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Qwen2.5-Math

Qwen Teamfamily · Qwen2.5

Open in Graph
data quality56

Updated 7 h ago · first seen 12 Sept 2026

model_01M29X37HWS1FBV8GEFBYARDBY

Overview

Identity

Canonical model
Yesidentity confidence: highOne row per real model release. Artifacts (checkpoints, quantisations, conversions) and folded evaluation variants point here.
Official checkpoints
None recordedofficial_checkpoints = hf_repo identifiers carried by the model itself; artifacts are separate entities pointing here through canonical_id.
Artifacts
None recordedSeparate entities (checkpoint · quantization · conversion · packaging) pointing to this model through canonical_id.
Provider deployments
None recorded
API aliases
NoneIdentifiers under which providers and evaluators refer to this model.
Folded evaluation variants
0Effort / thinking variants (…-high, …-non-reasoning) are result configurations of this model, not separate models. Their old URLs redirect here.

Openness

Open weights— weights downloadable; 7 dimensions unknown.

Weights downloadable under a permissive or Creative Commons licence allowing commercial use; code or data may be missing.

  • Weights

    Yes

  • Inference code

    —

  • Training code

    —

  • Training data

    —

  • Dataset

    —

  • Commercial use

    —

  • Redistribution

    —

  • Derivatives

    —

dimensions marked null are unknown, not false

Key facts

Release date

Source:Qwen — official blogT2observed 16 h agomediumLLM-extracted

Status

Source:Qwen — official blogT2observed 16 h agomediumLLM-extracted

Version

Source:Qwen — official blogT2observed 16 h agomediumLLM-extracted

Architecture

Architecture

Source:Qwen — official blogT2observed 16 h agomediumLLM-extracted

Capabilities

Modalities

Modalities
imagetext
Input
imagetext
Output
text

Capabilities

  • Tool calling

    Unavailable

  • Structured output

    Unavailable

  • Reasoning

    Yes

    Qwen — official blog · T2

  • Vision

    Yes

    Qwen — official blog · T2

  • Audio

    Unavailable

  • Fine-tuning available

    Yes

    Qwen — official blog · T2

Context window

Source:Qwen — official blogT2observed 16 h agomediumLLM-extracted

Languages

Source:Qwen — official blogT2observed 16 h agomediumLLM-extracted

Lineage

Open in Graph →
Explicit derived_from / fine_tuned_from / distilled_from relations stated by sources; artifacts collapsed by kind.
ANCESTORS 2Qwen2.572.7BQwen2.5 — 72.7BQwen2Qwen2Qwen2.5-Maththis modelQwen2.5-Math — this model

Versions & Artifacts0

Version history

Openness1 change

11 Sept 2026→12 Sept 2026current

Context windowfirst observation only

11 Sept 2026current

Statusfirst observation only

11 Sept 2026current

Each hop is a claim: click a value for its source, tier and observation time. Nothing is overwritten — a new observation closes the previous claim.

Artifacts 0

No artifact (checkpoint, quantisation, conversion or packaging) points to this model yet.

Change history

Temporal, append-only claims: a new observation closes the previous claim instead of overwriting it. Rewind the record with the as-of picker.
1 claims · 1 propertiesShow all properties

Weights availableweights_available1

Claim history for Weights available
ValueValid from → toStatusSourceConfidenceExtractor
Yes → currentcurrentAI Atlas curated registry (YAML, versioned in git, every entry carries its source URL)T2highderived

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

Provenance

Attributed facts

15

Source tiers

T215

Freshest observation

7 h ago

Conflicts

None

Source documents 2

Source documents
SourceDocumentTypeTierLast observedSnapshots
Qwen — official blogqwenlm.github.io/blog/qwen2.5-math newsT1· Official16 h ago1
Qwen — official blogqwenlm.github.io/blog/qwen2.5 newsT1· Official16 h ago1

Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.

Data quality (56/100) measures how well AI Atlas knows this entity — completeness, primary-source ratio, freshness, conflicts — never how good the model is. Methodology →