A clear score. An open methodology.

Sources & methodology

AIpediya Rating combines model quality, cost and available resources into one score. Alongside the number, we explain how well the evidence supports it.

How to read the scale

From 0 to 100%: a higher score reflects a stronger combination of quality, price efficiency and applicable resources. This is a composite score, not accuracy on a single test.

0%20%40%60%80%100%
LowerMiddle of the scaleHigher

Compare models within the same rating context. We show a rank only when sufficient evidence is available.

What the asterisk means

A small * beside the score indicates incomplete, estimated or limited data. It describes confidence in the estimate: open the tooltip to see what is included and what is missing.

Verified

No asterisk: key evidence is confirmed and the score meets verification requirements.

Partially verified

Some evidence is confirmed; gaps remain in the estimate.

Estimated

Evidence is sparse; broad baseline estimates are used.

Unknown does not mean poor. When direct evidence is missing, we use a neutral baseline with a wide uncertainty interval. The main score remains the central estimate; a conservative estimate informs confidence and leader comparisons.

Three components, one score

Quality

Permitted test results, accounting for the exact model version and evaluation conditions.

Price

Comparable standard API cost. Free hosted API and local execution are treated separately.

Resources

Confirmed context capacity or the relevant resource for this model type.

Local execution; cost depends on hardware and electricity

Details and sources

Here are the calculation rules, limitations and data provenance. Open any model panel to view its complete audit.

pplx-embed-v2-late-0.6b · AIpediya Rating 48.3%*

Embeddings and retrieval · EMB.RETRIEVAL@BALANCED · Estimated

Included:

Missing: independent verification · confirmed context / resource · direct evidence · second independent runner

Precision C · Support C · Rating data coverage 0.0%

Uncertainty interval: 13.2–83.9

Displayed rating formula: 100 × Q^0.93 × (0.2+0.8C)^0.00 × (0.3+0.7K)^0.07 = 48.3

capability=0.472022634188 · price=N/A · resource=0.500000000000

Capability: applicable, missing · context_quality

Standard API price: not applicable · self_hosted_tco

Resource component: applicable, missing · embedding_input_tokens

Additional numerical adjustment: 0.0

Conservative estimate for confidence and ranking: 13.2

Economics state: self_hosted

Local execution; cost depends on hardware and electricity

Unknown facts use a prior for calculation and remain unconfirmed in the catalog.

MODELS@BALANCED-v1.0-2026-11-03-09b7dbf0bb34

Data as of 2026-11-03

One public rating

Public AIpediya Rating always uses BALANCED. Contexts describe comparable tasks; there is no universal ranking across model types. Quality-first and economy profiles remain internal engine options for future collections.

Permitted external evidence

The canonical master stores exact model, configuration, task, runner, numeric result, date and source. Independent external results and Arena preferences are separate from weaker developer reports. Nonpublic research, composites, superseded duplicates and revoked permissions are excluded. AIpediya aggregates external results and does not operate an evaluation lab.

Frozen capability scale

The original frozen capability basket, item coefficients and scale are unchanged. New approved items require at least five exact frozen-theta anchors and append-only ridge binding. An unbound item may be stored in master but never silently scored. Arena Bradley-Terry ratings and Agent IPS are separate metrics with separate frozen transforms.

Caps and exact configurations

Family cap is 1.5 effective observations; Epoch cap 6, other runner cap 2. Arena keeps its cap of one effective observation and 25% of precision. Developer evidence is weaker and has a 20% precision share cap. The engine chooses one exact configuration by objective family coverage, then effort. Different efforts are never pooled; an unspecified effort is not inferred.

Arena and LiveBench

Arena comes only from the official lmarena-ai/leaderboard-dataset on Hugging Face, with CC BY 4.0 attribution. Raw and style-controlled versions and aggregate/subsignals are not double counted. LiveBench uses officially published task results, with the official release/category definitions. It is a separate objective family and independent external runner. Only subtasks are imported; category and global averages are excluded.

