Reviewed 11 October 2026
The older Claude 4.5, GPT-5 and Gemini 2 comparison is preserved in the URL, but it is not a current performance ranking. Official catalogs have moved on. The practical decision is to shortlist exact available models for the task and compare accepted results under the same operating constraints.
Direct answer: choose a model family through a task-specific evaluation, not a universal winner for writing, coding or reasoning. Use official catalogs for availability and features, and your own retained tests for quality and economics.
What the current catalogs show
The OpenAI catalog highlights GPT-6 Astra, GPT-6.1 Sol and GPT-6 Luna. The Claude overview includes Fable 5.1, Opus 5.5, Sonnet 5.5 and Haiku 5.5. The Gemini catalog includes Gemini 3.8 Flash and other modality-specific endpoints, while Gemini 3.1 Pro is labelled preview. These are catalog facts checked 11 October 2026, not independent quality measurements.
Select the exact endpoint and platform. A chat product may expose different tools, limits and defaults from its API. A preview endpoint may be inappropriate for a workload that requires a stable lifecycle. A model with audio output belongs to a different task comparison from a text-only extraction endpoint.
Do not transplant launch-era prices into a current table. Claude pricing and Gemini pricing distinguish model, processing mode and billing details. Output, cache, tool and multimodal charges can alter the total. A quoted rate needs a date and a defined surface.
The earlier article’s claimed identical-prompt tests and business scenarios are not retained as research because no inspectable run artifacts were supplied. This guide supplies an evaluation method and explicit decision boundaries instead of manufacturing a replacement leaderboard.
Build the shortlist from the work
| Task | Shortlist must support | Judge the final result |
|---|---|---|
| Document extraction | Input format and structured output | Exact fields, source trace and abstention |
| Coding | Repository context and permitted tools | Behaviour, tests, security and review effort |
| Business writing | Source grounding and style control | Accuracy, audience fit and editorial acceptance |
| Support assistant | Retrieval and bounded tool use | Policy-correct verified resolution |
| Real time voice | Required audio/latency interface | Turn handling, understanding and safe escalation |
| Research | Current retrieval with sources | Claim support, coverage and uncertainty |
A strong writing result does not establish safe tool execution. A high code benchmark does not establish whether a model understands your legacy integrations. A large context limit does not guarantee the answer is grounded in the correct passage. Match the test to the business failure that matters.
Begin with the simplest supported model configuration that might satisfy the task. Escalate effort or model capability only when accepted quality falls short. This is a measured operating policy, not a guarantee that a cheaper family will always be sufficient.
A comparison protocol you can inspect
Create a fixed set of representative tasks with an agreed rubric. Include edge cases and inputs that require an “unknown” or escalation. Keep the evaluation set separate from prompt tuning examples. Store raw inputs under appropriate access control and retain the expected outcomes.
For each candidate, record the exact model, provider, platform, settings and tools. Use supported parameters rather than forcing an identical configuration that some models reject. Specify timeout and retry rules. Record both the model output and any authoritative state produced by actions.
Have reviewers score blind when practical. Separate factual correctness, format validity, instruction adherence and risk violations. Ask them to explain the important errors with source references. A single overall score can hide a model that writes well but fabricates a critical number.
Measure elapsed time and cost at representative concurrency. Include the time spent reviewing or repairing outputs. Repeatedly calling a model until it produces a desired answer changes the cost boundary and should be reported. Retain failed calls so reliability problems do not disappear from the result.
Do not claim significance from a tiny sample. Report counts and uncertainty, and explain what the test covers. The result should tell a reader why a model fits a specific task, not imply that it wins every task for every organization.
When multi-model routing is worth the complexity
Routing can be useful when tasks have genuinely different requirements. A bounded classification may need low latency while a complex review warrants more effort. A voice interface may require a specialized audio endpoint. Start with explicit routes based on task type or verified failure conditions, rather than a model’s uncalibrated claim that it is confident.
A routed system introduces more model versions, prompts, billing surfaces and fallback behaviour. Each route needs acceptance evidence. Preserve the permission and data boundary across fallbacks. An application should not send confidential material to a second provider merely because the preferred endpoint timed out.
For illustrative math, assume 8,000 simple tasks cost $0.002 each and 2,000 complex tasks $0.02 each. The token/service total is $16+$40=$56. Add routing, review and operating expenses before comparing it with a single model. If the simple route creates extra errors, the apparent saving may disappear in review cost.
Keep a small replay set for every route. Re-evaluate after catalog, pricing or prompt changes. A fallback that has not been tested is a second unverified system, not a reliability improvement. Use the accepted-task cost example for a more detailed economic boundary.
Make the buying decision reviewable
Write a one-page decision with the task, required interface, exact model ID, acceptance result, total cost and known limitations. Include lifecycle risk and an exit route. If two candidates perform similarly, prefer the configuration the team can operate and inspect consistently.
For consequential actions, quality evidence must include authorization and postcondition checks. For writing, require factual sources and human editorial approval. For research, require linked supporting evidence and explicit uncertainty. These responsibilities remain with the workflow owner.
Read the current model decision guide for the broader market snapshot. The useful next step here is to select one concrete task and retain its evaluation artifacts. A catalog can tell you what exists; only a scoped acceptance test can tell you whether it fits your work.
Frequently asked questions
Does this page rank the old Claude 4.5, GPT-5 and Gemini 2 models as current?
No. The URL retains historical context while the body uses current official catalogs and a task-based evaluation method.
Which family is best for every business?
There is no verified universal winner. Interface, task quality, risk, latency and operating cost determine the decision.
Why not compare only token prices?
Tokenization, output length, retries, tools, review and acceptance affect the complete cost.
Is multi-model routing automatically cheaper?
No. Routing and fallback overhead need to be measured, and every route needs its own quality and permission checks.
Sources checked 11 October 2026
Research Path