Updated 29 min ago · first seen 12 Sept 2026
model_01M29XKN4EYYNHP0PA0KK7314X
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 recorded — closed weightsofficial_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
Closed / proprietary— weights not available; 7 dimensions unknown.
Weights are not available; the model is reachable only through an API or a product.
Weights
No
Inference code
—
Training code
—
Training data
—
Dataset
—
Commercial use
—
Redistribution
—
Derivatives
—
dimensions marked null are unknown, not false
Key facts
- Status
Source:Mistral AI docsT2observed 10 h agomediumLLM-extracted
Architecture
- Architecture
Source:Mistral AI docsT2observed 10 h agomediumLLM-extracted
- Parameters
Source:Mistral AI docsT2observed 10 h agomediumLLM-extracted
- Mixture of experts
Source:Mistral AI docsT2observed 10 h agomediumLLM-extracted
Capabilities
Modalities
- Modalities
- Navigation commands (pointing coordinates/orientation or local frame displacements)Plain-language instructionRGB images
- Input
- Plain-language instructionRGB images
- Output
- Navigation commands (pointing coordinates/orientation or local frame displacements)
Capabilities
Tool calling
Unavailable
Structured output
Unavailable
Reasoning
Unavailable
Vision
Yes
Mistral AI docs · T2
Audio
No
Mistral AI docs · T2
Fine-tuning available
Unavailable
- Languages
Source:Mistral AI docsT2observed 10 h agomediumLLM-extracted
Hardware fit37
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 →Versions & Artifacts0
Version history
Opennessfirst observation only
11 Sept 2026current
Parametersfirst 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.
Timeline1
Full timeline →Change history
Output modalitiesmodalities_output1
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
13
Source tiers
T213
Freshest observation
29 min ago
Conflicts
None
Source documents 1
Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.
Data quality (50/100) measures how well AI Atlas knows this entity — completeness, primary-source ratio, freshness, conflicts — never how good the model is. Methodology →