Price and applicable resources

Standard hosted API prices use the existing frozen anchors and comparable units. A confirmed permanent free hosted API may have C=1. Local weights without verified comparable TCO have economics N/A/self-hosted. Resource definitions depend on context: LLM token context, embedding input limit, TTS input limit, STT audio duration, image resolution and video duration. Unconfirmed applicable resources remain missing. No new arbitrary resource anchors are created to raise scores.

Applicability and weights

Each component is applicable-known, applicable-missing or not applicable. N/A has no estimate and zero weight, and is removed from the coverage denominator. Remaining BALANCED weights are normalized from 78/16/6. Missing applicable components keep their original neutral estimate and wide uncertainty. N/A and unknown are not interchangeable.

Evidence gates

A qualified Overall model needs two non-Arena families in two domains. A context needs sufficient models, families and external runners. Specialized contexts additionally require validated distinctiveness. Storing new results does not itself make a context Ready. Unapproved SWE-bench/CODE research remains excluded.

Displayed score and uncertainty

Displayed Rating is the central composite 100 × Q^wq × (0.2+0.8C)^wc × (0.3+0.7K)^wk over applicable components. Every snapshot stores its mask, exact components, base/effective weights and explicit adjustment, currently zero. Runtime verifies reproduction. The tenth percentile is a separate conservative confidence/frontier estimate and never replaces the displayed score.

Precision, Support and coverage

Precision describes statistical uncertainty after misfit and early-evidence adjustments. Support counts independent external runner paths: A at least three, B two, C fewer. Developer reports never become independent paths. Coverage is the fraction of relevant quality, applicable price/resource, exact configuration, external runner diversity and breadth. N/A is excluded; missing evidence affects confidence and the asterisk.

Freshness

Freshness uses the snapshot data cutoff, not the request clock. Price/API availability thresholds are 30/60 days, resource 180/365, configuration 90/180. Expired required facts become unknown. A benchmark is excluded for invalidation or revoked permission, not simply age. Snapshot age is a separate warning.

Compatible ranks and frontier

Exact public rank is limited to qualified models within one compatible context. Coverage below 20% suppresses exact rank and labels the score as based on limited data. Conservative frontier uses the frozen meaningful gap delta theta 0.28 and original eligibility gates. Catalog numeric sorting does not invent a cross-context #N.

Reproducibility

Builds use 6,000 deterministic Monte Carlo draws, or 12,000 in the existing threshold zone. Stable Record IDs define seeds. Scientific computation runs offline after canonical master changes; runtime reads validated JSON only. Historical snapshots and original calibration bytes are preserved.

Asterisk and limits

No asterisk means the relevant inputs and corroboration requirements are met. Partial * means corroboration is incomplete; Estimated * means a required input is unknown or quality remains a prior. Unknown does not mean bad. Single-factor quality and per-token economics cannot represent every real task; preference results do not become objective benchmarks.

How the catalog is verified

AIpediya is an editorial reference catalog of AI models and AI tools. Models and tools are separate catalogs with their own numbering, filters and cards. It is a curated selection, not a complete registry of every model that exists.

Sources and check dates

Each record is based on sources: first the developer's official documentation, announcement, model card or pricing page; reputable secondary sources only where no official source exists. Every card links its source and shows when an editor last checked it. The check date is not the release date and not a test run by AIpediya.

What gets published

Publication requires reviewed evidence of the product's existence and identity. Unresolved existence or identity questions keep a candidate in the internal review queue. A real product may remain public with unknown prices, dates or origins, explicitly distinguished from confirmed data. Removing a record from publication preserves its history.

Release dates and catalog numbers

An exact release date is shown only when a source confirms it. Otherwise the earliest reliable date of public existence is shown with the ≈ sign and month or day precision. Unknown dates stay empty and those records appear last in chronological order. Catalog numbers follow verified release dates from oldest to newest; releases on the same date are ordered by name.

Prices

Prices are the provider's published list prices in US dollars with the unit stated, for example per 1 million input tokens, per image, per second of video or per month. Input and output tokens, batch, off-peak and other tiers are kept separate together with their conditions. A free tier is not free unlimited API access, and a subscription belongs to a service rather than to one model version. Taxes, quotas and regional terms can change the final cost. An unknown price stays empty, never zero.

