FajarLast reviewed: September 2026. Model lineups change every few months, so check the vendor pages linked below and re-run your own tests before you commit.
“Which AI is best: GPT, Claude or Gemini?” is a question we hear often, and it is the wrong one. The three vendors’ top models are close enough on general capability that the name on the box is rarely what decides whether a product works. The deciding properties are duller: how latency behaves under your prompts, what the cost looks like under your traffic, how reliably the model calls tools in your agent loop, and whether your compliance team will sign off. Public leaderboards measure none of those for your workload, so this post ranks nobody. It describes how to choose.
How Each Vendor Structures Its Lineup
Model names and versions change every few months, so this post does not list them. What stays stable is the shape of each lineup, and the vendor’s own models page is the only reliable source for what is current.
| Vendor | Model Family | How the Lineup Is Tiered | Current Models |
|---|---|---|---|
| OpenAI | GPT | A flagship model, plus smaller and cheaper variants | OpenAI models |
| Anthropic | Claude | A top tier for demanding reasoning, a mid tier for most work, and a small fast tier | Claude models overview |
| Gemini | Pro models, plus Flash and Flash-Lite models for speed and cost | Gemini models |
Two notes on reading those pages. Each vendor says which model it recommends as a starting point, and that is usually a mid-tier model and not the flagship. Each vendor also marks some models as preview or experimental. Preview models can change or be withdrawn at short notice, which matters if you are about to build a product on one.
Every vendor also publishes retirement dates for older models. Read the deprecation page before you pin a version, because a model you choose today may have an end date inside your roadmap.
Choose a Tier per Task, Not a Brand per Company
Each vendor sells a ladder: a top tier for hard reasoning, a fast middle tier for everyday work, and a small, cheap tier for simple tasks at high volume. The gap between tiers inside one vendor is usually wider than the gap between vendors at the same tier, in both price and speed. Anthropic’s published list prices, for instance, differ by a factor of ten between its top and smallest models.
A sound design therefore uses two or three models, often from different vendors: a small model for classification, routing and extraction, a mid-tier model for the main user-facing generation, and a top-tier model for the rare step that is hard. Sending every request to the flagship overpays for most calls. Forcing everything through the smallest model is how an agent comes apart on step four.
Latency: The Constraint Teams Discover Late
Latency has two parts that get mixed up: time to first token (how long the user looks at nothing) and throughput (how fast the rest streams). In chat, time to first token dominates perceived speed, and people forgive a slow tail if words start appearing quickly. In agent workloads nothing streams to a person and each output feeds the next step, so total completion time is what counts, and it compounds. Five chained steps at five seconds each make a 25-second feature, which is a different product from a 4-second one.
- For conversational features, measure time to first token at the 95th percentile with your real prompt sizes. Long system prompts and large retrieved contexts raise it on every vendor.
- For agent features, count the steps before you pick the model. A mid-tier model that finishes each step in a second or two often makes a better product than a stronger model that takes twice as long.
- Reasoning or “thinking” modes trade latency for quality on all three families. They suit background jobs and are poor defaults for interactive paths. Turn them on by task type.
Cost: Model Your Traffic, Not the Price Page
Per-token prices change too often to print. The cost structure is stable, and that is where the decisions are.
First, know whether you are input-heavy or output-heavy. Retrieval and document workloads send large inputs and receive short answers, so input pricing and caching dominate. Content generation is the reverse. The cheapest vendor changes depending on which side your tokens sit.
Second, design for prompt caching. The major vendors discount repeated prompt prefixes. Anthropic’s pricing page, for example, lists cache reads at a fraction of the base input price and batch requests at half price. Check the equivalent pages at OpenAI and Google for their current terms. To benefit, put static instructions and examples first and volatile content last. That is an architecture decision, and it can matter more than the choice of vendor.
Third, use batch processing for anything that can wait. Nightly enrichment, summarization queues and evaluation runs do not need an answer in seconds.
