Current Large Language Models and What They Generate Best (TickTockIT)

Large language models are no longer interchangeable chatbots. Current model families differ in reasoning depth, coding ability, writing style, context handling, multimodal input, tool use, deployment options, governance, latency and cost.

This comprehensive Version 3 guide explains the major active model families, what each is best at generating, where OpenAI Codex fits, and how organisations should choose between frontier, balanced, small, open-weight and specialist models.

Quick Selection Guide

What “Best” Means

Public benchmarks do not represent every real workflow. A model should be evaluated against the quality of the required output, its ability to complete the whole task, tool reliability, latency, total cost, context handling, deployment constraints and governance requirements.

• Output quality: factual accuracy, completeness, clarity and adherence to instructions.
• Task completion: whether the model finishes the workflow rather than merely suggesting an approach.
• Tool reliability: correct API calls, terminal use, structured output and retrieval.
• Operational fit: latency, cost, scaling, data retention and auditability.

OpenAI Codex: More Than Code Completion

What GPT-5.3-Codex Generates Best

• Complete features spanning multiple files and directories.
• PowerShell, Python, PHP, JavaScript, TypeScript, C#, Java, SQL and shell scripts.
• Unit, integration and regression tests.
• Dockerfiles, Compose files, Kubernetes manifests and infrastructure-as-code definitions.
• CI/CD workflows, Git automation and repository documentation.
• Database migrations, API clients, webhooks and third-party integrations.
• Repository-wide refactoring and software migrations.
• Bug fixes grounded in logs, test failures and reproducible behaviour.

Codex is most valuable when it can inspect the repository and use a controlled development environment. It can plan changes, edit multiple files, run commands, inspect the result, correct failures and present a reviewable diff. The unit of work becomes a software-engineering task rather than a single prompt.

When to Use Codex

• The project structure and existing code matter.
• The task requires changes across several files.
• Tests or build commands must be executed.
• A migration or refactor must preserve existing behaviour.
• The output must be a working implementation, not only an explanation.

When a General Model Is Better

• Writing reports, proposals, policies or marketing material.
• Explaining a coding concept without changing a repository.
• Broad research and synthesis.
• Producing a small standalone example.

Codex Governance

Repository agents should operate with least-privilege credentials, branch protection, test environments, secret controls, human review and audit logs. Generated code must be reviewed as contributed code rather than automatically trusted code.

Major Current Model Families

Specialist Model Categories

Embedding Models

Embedding models convert text or images into vector representations for semantic search, recommendations, clustering and retrieval-augmented generation. They do not normally generate prose and should not be compared directly with chat models.

Rerankers

Reranking models improve search quality by reordering retrieved documents before they are given to the generative model.

Vision and Document Models

These models extract information from PDFs, forms, tables, screenshots, charts and diagrams. For document automation, a specialist vision or OCR model may outperform a more expensive general model.

Speech Models

Speech systems cover transcription, translation, text-to-speech and real-time speech-to-speech conversation. Audio support should be evaluated separately from text reasoning.

Safety and Guardian Models

Guardian models classify risks, detect unsafe prompts, evaluate outputs or enforce organisation-specific criteria. They supplement rather than replace access control and human oversight.

What Each Model Type Generates Best

Context Windows

A large context window permits more material to be supplied, but it does not guarantee that every detail will be recalled or interpreted correctly. Very large prompts can increase cost, latency and confusion. Effective systems retrieve the most relevant material, remove duplication and preserve clear source boundaries.

Multimodal Capability

“Multimodal” may mean image input, PDF analysis, audio or video understanding, image generation, speech generation or computer use. A model that analyses images is not automatically an image-generation model. Each required modality should be tested separately.

Open Weight, Open Source and Proprietary

• Proprietary hosted model: accessed through an API or managed application.
• Open-weight model: downloadable weights under a licence, without necessarily exposing training data or the full development process.
• Open-source model: code, weights and licensing intended to meet open-source criteria.
• Private hosted model: deployed in the organisation's cloud, data centre or local device.

Self-hosting provides control but transfers responsibility for infrastructure, updates, security, scaling, monitoring and evaluation to the organisation.

Cost and Model Routing

A mature production system rarely sends every request to the most expensive model. A typical routing design sends simple extraction and classification to a small model, escalates difficult reasoning to a frontier model, routes repository work to Codex or another coding agent, and sends images or documents to a multimodal specialist.

Evaluation Framework

1. Create a test set containing real organisational work.
2. Define measurable acceptance criteria.
3. Test several candidate models with identical source material.
4. Measure hallucinations, omissions and tool-call failures.
5. Measure latency and total operational cost.
6. Record the amount of human correction required.
7. Repeat the evaluation after major model or prompt changes.

Security and Governance

• Apply least-privilege tool and data access.
• Separate trusted instructions from untrusted document content.
• Protect secrets and credentials.
• Log model, prompt, tool calls and final actions.
• Require human approval for consequential actions.
• Use independent validation in medical, legal, financial and safety-critical work.
• Track model versions, aliases and retirement notices.

Recommendations by User Type

IT Professionals

Use a balanced general model for documentation and troubleshooting, Codex for scripts and repositories, and a small model for ticket classification or routine extraction.

Software Developers

Use Codex, Claude Opus or Devstral for repository work. Retain a general frontier model for architecture, requirements, threat modelling and technical explanation.

Businesses

Prioritise retrieval, governance, predictable cost and integration. A smaller model grounded in company data can be more useful than a frontier model without access to current business information.

Content Creators

Evaluate tone, originality, long-form coherence and editing control. Balanced conversational models usually provide better value than specialist reasoning or coding models.

Private Deployment

Evaluate Llama, Mistral, Qwen, DeepSeek and Granite. Include the full cost of inference infrastructure, operations, model updates and security.

Model Selection Checklist

1. Define the required output.
2. Determine whether current external information is required.
3. Identify tool, retrieval, code-execution or repository requirements.
4. Set accuracy, latency and cost thresholds.
5. Determine privacy and deployment constraints.
6. Test multiple models using real examples.
7. Measure failure modes as well as successful demonstrations.
8. Use the smallest model that reliably meets the requirement.
9. Add escalation to a stronger model for difficult cases.
10. Monitor provider changes and model retirement.

Final Recommendation

There is no single best LLM. For difficult general work, begin with a frontier reasoning model. For everyday professional work, use a balanced model. For software-engineering execution, use Codex, Claude Opus or Devstral. For high-volume routine work, use a smaller model. For private deployment, evaluate open-weight families such as Llama, Mistral, Qwen, DeepSeek and Granite.

The strongest production architecture is often a combination of models, retrieval, tools, validation and human approval rather than dependence on one model for every task.

Official Sources

• OpenAI models
• GPT-5.3-Codex
• OpenAI Codex application
• Anthropic Claude models
• Google Gemini models
• Mistral models
• Amazon Nova models
• IBM Granite 4.1

Version 3 comprehensive edition. Last reviewed: 16 July 2026.