OpenAI Models
Review OpenAI model families for reasoning, coding, multimodal, real-time, and cost-sensitive product workloads.
Open guide →AI model decision library
Choose the right model for the workload—not the loudest benchmark.
Use DataKnobs guides to shortlist provider, open-weight, domain and regional model families, then validate them against your own task, context, risk and operating constraints.
1. TaskReasoning, coding, retrieval, multimodal, voice, extraction, or workflow action.
2. ContextGeneral knowledge, enterprise documents, tools, memory, structured data, and policy.
3. ConstraintsQuality, latency, cost, residency, privacy, deployment, and supportability.
4. EvidenceEval set, failure analysis, shadow testing, production telemetry, and rollback.
A task-first decision model
Model selection is an engineering and business decision
A model can look excellent on a public benchmark and still be the wrong production choice. Decide using the workflow you actually need to operate.
01
Define the task
Write the outcome in one sentence and identify whether the system must generate, retrieve, reason, classify, transform, converse, or act.
02
Define the context
Specify proprietary data, freshness, tools, memory, permissions, and citations required for a correct answer or action.
03
Set operating constraints
Set targets for quality, p95 latency, cost per successful task, data boundary, scale, availability, and portability.
04
Run controlled evaluation
Compare candidates on the same representative inputs and failure taxonomy. Promote a model only when the evidence improves.
Guide library
Browse by the decision you are making
Provider pages change quickly. These guides are best used as starting points for a shortlist; verify current model availability, pricing and limits with the provider before production.
Claude Models
Review Anthropic Claude model families for reasoning, coding, long-context work, and enterprise workflows.
Open guide →Gemini Models
Review Google Gemini model families for multimodal, long-context, cloud, and device-oriented AI workloads.
Open guide →Meta AI Models
Understand Llama, vision, segmentation, and generative AI options for open and custom deployments.
Open guide →Mistral Models
Review Mistral model families for general language, coding, multimodal, and efficient deployment patterns.
Open guide →Alibaba Qwen Models
Review Qwen language, coding, math, vision, and audio models for global AI applications.
Open guide →xAI Grok Models
Explore Grok model options for reasoning, vision, real-time apps, and production APIs.
Open guide →Sarvam AI Models
Review Indic LLMs, speech recognition, TTS, translation, and API choices.
Open guide →BioMistral Models
Explore biomedical and healthcare-focused model choices for clinical, research, and life-sciences workflows.
Open guide →Bloomberg Models
Review finance-oriented models for market research, filings, financial language, and enterprise analytics.
Open guide →FinGPT Models
Compare financial LLM approaches for sentiment, risk, research, reports, and quantitative workflows.
Open guide →Apertus Models
Explore Apertus model guidance for regional, multilingual, and sovereign AI planning.
Open guide →Krutrim Models
Review India-focused model options for Indic languages, local use cases, and production AI apps.
Open guide →Small Language Models
Use SLMs for lower-latency, cost-efficient, private, or task-specific AI deployments.
Open guide →Foundation Models
Understand the model layer behind modern generative AI products and enterprise AI platforms.
Open guide →RAG Slides
Plan retrieval-augmented generation systems that connect LLMs with trusted enterprise content.
Open guide →Context Engineering
Design context layers, memory, tools, retrieval, and guardrails for reliable AI applications.
Open guide →Prompt Engineering
Improve instructions, examples, output formats, and evaluation loops for better AI responses.
Open guide →Multimodal AI
Explore AI systems that combine text, images, documents, audio, video, and structured data.
Open guide →Evaluation
Shortlist with benchmarks. Decide with your workload.
The production question is not “Which model is best?” It is “Which configuration meets our quality, cost, latency and risk targets for this workflow?”
| Dimension | What to measure | Why it matters |
|---|---|---|
| Task quality | Task success, graded correctness, evidence coverage, refusal quality | Captures whether the user’s actual goal was achieved. |
| Reliability | Failure categories, variance, tool errors, unsupported claims | Averages can hide rare but costly failures. |
| Latency | p50 / p95 / p99 end-to-end time | Agent and retrieval stacks can add substantial tail latency. |
| Economics | Cost per successful task, not just cost per token | Retries, retrieval, tools and human review all belong in the denominator. |
| Governance | Data handling, auditability, policy controls, model/version lineage | Production teams need to know what ran, with which context and permissions. |
| Changeability | Migration effort, fallback, routing, shadow testing | Model availability, behavior and pricing change; the architecture should absorb change. |
Where DataKnobs fits
Treat the model as a governed Knob
The model is one adjustable component inside a larger AI system. Context, prompt, retrieval, tools, autonomy and policy may matter just as much.
KKREATEBuild the application, context and evaluation pipeline around the model.
CKONTROLSDefine data boundaries, approvals, lineage, audit and safety evidence.
NKNOBSCompare model and configuration variants, measure outcomes, promote and roll back deliberately.
FAQ
Model-selection questions
How should an enterprise choose an AI model?
Start with the task and evaluation set, then compare candidates on quality, latency, cost, context needs, data handling, deployment constraints and operational evidence. Public benchmarks help form a shortlist; they do not replace workload-specific testing.
Should we standardize on one model?
Not necessarily. Different tasks can favor different models. A small governed portfolio with explicit routing and evaluation criteria can be more resilient than forcing one model across every workload.
When should we consider self-hosted or open-weight models?
When deployment location, data control, customization, cost at scale or vendor independence materially outweigh the operational responsibility of serving, securing and maintaining the model.