-100) calculated from weighted dimensions: intelligence, math, code, reasoning, and instruction following. Data sources include LMArena ELO, Artificial Analysis Intelligence Index, MATH benchmark, HumanEval, MMLU-Pro, and more. The card shows key metrics—context window, provider, ELO rating—at a glance. A trend indicator signals whether the model is rising, stable, or declining in the landscape. Hover to see compatibility with frameworks and accelerators. Click through for the full breakdown.

The benefit: you compare models on a consistent scale. NeoSignal does the benchmark aggregation so you can focus on which model fits your use case.

Detailed Walkthrough

The Scoring Problem

AI model comparison is fragmented. Consider what it takes to evaluate a model:

Benchmark Overload: MMLU, MMLU-Pro, HumanEval, MBPP, GSM8K, MATH, ARC-Challenge, BBH, HellaSwag, WinoGrande, TruthfulQA, IFEval—each measures something different. No single benchmark captures "model quality."

Methodology Differences: LMArena uses ELO from human battles. Artificial Analysis tests with consistent prompts. HuggingFace Open LLM Leaderboard aggregates six benchmarks. Results don't directly compare.

Rapid Updates: New models drop weekly. Benchmark results trickle in over days. By the time you've analyzed the data, it's already stale.

NeoSignal Model Cards solve this by maintaining a composite scoring system that aggregates authoritative sources into a single, comparable score.

The Scoring Rubric

NeoSignal scores models across five weighted dimensions:

The composite score combines these dimensions:

Score = (Intelligence × 0.30) + (Math × 0.20) + (Code × 0.20) +
        (Reasoning × 0.15) + (Instruction Following × 0.15)

Each dimension score is normalized to 0-100 based on the performance range across all tracked models.

Card Anatomy

Each Model Card displays essential information at a glance:

Header Section

• Model name with provider logo
• Category tag (Models, in purple)
• Composite score badge with trend indicator

Metrics Grid

• ELO: LMArena rating
• Context: Maximum context window
• Provider: Model provider name

Hover State After 500ms of hovering, the card expands to show compatibility:

• Top 3 most compatible components
• Compatibility scores (color-coded)

Click-Through Links to the full component detail page with:

• Score breakdown by dimension
• Complete metrics table
• All compatibility mappings
• Related signals

Score Breakdown

The component detail page shows how the composite score is calculated:

Dimension Scores: Each of the five dimensions displays its individual score with the data sources that informed it.

Score History: How the model's score has changed over time as new benchmark data arrives.

Comparison Context: Where this model ranks among all tracked models.

For example, Claude 3.5 Sonnet might show:

• Intelligence: 92 (LMArena ELO: 1275)
• Math: 88 (MATH-Lvl5: 78.3%)
• Code: 95 (HumanEval: 92.0%)
• Reasoning: 90 (BBH: 86.2%)
• Instruction Following: 94 (IFEval: 91.5%)
• Composite: 91

Trend Indicators

Each model displays a trend indicator:

Rising: Model is gaining ground in benchmarks or climbing the ELO ladder. New capabilities or improvements are being recognized.

Stable: Model performance is consistent. No significant changes in relative ranking.

Declining: Newer models are surpassing this one. Still capable, but no longer at the frontier.

Trend is calculated from score changes over the past 90 days and relative position shifts on leaderboards.

Compatibility Mapping

Models don't exist in isolation. They run on frameworks, accelerators, and cloud providers. NeoSignal tracks compatibility across the stack:

Inference Framework Compatibility

• vLLM: PagedAttention support, model architecture compatibility
• TensorRT-LLM: Optimized kernels, quantization support
• llama.cpp: GGUF conversion availability
• SGLang: Structured generation support

Accelerator Compatibility

• H100/H200: Optimized kernels, FP8 support
• A100: CUDA compute compatibility
• Apple Silicon: MLX or llama.cpp support
• TPU: JAX/XLA support

Cloud Provider Compatibility

• AWS Bedrock: Managed API availability
• Azure OpenAI: Regional availability
• GCP Vertex AI: Model garden inclusion
• Together/Fireworks: Inference API support

