Buying the biggest AI model for every task is like hiring your most expensive specialist to rename files. Choosing the cheapest model for every task creates a different bill: corrections, retries and work your team cannot use.
The useful question is: which model completes this job reliably at an acceptable cost? That depends on the job, the tools around it and how you check the result.
This guide covers the current general-purpose OpenAI and Anthropic lineup checked on October 3, 2026. The model specifications and API prices come from official documentation. The starting recommendations are our editorial judgment, not the result of a private head-to-head benchmark.
The short answer
For a mixed business workload, trial GPT-6.1 Sol and Claude Sonnet 5.5 as practical starting points. Trial GPT-6 Astra or Claude Opus 5.5 when the task is harder and your results justify the extra cost. Within Anthropic's lineup, test Opus at higher effort before adding Fable 5.1 for jobs that still fall short. For high-volume, well-defined tasks, test GPT-6 Luna or Claude Haiku 4.5 against a quality threshold before using them at scale.
That is a testing strategy, not a universal ranking. OpenAI positions Astra as its most capable model and Sol as a lower-cost option for complex work. Anthropic recommends Opus 5.5 for most workloads when you are unsure, with Fable 5.1 for demanding reasoning. Our recommendation to trial Sonnet first reflects its lower token price: it costs half as much as Opus at the listed Standard rates. Your business may value turnaround, writing style or integration more than the vendor's default recommendation. OpenAI's current guide and Anthropic's model overview explain their positioning.
First, separate the model from the product
ChatGPT, Codex and Claude Code are products that put models to work with interfaces and tools. Dots and Muse are agents that coordinate responsibilities. An API model is the intelligence your own application calls. You are making different decisions when you pick an app subscription, choose a model in a coding tool or design a production workflow.
A model's ability to understand an image does not automatically mean the surrounding app can read your whole project folder. Its context window does not automatically grant access to your CRM. Tool permissions, retrieval, browser support, integrations and the product's review flow affect what actually gets done.
If you want an ongoing assistant, evaluate the product and the model together. If you are building a customer-facing tool, evaluate the API, your source data and your application's failure handling together. Our guides to OpenAI dots and Meta Muse cover the agent side.
OpenAI: Astra, Sol and Luna
GPT-6 Astra: start here for the hardest jobs
OpenAI describes GPT-6 Astra as its most capable model for complex reasoning, coding, computer use, research and document creation. Its model page lists a 1,050,000-token context window, 128,000 maximum output tokens, and Standard text-token rates of US$10 per million input tokens and US$50 per million output tokens. GPT-6 Astra specifications.
Put Astra on your trial list when a mistake is expensive to unwind or the work requires several dependent decisions. Examples include untangling conflicting requirements, planning a substantial software change or analyzing a messy set of business documents. It is still your job to check the result. A higher-capability model is not an approval process.
The decision to use it should come from the failures you observe elsewhere. If a less expensive model produces the same usable result with the same review effort, the premium has not bought you anything on that task.
GPT-6.1 Sol: a sensible everyday candidate for complex work
OpenAI positions GPT-6.1 Sol as near-Astra performance at a lower cost for complex coding, computer use and professional work. Its listed context and output limits match Astra's. Standard text rates are US$2 input and US$10 output per million tokens. For developers, the model supports reasoning efforts from low through max; none and minimal are unsupported. Tool calling uses the Responses API, while Chat Completions supports requests without tools. Above 272,000 input tokens, its full-request input and output rates increase to US$4 and US$15 per million tokens. GPT-6.1 Sol specifications.
This makes Sol a useful first candidate for a mixed workflow: drafting from approved sources, analyzing operational information and helping build or maintain software. Compare its output with Astra on the tasks that cause rework. Do not assume the newer name means every task improves without a test.
GPT-6 Luna: test it on bounded, repeated work
OpenAI positions GPT-6 Luna as its most efficient option for focused, high-volume tasks. The model page lists a 1,050,000-token context window, 128,000 maximum output tokens, and Standard rates of US$0.10 input and US$0.50 output per million tokens. GPT-6 Luna specifications.
Try it for jobs with a clear expected answer: classifying inquiries, extracting fields into a schema or producing a first pass that another step checks. Include awkward examples in the test. A model that extracts ten easy invoices correctly may still confuse a credit note with an invoice or miss a changed currency.
Anthropic: Fable, Opus, Sonnet and Haiku
Claude Opus 5.5: long-running work with substantial judgment
Anthropic released Opus 5.5 on September 22, 2026 and positions it for long-running agentic coding and knowledge work. Its API model ID is claude-opus-5-5. The model has a 1-million-token context window, 128,000 maximum output tokens, and Standard rates of US$4 input and US$20 output per million tokens. Adaptive thinking is always on. Claude Opus 5.5 specifications.
Consider it for a difficult refactor, a document synthesis with competing sources or an independent review of a consequential deliverable. In a trial, look for what happens after the first complication: does it preserve the brief, identify uncertainty and complete the job? A beautiful first answer does not tell you whether it can sustain a longer assignment.
Claude Sonnet 5.5: speed and capability for everyday work
Sonnet 5.5 was released on September 28, 2026. Anthropic positions it as the best combination of speed and intelligence. Its model ID is claude-sonnet-5-5, with a 1-million-token context window, 128,000 maximum output tokens and Standard rates of US$2 input and US$10 output per million tokens. Claude Sonnet 5.5 specifications.
Trial it alongside Sol for regular content, analysis and development work. Judge the deliverable against your requirements rather than deciding that one vendor must be better at writing and the other at coding. Voice, completeness and willingness to ask useful questions can change with your prompt and source material.
