AI Model Profile
text-embedding-3-large
text-embedding-3-large is OpenAI's larger embedding model for vector representations of text.
- Family
- Embeddings
- Release date
- Unknown
- Status
- Current
- Context window
- Embedding input limits vary by API; verify current docs.
- Output limit
- Vector embedding output
- API
- yes
- Open weights
- no
- Local/self-hosted
- no
- Pricing
- See official pricing or platform page; rates can vary by endpoint, region, tier, batch mode, and partner platform.
- Evidence state
- Recheck due
Verification & Sources
- Evidence state
- Recheck due
- Source links
- 2
- Freshness
- Needs recheck: checked June 24, 2026
- Last updated
- July 30, 2026
What this evidence state means
- Definition
- The claim was previously checked, but its review window expired or a material change may have invalidated it.
- Required provenance
- The prior evidence and check date are retained, together with the expiry or change signal that triggered recheck.
- Owner
- Kingy freshness queue owner and assigned editorial reviewer
- Freshness rule
- This is already outside its freshness rule. It must not be presented as current until reviewed against current evidence.
- Disputes and corrections
- Use “Suggest a correction” on the record. Kingy editorial reviews the cited evidence, records material corrections, and changes or removes the state when it is not supported.
Key source checks
Suggest a correction
Benchmark Caveat
Benchmarks and provider capability notes are directional, not universal rankings. Results can shift with prompts, tool use, latency targets, pricing tier, safety filters, context length, and the workload mix a real team runs.
See the linked official model, docs, model-card, or pricing source for provider-published capability notes.
Best for
Semantic search, retrieval, clustering, recommendations, and higher-quality embedding use cases.
Skip if
Skip if you need the lowest embedding cost or non-OpenAI hosting.
Strengths
OpenAI's all-models catalog lists text-embedding-3-large as its most capable embedding model.
Weaknesses
Embedding quality depends on corpus, chunking, metadata, retrieval stack, and evaluation set.
Agent suitability
Useful for agent workflows when the provider supports tool use, long context, structured outputs, or workflow-specific APIs.
Kingy AI take
Use this as a source-backed shortlist candidate, not a universal ranking. Re-check official provider docs and run a task-specific trial before production adoption.
Full Model Notes
text-embedding-3-large is OpenAI’s larger embedding model for vector representations of text.
The Kingy Brief
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One consequential launch, one pricing, limit, or shutdown change, one hands-on test, one exact prompt or Test Pack, and one try / watch / skip verdict.
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Coding notes
Use official docs and live evals before selecting this model for production coding workflows.
Reasoning notes
Provider capability notes are useful but should be validated on representative prompts and tools.
Creative notes
Use a small creative test set before standardizing outputs for brand, media, or customer-facing work.
Research notes
Track release notes and model lifecycle notices because availability and aliases can change.
API pricing notes
Check the official pricing page before budget decisions; Kingy does not freeze token, credit, or subscription prices in model cards.
License notes
Commercial/API terms apply unless the linked official source states otherwise.
Hardware requirements
Cloud/API model; local hardware requirements are not published as a self-hosted path.
Official Model Links
Model Intelligence Research Map
Use these internal paths to move from this model profile into provider pages, static comparison pages, related Kingy records, and the broader AI launch graph. These are research paths, not rankings.