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Neither OpenAI nor Anthropic is the defensible default for every workload. Choose by running both providers’ viable models on the same representative tasks, then comparing successful-task quality, latency and total cost under your real constraints. Model descriptions and published prices can narrow the candidates; they cannot tell you which provider will do your work better.
Start with the work, not the provider
Write down what the system must do before comparing model names. A useful test case includes the prompt, typical input, expected output, tools the model may call, and what counts as failure. Include ordinary requests as well as difficult or unusual cases that matter in production.
Score task completion and correctness—not just fluency. Depending on the job, assess factual accuracy, instruction following, required-format compliance, tool-use success and failure rate. