scoring.md

Scoring

whichllm does not pick the largest model that fits. It ranks candidates by a composite score that tries to answer a more practical question:

Of the models that can run here, which one is likely to be the best usable choice?

The source of truth is engine/ranker.py.

Inputs

Each candidate score uses:

• model metadata from HuggingFace
• detected or simulated hardware
• estimated VRAM/RAM fit
• estimated tok/s
• quantization type
• benchmark evidence
• downloads and likes
• source organization
• model lineage and generation

The score is capped to 0..100.

Benchmark evidence

Independent benchmark matches are not all treated equally.

Source	Weight	Meaning
direct	0.62	Exact independent benchmark match
base_model	0.55	Match through cardData.base_model
variant	0.50	Suffix-stripped variant match
line_interp	0.40	Size-aware model-line interpolation
self_reported	0.30	Uploader-provided HuggingFace eval only
none	0.00	No benchmark evidence

self_reported evidence is intentionally weak. HuggingFace model cards can contain useful evaluation data, but it is not the same as an independent leaderboard.

Size score

Model size is used as a rough world-knowledge proxy:

size_score = 4.2 * log2(params_b) + 9

The result is capped at 35.

For dense models, params_b is the parameter count. For MoE models, whichllm uses total parameters for quality because all experts contribute to stored knowledge. Active parameters are used later for speed.

Quantization penalty

Lower-bit quantization can make a larger model fit, but it also reduces quality. The score core is multiplied by (1 - quant_penalty).

Examples:

Quant	Penalty
Q8_0	0.01
Q6_K	0.02
Q5_K_M	0.03
Q4_K_M	0.05
Q3_K_M	0.08
Q2_K	0.25
IQ2_XXS	0.40
Q1_0	0.55

Extreme low-bit variants are excluded by default when better candidates exist. They can still be requested explicitly with --quant.

Evidence confidence

After benchmark and size are combined, weak evidence is dampened:

Evidence state	Multiplier
Direct benchmark	1.00
Inherited evidence	0.78
Self-reported evidence	0.55
No benchmark	0.55

For inherited benchmark evidence, the raw score is also scaled by confidence before entering the scoring function. Line interpolation therefore receives a double discount: once for its interpolation confidence and once for being inherited evidence.

Runtime fit

The candidate's runtime form matters:

Fit	Multiplier
Full GPU	1.00
Partial offload	0.42-0.88, based on spill ratio
CPU-only	0.50

Light partial offload is penalized less than heavy offload. MoE models receive a milder penalty when the active parameter working set can plausibly stay on GPU while inactive experts spill to CPU RAM.

The final family selection key does not add a separate full-GPU bonus. Runtime fit is already reflected in the quality score through the multiplier above and the speed adjustment below. CPU-only results receive a small extra sort penalty when mixed with GPU-backed candidates.

Speed adjustment

Speed is treated as a usability gate. It is not the main quality signal.

Required speed depends on fit:

Fit	Required speed
Full GPU	8 tok/s
Partial offload	4 tok/s
CPU-only	1.5 tok/s

Candidates below the required speed receive up to -8 points. Candidates above it receive up to +8 points.

After ranking, if any candidate is at least 5 tok/s, whichllm drops candidates below 1.5 tok/s. This avoids recommending models that technically fit but are not practical to use.

The reported speed is a point estimate, not a live benchmark. Ranking also exposes speed confidence:

Confidence	Range factor	Typical cases
medium	0.60x-1.60x	Normal GPU estimates, synthetic GGUF estimates, AMD shared-memory APU MoE estimates
low	0.35x-2.00x	CPU-only, partial offload, unknown bandwidth, Apple Silicon MoE
high	0.85x-1.20x	Reserved for future measured-speed data

Speed cells are colored by absolute usability: red is under 4 tok/s, yellow is 4-10 tok/s, green is 10-30 tok/s, and bright green is 30+ tok/s. ~ marks medium-confidence estimates with a range, and ? marks low-confidence estimates. JSON exposes the same uncertainty data as speed_confidence, speed_range_tok_per_sec, and speed_notes.

Source trust

The source organization contributes a small adjustment:

• official model organizations receive a small bonus
• trusted GGUF converters can inherit that trust
• known repackagers receive a small penalty

The adjustment is intentionally small. It should break ties, not replace benchmark and fit signals.

Popularity

Downloads and likes act as tie-breakers. Their weight is lower when benchmark evidence is strong and higher when evidence is weak.

Popularity has no effect for direct benchmark matches.

Generation lineage

Some benchmark sources are frozen. A model released after a frozen leaderboard cannot appear there, while older models can keep strong but stale scores.

whichllm uses family-specific lineage maps to avoid that inversion. Newer generations can receive a small bonus; older generations can receive a small penalty. This is applied carefully so direct benchmark evidence still matters.

Examples of tracked lineages include:

• Qwen
• Llama
• DeepSeek
• Gemma
• Phi
• Mistral
• GLM
• Kimi
• Granite
• OLMo
• T5 (incl. Flan-T5, mT5, ByT5, T5Gemma)

Benchmark markers

The table score can include a marker:

Marker	Meaning
none	Direct independent benchmark evidence
~	Estimated or inherited benchmark evidence
!sr	Self-reported HuggingFace eval only
?	No benchmark evidence

Top-pick confidence is computed from the score gap, benchmark status, and fit type. Partial-offload and CPU-only top picks are reported with lower confidence than full-GPU direct-benchmark winners.

Why a smaller model can win

A smaller model can outrank a larger one when it has:

• stronger current benchmark evidence
• a newer generation signal
• better quantization quality
• full-GPU fit instead of partial offload
• higher estimated speed
• a more trustworthy source

That is intentional. whichllm ranks likely usable quality, not parameter count alone.