AI models in October 2026: OpenAI, Gemini, Claude, Meta, Grok, Mistral and DeepSeek

Which versions should you investigate?

A product name such as ChatGPT does not identify a fixed model. Applications can change their engines, tools and limits. This table covers documented model families and access, rather than promising identical features on every subscription.

Sources: OpenAI, Google, Anthropic, Meta Wonder, Llama, Grok, Mistral and DeepSeek.

Recent changes that affect comparisons

Meta coverage now needs to distinguish Llama from the Muse families. Do not automatically put every Meta model in an open weights comparison. For Mistral Large 4, the October announcement describes weights as forthcoming: a family label alone does not establish that a download is available now.

DeepSeek also illustrates why an accepted model name is not enough to reproduce a test. Some legacy names can route to a newer model. Keep the requested identifier, the resolved version when supplied and the test date. Consult its model listing documentation rather than inferring capabilities from an old tutorial.

Modalities need separate evaluations. A text reasoning score says nothing directly about character continuity in a film or pronunciation of a French name. Use our video comparison and audio evaluation guide for those tasks.

Other model families to track

Include Qwen3.8-Max, GLM-5.3 and Kimi K3 in ongoing research too. Their documentation expands the candidate set; it is not a LearnIA comparison test. Check actual access, licences and endpoint capabilities before adding them to your protocol.

Evaluate document synthesis with a deliberate ambiguity

Prepare three fictional files: a proposal, a revision and meeting notes that leave one issue unresolved. Ask for a decision brief with a source for each factual claim. Introduce two possible dates without a final decision. An acceptable response must preserve that uncertainty.

Score factual accuracy before style. Count correct facts, omissions and invented claims. Have a reviewer open the references instead of judging how convincing the prose sounds. A polished brief that makes an unsupported decision fails this task.

Before using real files, examine your organisation's rules and the service's processing terms. The hosting provider, model and application collecting the files are distinct parts of the system.

Evaluate a coding agent as a complete system

An agent combines a model, tools, instructions and an environment. Start each candidate from the same repository and a bounded ticket, such as repairing a date filter or adding an accessible empty state. Define expected behaviour and checks in advance.

Keep logs, changes and evidence of the checks actually run. Record regressions, unnecessary dependencies and human repair time. One successful ticket does not demonstrate the ability to maintain an entire production system. An installation failure also does not directly measure model reasoning.

For a narrow classification or routing task, include a deterministic rule and a decision model such as Jev in the candidate set. A general generative model is not required for every software decision.

Compare cost per accepted result

Include input and output charges, tools, infrastructure, rejected attempts and review time. A price per million tokens is not directly comparable with a monthly subscription or a price per task.

Choose a business unit: an accepted product record, a verified report or a resolved ticket without regression. A cheaper call can require more correction; a more expensive model can be unnecessary for short extraction. The useful figure is the cost of producing a result that passes your criteria.

Help me compare models for a specific task. Define fictional test inputs, an expected result, five verifiable criteria and one critical failure. Create a table for model version, tools, reasoning settings, cost, total duration and corrections. Do not claim to have run the trials or choose a winner before results exist.

Download the selection worksheet. Keep a small set of difficult cases and rerun them after material model or product changes. A revised article date is not evidence of a new evaluation. For office workflows, the assistant comparison protocol supplies another practical test.

Frequently asked questions

Which model is best in October 2026? Specify the task, budget and constraints first. Public rankings inform a shortlist; they do not validate your workflow.

Should I always choose the latest release? Only if it improves the result under your criteria. Preview status, pricing and missing tools can change the decision.