Finally, project the cost at 100 times the pilot. A feature that costs $30 a month with ten testers costs $3,000 a month at modest success. Working out cost per user action before choosing a tier ends more flagship-model plans than any benchmark does.
Context Windows: Advertised vs Usable
Context windows of about a million tokens are now listed on the current OpenAI and Anthropic models, and Google has offered windows of that size for some time. Two cautions apply.
Advertised context is not the same as usable context. Research such as “Lost in the Middle” (Liu et al., 2023) found that models retrieved information best from the start and end of a long context and worse from the middle. Newer models have improved, but the way to find out how yours behaves is to test it with your own documents. Pasting everything in remains a weaker design than targeted retrieval for most workloads, and our post on what breaks in production RAG covers the retrieval side.
Large contexts are also billed as large inputs on every call. A long-context shortcut that avoids building retrieval can cost more each month than the retrieval build would have.
Long context does earn its price in deep analysis of a single artifact (a contract, a codebase, a transcript) where the whole thing matters at once.
Tool Use: The Differentiator for Agent Products
If your product is an agent that calls tools, chains steps, edits files and hits APIs, test tool-calling reliability hardest, because small differences per step compound. Suppose one model follows your tool schema correctly 98% of the time and another 93%. Across a ten-step chain, the first completes cleanly about 82% of the time (0.98 multiplied by itself ten times) and the second about 48% (0.93 by the same sum). The numbers are illustrative, but the arithmetic is why a gap that looks small on paper separates a working feature from a stream of support tickets.
Tool calling also varies between versions within a family, shifts with each release, and depends on your schemas: deeply nested parameters, optional fields and parallel calls are where models diverge. Reputation is a poor guide here. Build an evaluation set of 50 to 100 cases from your real tool schemas and transcripts, and run every candidate model against it. It takes a few days and turns a debate into a spreadsheet. If you do one thing from this post, do this.
Structured Output, Instruction Following and Voice
Three smaller criteria regularly settle ties. All three vendors offer schema-constrained JSON output, so if code consumes the model’s output, use that feature and stop parsing prose. Models differ in how well they hold many constraints deep into a long conversation, and only your own evaluation set will show which one holds yours. And the families have different default writing registers, which matters for customer-facing copy. That last one is a matter of taste plus testing, so put sample outputs in front of the people who own your brand voice.
Compliance and Deployment: Sometimes the Whole Decision
For regulated work, capability is irrelevant until four things clear: data residency (where inference runs and what the contract guarantees), training-data terms (API traffic excluded from training, in writing), certifications and a Business Associate Agreement where health data is involved, and availability through your cloud provider. The last point is underrated. All three families can be reached through major cloud platforms, so if your infrastructure and agreements already sit with one cloud, consuming models through it can inherit your existing contracts and billing. For an enterprise, that is often a legitimate way to settle the vendor question before any capability test.
On our side, we use the vendors’ API tiers whose terms exclude training on your data, and we confirm the current terms with you before a build starts.
Build for Switching, Because You Will Switch
Treat model choice as a configuration value. Route every LLM call through one internal gateway module. Keep prompts in versioned files. Keep tool schemas vendor-neutral and translate them at the edge. Maintain the evaluation set, so that when a promising model ships, assessing it takes an afternoon and not a migration project. The reasons to switch tend to be the dull ones above: latency, cost structure, tool reliability or compliance. The same discipline applies to workflow tools, as our comparison of n8n, Zapier and custom code explains.
The Short Version
Pick per task, not per company. Test time to first token under your real prompts. Model cost at 100 times pilot traffic, with caching designed in from the start. If you are building agents, evaluate tool calling on your own schemas before committing. Let compliance veto early. Re-test when a vendor ships a new version, because the lineup in the table above will not survive the year unchanged.
If you would like help running that evaluation for your product, or building the gateway layer that keeps switching cheap, that is the work our AI and automation team does. Get a quote and bring your use case.