Claude Fable 5.1: include it in demanding-work evaluations
Fable 5.1 was released on September 1, 2026. Anthropic positions it for demanding reasoning and long-horizon agentic work. It has a 1-million-token context window, 128,000 maximum output tokens, and Standard rates of US$10 input and US$50 output per million tokens. Claude Fable 5.1 specifications.
Fable belongs in the comparison when a difficult task still falls short after you improve the instructions, tools and source material and test Opus 5.5 at higher effort. Treat it as a premium candidate whose value must appear in the result. The relevant question is whether it reduces mistakes or review time enough to offset the cost.
Claude Haiku 4.5: a lower-cost option in Anthropic's current lineup
Haiku 4.5 remains in Anthropic's current model overview. The listed rates are US$1 input and US$5 output per million tokens, with a 200,000-token context window and 64,000 maximum output tokens. Anthropic describes its latency as the fastest in that lineup. Current Claude model comparison.
Test it for focused, repeated processing when your application already uses Anthropic and you want to reduce the cost of routine steps. Its smaller context window may matter if your input is large. A retrieval step that supplies the right evidence can be more useful than paying to send every document on every request.
What the API prices mean in a real task
The rates above are US dollars per million text tokens at Standard rates. They are API prices, not ChatGPT or Claude subscription prices. Cache reads and writes, long-context premiums, reasoning usage, tool calls, processing tiers and regional processing can change the bill. Check the model's current pricing page before budgeting a production workload.
Consider a deliberately simple example: one uncached request with 10,000 input tokens and 2,000 billed output tokens, below long-context thresholds, with no tool fees or other charges. At the listed rates, text-token cost is US$0.002 for Luna, US$0.02 for Haiku, US$0.04 for Sol or Sonnet, US$0.08 for Opus, and US$0.20 for Astra or Fable.
That arithmetic does not predict your actual bill. Reasoning and tool use can add work, and a second attempt changes the total. Track the actual usage fields and total charges from a representative pilot instead of multiplying the word count of the visible answer.
The cheaper request can also produce the more expensive finished job. If it needs repeated attempts and substantial human correction, compare cost per usable deliverable. Include your team's review time. That is the number that matters to a business.
Choose by workload, then test the recommendation
Customer replies and content
Trial Sol and Sonnet with the same brief, source documents and brand examples. Include a question the source material cannot answer. The model should identify the gap rather than invent a policy. Score factual accuracy first, then voice and usefulness. A confident invented refund promise is a bigger failure than a dull opening sentence.
Coding and product changes
Trial the model inside the actual tool you intend to use. Require the same repository context and a meaningful validation step. Evaluate whether the change solves the problem, preserves existing behavior and stays within scope. Consider Astra, Opus or Fable when the task crosses several systems or needs substantial judgment.
Extraction and classification at volume
Start with Luna or Haiku as candidates and define a pass threshold. Use difficult inputs: incomplete records, duplicate names, inconsistent dates and documents that should be rejected. Route uncertain results to a review queue. You save little by processing bad records quickly if someone then has to repair the database.
Research and decisions
Give each candidate the same source-access rules and deliverable. Ask it to distinguish facts, inference and missing information. Compare dated sources and the reasoning behind the recommendation. A model's built-in knowledge cutoff is not a live source; research about changing products needs current retrieval.
Run a small evaluation your team can maintain
Collect about twenty real examples of the work, with permission to use the data. Include routine jobs, awkward edge cases and at least one task where the right answer is to ask for more information. Redact sensitive information when it is unnecessary.
Write the pass condition before seeing the model's answer. For a lead classifier, that might be the right category and an explicit reason. For a proposal, it might be correct scope, no invented claims and a usable editable file. For code, use the actual behavior and appropriate checks.
Keep the inputs and tool access consistent across candidates. Record the exact model ID, effort setting, prompt version, latency, retries and billed usage. Have a person review the outputs without seeing the model name where practical. Then choose the least expensive setup that consistently passes your requirements.
Do not turn a small pilot into a sweeping public benchmark. Twenty examples help you choose a setup for your business. They do not establish that one model is best at everything. Re-run the useful cases when you change models, prompts or integrations.
Use a second model to challenge a first draft
Independent review is useful when it has a specific job. Ask the reviewer to identify unsupported claims, missing requirements, misleading comparisons and the most damaging plausible failure. Give it the original brief and source material, not only the polished draft.
Review this deliverable adversarially. Find claims the sources do not support, assumptions presented as facts, missing constraints and recommendations that would fail in practice. Rank the issues by consequence. Quote the exact sentence, explain the problem and propose a precise correction. Do not rewrite it merely to match your own style.
A second model's agreement is not proof. Two models can share the same blind spot. Resolve disputed facts against the actual source, and verify code, calculations or external actions with evidence that does not depend on either model's confidence.
Common model-selection questions
Which model is best for a small business?
Start with the work you need done. Sol and Sonnet are useful everyday trial candidates; higher-capability models belong in the test when complexity demands it. For an app subscription, compare the available tools and plan limits as well as the model.
Should I always choose the latest model?
No. A release can change cost, effort behavior or API compatibility. Keep your existing evaluation cases and test the new model before changing a working process. A model upgrade is useful when it improves your results or economics.
Can I use a cheap model first and escalate difficult cases?
Yes, as a workflow design. Define which cases need escalation using validation rules, missing evidence or the cost of an error. Do not rely only on the model's self-reported confidence. Check that your routing actually catches failures.
If you want to choose models for a real process, our AI integration, custom AI tools and AI training services help you work through the data, delivery and review requirements. Bring one recurring job and a few representative inputs to the conversation.
Specifications and prices checked October 3, 2026. Vendor positioning is attributed above. Recommendations and example workflows are editorial guidance; this article does not claim an independent benchmark winner. Header image: AI-generated editorial photography, not a documented product test.
