When a CAIO asks which AI model their agency should use, they're usually asking three different questions at once without knowing it. The answer depends on whether the task is simple or complex, whether the input is text or something else, and how much they're willing to pay per inference. Base and chat models, reasoning models, and multimodal models are the three categories that organize the answer. You'll encounter all three in enterprise purchasing conversations — sometimes in the same meeting, sometimes in the same sentence — and the precision of your language is what separates a fluent response from a fumble.
Base and Chat Models
What it is. A language model trained to predict and generate text, served with a conversational interface that accepts a prompt and returns a response.
What it does. Handles the majority of enterprise language tasks: drafting, summarization, classification, question-answering over documents, translation, code generation, and conversational interfaces. Response latency is low, typically under two seconds for most prompts, and cost per inference is the lowest of the three categories. These are the workhorses. When an agency wants to stand up a document Q&A tool for HR policy, or a drafting assistant for contracting officers, this is the class they're buying.
Who's behind it / where it comes from. Every major AI provider ships at least one model in this class. OpenAI's GPT-4o, Anthropic's Claude Sonnet series, Google's Gemini Flash, and Meta's Llama family (available for self-hosted deployment) all sit here. The market is crowded and moving fast; what distinguishes providers at this tier is context window size, pricing, and the quality of their API documentation and rate-limit tiers, not dramatic capability differences.
What makes it distinct. Speed and cost. A chat model answers in one pass: it reads the prompt, generates tokens left to right, and stops. No deliberation step. For tasks where the right answer is retrievable or composable from a single pass through the context, this is sufficient and significantly cheaper than the alternatives. Single-pass generation handles retrieval and composition well. Tasks that require checking intermediate conclusions before committing to a final answer are where it runs out of runway — which is the gap reasoning models fill.
Reasoning Models
What it is. A language model that generates an extended internal reasoning trace before producing its final answer, spending additional tokens deliberating before committing to a response.
What it does. Handles tasks where the answer isn't directly retrievable but must be derived through multiple inferential steps: complex legal analysis, multi-constraint policy compliance checks, mathematical reasoning, code debugging across large codebases, and any task where a wrong intermediate conclusion would corrupt the final output. The model produces a chain-of-thought, a visible (or semi-visible) scratchpad of reasoning steps, before surfacing its answer. This deliberation costs tokens, which costs money and adds latency. Response times of 15–60 seconds are common for hard problems. Cost per task can run 5–15x higher than an equivalent chat model request.
Who's behind it / where it comes from. OpenAI's o-series (o3, o4-mini) established the commercial category. DeepSeek-R1, released by the Chinese AI lab DeepSeek in early 2025, demonstrated that the approach was reproducible at lower training cost and is available under an open-weights license, meaning agencies with the infrastructure can self-host it. Google's Gemini 2.0 Flash Thinking and Anthropic's Claude with extended thinking mode both compete here. The category is defined by the deliberation mechanism, not by any single vendor.
What makes it distinct. The token budget for thinking is separate from the token budget for the answer. When you call a reasoning model, you're paying for the scratchpad, not just the output. Some providers expose the reasoning trace; others surface only the final answer. Federal auditability requirements turn on this distinction: an agency that needs to explain why an AI system reached a conclusion has a different procurement requirement than one that only needs the conclusion itself. Whether the reasoning trace is accessible, and whether it's retained, is a question worth surfacing early.
Multimodal Models
What it is. A language model that accepts inputs beyond text — images, audio, video, or combinations — and generates responses that may include non-text outputs depending on the model.
What it does. Processes and reasons over mixed-media inputs. An agency submitting a scanned form gets a structured data extraction. A field inspector uploading a site photograph gets a condition assessment. A meeting recording gets a transcript and summary. The underlying capability is that the model has been trained to represent images, audio frames, or video segments in the same embedding space as text tokens, so it can reason across modalities in a single inference pass rather than requiring a separate OCR step or audio transcription pipeline upstream.
Who's behind it / where it comes from. GPT-4o (OpenAI) accepts text, image, and audio inputs and is the most widely deployed multimodal model in enterprise contexts. Gemini 1.5 Pro and Gemini 2.0 (Google) support text, image, audio, and video with notably large context windows — Gemini 1.5 Pro's 1-million-token context window can ingest roughly an hour of video. Anthropic's Claude 3.5 Sonnet accepts text and images. The capability axes vary by provider: not every multimodal model handles every modality, and audio and video support remain less standardized than image support.
What makes it distinct. The input surface, not the output. Most multimodal models still produce text as their primary output; the expansion is on the input side. Agencies scope use cases around this accordingly. A workflow that previously required a human to read a document and type its contents into a system can be replaced by a multimodal model that reads the document directly. Getting images, audio, or video to the API then becomes the integration question — a data plumbing problem that belongs in the architecture conversation before the model conversation.
Comparison Strategy
Scenario mapping. The most useful structure for AEs isn't a feature matrix — it's a task-type map. Buyers describe a workflow and ask if AI can help. The model class question is implicit in the workflow description. The comparison below organizes model class selection by task type, input type, and cost tolerance. Each model class appears in every dimension where it's relevant.
