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Qwen2.5

Qwen Teamfamily · Qwen2.5

Open in Graph
data quality65

Updated 5 h ago · first seen 12 Sept 2026

model_01M29X37HCFESK6GEZVEDZKA82

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

Restricted weights— weights downloadable under Research-Only; commercial use restricted; 6 dimensions unknown.

Weights downloadable, but the licence restricts commercial use, hosting, derivatives or field of use (community, research and RAIL licences).

  • Weights

    Yes

  • Inference code

    —

  • Training code

    —

  • Training data

    —

  • Dataset

    —

  • Commercial use

    No

  • Redistribution

    —

  • Derivatives

    —

Licence: Research / non-commercial licence (custom) (research-only · stated as “Apache 2.0 (most models), Qwen Research License (Qwen2.5-3B), Qwen License (Qwen2.5-72B)”)

dimensions marked null are unknown, not false

Key facts

Release date

Source:Qwen — official blogT2observed 14 h agomediumLLM-extracted

Status

Source:Qwen — official blogT2observed 14 h agomediumLLM-extracted

Version

Source:Qwen — official blogT2observed 14 h agomediumLLM-extracted

Official page
qwen.ai

Source:Qwen — official blogT2observed 14 h agomediumLLM-extracted

Paper
qwenlm.github.io/blog/qwen2.5

Source:Qwen — official blogT2observed 14 h agomediumLLM-extracted

Repository
huggingface.co/Qwen

Source:Qwen — official blogT2observed 14 h agomediumLLM-extracted

Architecture

Architecture

Source:Qwen — official blogT2observed 14 h agomediumLLM-extracted

Parameters

Source:Qwen — official blogT2observed 14 h agomediumLLM-extracted

Active parameters

Source:Qwen — official blogT2observed 14 h agomediumLLM-extracted

Mixture of experts

Source:Qwen — official blogT2observed 14 h agomediumLLM-extracted

Capabilities

Modalities

Modalities
text
Input
text
Output
text

Capabilities

  • Tool calling

    Yes

    Qwen — official blog · T2

  • Structured output

    Yes

    Qwen — official blog · T2

  • Reasoning

    Yes

    Qwen — official blog · T2

  • Vision

    No

    Qwen — official blog · T2

  • Audio

    No

    Qwen — official blog · T2

  • Fine-tuning available

    Yes

    Qwen — official blog · T2

Context window

Source:Qwen — official blogT2observed 14 h agomediumLLM-extracted

Max output

Source:Qwen — official blogT2observed 14 h agomediumLLM-extracted

Languages

Source:Qwen — official blogT2observed 14 h agomediumLLM-extracted

Hardware fit37

Estimated

23 of 37 device × quantization combinations fit.