Country of origin

Confirmed origins identify documented countries of the organizations or independent teams responsible for a product, with sources. More than one country can apply. A tentative origin is separately labeled and supported by the available evidence; it is not a confirmed country. An unknown origin does not by itself hide a real product.

Independent evaluations

Only results of the same benchmark and protocol are comparable. Each result names the evaluator, benchmark, configuration, snapshot and check date. Developer results are separate from independent runs and composite indexes. Third-party results require verified exact model identity and permission to publish. AIpediya Rating v1.0 is computed separately from permitted evidence; its contexts and limitations are explained below.

Open weights and licenses

Open weights means the weights can be downloaded under the license stated by the developer. It is not the same as open source, and running a model locally still requires hardware and energy.

Unknown values and conflicting sources

Unknown is shown as empty, never as zero or as “no”. When sources disagree or a value cannot be confirmed, the value stays empty or the record stays in review; values are never averaged or guessed.

Updates and translations

The owner directs research assisted by AI agents and automated consistency checks, and approves publication. Reviewed batches update records, prices, facts and evaluations; an internal history preserves changes. Daily verification of every source is not guaranteed. English and Russian are the authored source languages. Other translations follow the existing pipeline; missing or outdated primary translations fall back to English and prevent indexing of that locale until ready.

Personal features and public access limits

A public general-purpose API and a public per-record revision log are not available. Personal saved models and tools, subscriptions, notifications and product discussions are available through the existing account features; they are separate from the catalog's evidence and rating calculations.

Contexts and data snapshot

Data as of 2026-11-03 · Methodology text version v1.0

AIpediya Overall LLM · #1 AIpediya Overall LLM

Conditionally ready: most models have Support C · Data as of 2026-11-03

Claude Opus 5.5 · P(№1) 65.65% · MCSE 0.61%

GPT-6 Sol · P(№1) 18.97% · MCSE 0.51%

GPT-6 Astra · P(№1) 8.55% · MCSE 0.36%

Table rank does not imply a #1 badge

Sources & methodology · v1.0
LLM.OVERALLConditionally ready: most models have Support CModels: 63
LLM.MATHBetaModels: 46
LLM.LOGICBetaModels: 26
LLM.CODEInsufficient dataModels: 0
LLM.AGENTSInsufficient dataModels: 0
LLM.LONGDOCInsufficient dataModels: 0
LLM.KNOWLEDGEInsufficient dataModels: 0
LLM.SCIENCEInsufficient dataModels: 0
LLM.VISIONInsufficient dataModels: 0
LLM.CHATInsufficient dataModels: 0
LLM.FORECASTInsufficient dataModels: 0
LLM.MULTILINGUALInsufficient dataModels: 0
STT.TRANSCRIBEInsufficient dataModels: 0
STT.STREAMINGInsufficient dataModels: 0
TTS.NARRATIONInsufficient dataModels: 0
TTS.REALTIMEInsufficient dataModels: 0
VOICE.AGENTInsufficient dataModels: 0
IMG.GENInsufficient dataModels: 0
IMG.EDITInsufficient dataModels: 0
VID.GENInsufficient dataModels: 3
EMB.RETRIEVALInsufficient dataModels: 0
DOC.OCRInsufficient dataModels: 0
AUD.MUSICOut of scope v1Models: —
SAFETY.CLASSIFIEROut of scope v1Models: —
SCI.STRUCTUREInsufficient dataModels: —
SCI.FORECASTInsufficient dataModels: —
ROBOT.ACTIONInsufficient dataModels: —
3D.GENInsufficient dataModels: —
MOTION.GENInsufficient dataModels: —

MODELS@BALANCED-v1.0-2026-11-03-09b7dbf0bb34

SHA-256 09b7dbf0bb34412e955d01cf4f0cd2f214f29e9cafe0ebd1befccc82bc4d01e9

How the catalog is verified

Each result retains its source, benchmark, model version and run conditions. We use only data permitted for publication; developer results are shown separately from independent evaluations.

Models: 466 · Tools: 186

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