Task complexity is the primary selector.
For tasks where the answer is directly in the source material — summarize this document, classify this ticket, draft a response to this email — a chat model is appropriate. The task is single-pass retrievable. Adding a reasoning model here doesn't improve quality meaningfully; it adds cost and latency.
Tasks that require deriving an answer through multiple steps — evaluate whether this contract clause conflicts with FAR Part 15, identify the logical flaw in this proposed acquisition strategy, debug why this policy configuration produces an unexpected access result — call for a reasoning model. The deliberation step is doing real work. A chat model attempting the same task will often produce a confident-sounding answer that skips the intermediate checks, which is worse than a slower correct answer.
When the input isn't text — process this stack of scanned SF-86 forms, analyze this satellite imagery for site assessment, transcribe and summarize this recorded deposition — a multimodal model is appropriate, regardless of task complexity. If the input is an image, you need a model that can read images. That's a capability gate.
Cost tolerance is the secondary selector.
Chat models are appropriate for high-volume, lower-stakes inference: the thousands of daily document queries in a large agency, the automated classification of incoming correspondence, the first-pass summarization before human review. Reasoning models are appropriate for lower-volume, higher-stakes inference: the compliance analysis that a contracting officer will act on, the policy interpretation that will be cited in a decision memo. Multimodal models sit between the two on cost, depending on the modality and the provider.
The combination case. Agentic workflows — where an AI system executes a multi-step task autonomously — often chain model classes. A multimodal model extracts structured data from a scanned document; a chat model drafts a response; a reasoning model validates that the response is compliant with applicable policy. Buyers who are building pipelines rather than point tools will encounter this architecture. In those cases, the answer is usually all three, sequenced by task type.
Field Language Guide
| Don't say | Do say | Why it matters |
|---|---|---|
| "Which AI model is best?" | "What's the task type — single-step retrieval, multi-step reasoning, or mixed-media input?" | Reframes the question to one you can actually answer; prevents you from recommending a model class before you know the use case |
| "The smarter model" | "The reasoning model — it's appropriate when the task requires multi-step inference" | "Smarter" implies the chat model is deficient; reasoning models aren't smarter, they deliberate longer on problems that require it |
| "The cheaper model" | "A chat model — lower cost per inference, appropriate for high-volume or single-pass tasks" | Buyers hear "cheaper" as "worse"; the framing should be fit-for-purpose, not budget-constrained |
| "The expensive one" | "A reasoning model — the cost reflects the additional token budget for chain-of-thought deliberation" | Explains the cost structure rather than just acknowledging it; gives the buyer a reason, not an apology |
| "It can see images" | "It accepts image inputs — the model processes image and text in the same inference pass" | "See" is anthropomorphic and imprecise; "accepts image inputs" is the language a technical buyer will use and respect |
| "GPT" (as a generic term for any AI model) | The specific model class relevant to the use case | Buyers who know the difference will notice; buyers who don't will learn the wrong vocabulary from you |
| "It thinks longer" | "It runs extended chain-of-thought before returning an answer" | "Thinks" is anthropomorphic; "chain-of-thought" is the term in vendor documentation and procurement specs |
| "The vision model" | "A multimodal model with image input support" | "Vision" is a capability axis, not a model class; some multimodal models handle audio but not images, or video but not audio |
| "We can use AI to process those forms" | "A multimodal model can extract structured data from scanned documents in a single inference pass, without a separate OCR pipeline" | Specificity signals fluency; it also surfaces the integration question (how do scanned documents get to the API?) before the buyer discovers it themselves |
| "The reasoning model is overkill for that" | "A chat model is the right fit here — the task is single-pass retrievable and doesn't need the deliberation step" | "Overkill" sounds dismissive; "right fit" keeps the conversation on task type rather than capability hierarchy |
| "DeepSeek is the open-source option" | "DeepSeek-R1 is an open-weights reasoning model — agencies with the infrastructure can self-host it, which changes the procurement and data residency conversation" | The open-weights label matters less than the deployment model: hosted API vs. self-hosted carries direct ATO implications |
| "It handles audio too" | "This model accepts audio inputs — confirm with the agency whether audio data is within scope for the ATO before scoping that capability" | Multimodal capability expansion is also attack surface expansion; flagging the ATO question early prevents a late-stage surprise |
Okta Concept Mapping: Scope Selection
Choosing a model class maps loosely to selecting OAuth scopes for a client application. When you configure an OAuth client, you request the minimum scope the task requires — read-only access for a read-only operation, not admin scope because it would technically work. Choosing a reasoning model for a task that a chat model handles is the same structural error: technically functional, unnecessarily expensive, and harder to govern. The analogy holds for the selection logic. It breaks on the stakes: in OAuth, requesting excess scope is a security boundary violation. In model selection, it's a cost and latency inefficiency, consequential in a large deployment but not a trust architecture failure. A CAIO who asks "why are we using the expensive model for document summarization?" is asking a procurement question, not a security question. The answer is the same either way — minimum sufficient capability for the task — but the urgency is different.