Run locally: your machine →
Estimated hardware fit
HardwareQuantizationDevice memoryEst. memoryFits
Apple M3 Ultra4bit—42.3 GB est.Yes
Mac Studio (Apple M5 Ultra)4bit—42.3 GB est.Yes
Apple M2 Ultra4bit—42.3 GB est.Yes
Apple M1 Ultra4bit—42.3 GB est.Yes
Apple M3 Max4bit—42.3 GB est.Yes
Apple M4 Max4bit—42.3 GB est.Yes
Mac Studio (Apple M5 Max)4bit—42.3 GB est.Yes
MacBook Pro (Apple M5 Max)4bit—42.3 GB est.Yes
Apple M2 Max4bit—42.3 GB est.Yes
Apple M1 Max4bit—42.3 GB est.Yes
Apple M4 Pro4bit—42.3 GB est.Yes
Mac mini (Apple M5 Pro)4bit—42.3 GB est.Yes
MacBook Pro (Apple M5 Pro)4bit—42.3 GB est.Yes
NVIDIA A100 80GB4bit80 GB42.3 GB est.Yes
NVIDIA H100 SXM4bit80 GB42.3 GB est.Yes
NVIDIA H100 NVL4bit94 GB42.3 GB est.Yes
NVIDIA DGX Spark4bit128 GB42.3 GB est.Yes
NVIDIA H2004bit141 GB42.3 GB est.Yes
NVIDIA H200 NVL4bit141 GB42.3 GB est.Yes
NVIDIA B2004bit180 GB42.3 GB est.Yes
AMD Instinct MI300X4bit192 GB42.3 GB est.Yes
AMD Instinct MI325X4bit256 GB42.3 GB est.Yes
NVIDIA DGX B2004bit1,440 GB42.3 GB est.Yes
Apple M3 Pro4bit—42.3 GB est.No
Apple M1 Pro4bit—42.3 GB est.No
Apple M2 Pro4bit—42.3 GB est.No
Apple M44bit—42.3 GB est.No
iMac (Apple M4)4bit—42.3 GB est.No
Mac mini (Apple M6)4bit—42.3 GB est.No
MacBook Air (Apple M5)4bit—42.3 GB est.No
MacBook Pro (Apple M5)4bit—42.3 GB est.No
Apple M24bit—42.3 GB est.No
Apple M34bit—42.3 GB est.No
Apple M14bit—42.3 GB est.No
NVIDIA GeForce RTX 30904bit24 GB42.3 GB est.No
NVIDIA GeForce RTX 40904bit24 GB42.3 GB est.No
NVIDIA GeForce RTX 50904bit32 GB42.3 GB est.No
Assumptions (7)
  • Estimated, not measured: weights = parameters × bytes/param × 1.15 runtime overhead (or the observed artifact file size when one is recorded).
  • bytes/param: 4bit = 0.5, 8bit = 1.0, fp16 = 2.0 (uniform quantization, no per-layer exceptions).
  • KV cache: 2 × layers × kv_heads × head_dim × 2 bytes × context × batch when the architecture is known; otherwise 0.5 GB per 8 192 tokens (× batch), independent of architecture (GQA/MLA models need less).
  • A model 'fits' when the estimate is at most the device memory minus 2 GB reserved for the OS and framework.
  • Mixture-of-experts models are estimated on total parameters (all experts must be resident); active parameters are ignored.
  • Device memory uses the largest configuration when several are listed (e.g. Apple silicon tiers).
  • Multi-GPU: device memories are summed; interconnect bandwidth, tensor-parallel replication and pipeline bubbles are not modelled.

Lineage

Open in Graph →
Explicit derived_from / fine_tuned_from / distilled_from relations stated by sources; artifacts collapsed by kind.
ANCESTORS 1DESCENDANTS 2Qwen2Qwen2Qwen2.5-Coder32.5BQwen2.5-Coder — 32.5BQwen2.5-MathQwen TeamQwen2.5-Math — Qwen TeamQwen2.572.7B params · this modelQwen2.5 — 72.7B params · this model

Versions & Artifacts0

Version history

Context window1 change

11 Sept 2026→11 Sept 2026current

License1 change

11 Sept 2026→11 Sept 2026current

Max output1 change

11 Sept 2026→11 Sept 2026current

Openness1 change

11 Sept 2026→12 Sept 2026current

Parameters1 change

11 Sept 2026→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

Commercial use allowedcommercial_use_allowed1

Claim history for Commercial use allowed
ValueValid from → toStatusSourceConfidenceExtractor
No → 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

29

Source tiers

T229

Freshest observation

5 h ago

Conflicts

None

Source documents 6

Source documents
SourceDocumentTypeTierLast observedSnapshots
Qwen — official blogqwenlm.github.io/blog/qwen2.5-math newsT1· Official14 h ago1
Qwen — official blogqwenlm.github.io/blog/qwen2.5-coder newsT1· Official14 h ago1
Qwen — official blogqwenlm.github.io/blog/qwen2.5-llm newsT1· Official14 h ago1
Qwen — official blogqwenlm.github.io/blog/qwen2.5 newsT1· Official14 h ago1
Qwen — official blogqwenlm.github.io/blog/qwen2.5-turbo newsT1· Official14 h ago1
Qwen — official blogqwenlm.github.io/blog/qwen3 newsT1· Official14 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 (65/100) measures how well AI Atlas knows this entity — completeness, primary-source ratio, freshness, conflicts — never how good the model is. Methodology →