Compatibility scores range from 0-100:

• 90-100: Excellent (native support, optimized)
• 70-89: Good (works well, some limitations)
• 50-69: Moderate (works, not optimized)
• Below 50: Limited (possible but not recommended)

The Metrics Grid

The three-metric grid on each card shows the most relevant information for quick comparison:

For Models:

• ELO: Human preference ranking
• Context: Maximum input length
• Provider: Who built the model

For Accelerators:

• Memory: GPU memory in GB
• TFLOPS: Compute capability
• Architecture: Hardware generation

For Cloud:

• Regions: Geographic availability
• Availability: GPU supply status
• Tier: Pricing category

For Frameworks:

• Stars: GitHub popularity
• Downloads: Weekly PyPI/npm downloads
• License: Open source status

Category-Specific Dimensions

While models use the five dimensions above, other categories have their own rubrics:

Accelerators (3 dimensions):

• Performance (45%): Raw compute and memory bandwidth
• Availability (30%): Market supply and cloud instance availability
• Ecosystem (25%): Software stack maturity and framework support

Cloud (3 dimensions):

• GPU Availability (40%): Ability to provision GPUs on demand
• Pricing (30%): Cost competitiveness
• Support (30%): Technical support quality and SLAs

Frameworks (3 dimensions):

• Performance (35%): Execution speed and efficiency
• Adoption (35%): Community size and industry usage
• Ecosystem (30%): Integrations and extensions

Agents (5 dimensions):

• Planning/Reasoning (25%): Task decomposition capabilities
• Tool Use (25%): Function calling accuracy
• Memory/Context (20%): Information retention
• Self-Reflection (15%): Error recognition
• Adoption (15%): Community usage

Data Sources

NeoSignal aggregates data from authoritative sources:

Tier 1 (Primary Research):

• SemiAnalysis: GPU market analysis
• LMArena: Human preference ELO
• Anthropic, OpenAI, DeepMind: Official benchmarks
• NVIDIA: Hardware specifications

Tier 2 (Industry Analysts):

• ThoughtWorks Technology Radar
• a16z State of AI reports
• HuggingFace Open LLM Leaderboard
• Artificial Analysis benchmarks
• MLPerf benchmark consortium

Data is updated continuously as new benchmarks are published. Score recalculations happen automatically.

Filtering and Search

Category pages let you filter and sort Model Cards:

Search: Find models by name Score Range: Filter by minimum/maximum score Trend Filter: Show only rising, stable, or declining Sort Options: By score, name, trend, or update date

Chat Integration

Model Cards integrate with NeoSignal AI chat:

Context Awareness: When viewing a model card, the chat knows which model you're looking at. Ask "How does this compare to GPT-4o?" without specifying the model name.

Score Clarification: "Why is the reasoning score 90?" triggers an explanation using the specific benchmark data that informed that dimension.

Compatibility Questions: "Will this model work well with vLLM?" gets analysis based on the compatibility mapping.

Real-World Usage Patterns

Model Selection: You're starting a new project requiring strong code generation. Filter by code dimension, sort by score. Compare the top options' full breakdowns. Choose based on the tradeoffs that matter for your use case.

Upgrade Decisions: You're using an older model. Check its trend indicator—if declining, look at rising alternatives with similar capability profiles.

Stack Planning: You've selected a model. Check its compatibility section. Which frameworks have the best support? Which cloud providers offer managed endpoints?

Benchmark Tracking: A new model was announced. Check its NeoSignal card to see the composite score and where it ranks, rather than parsing individual benchmark papers.

From Cards to Decisions

NeoSignal Model Cards compress the complexity of AI model comparison into scannable, consistent profiles. Every model is scored on the same dimensions using the same methodology. Every compatibility mapping uses the same scale.

The goal isn't to replace deep evaluation—it's to give you a starting point. Model Cards tell you which models deserve closer investigation and which don't match your requirements. They surface the information that matters: How good is it? Is it improving? What does it work with?

That's the NeoSignal approach: take the fragmented, rapidly-changing AI model landscape and make it navigable through consistent scoring, clear visualizations, and integrated compatibility data.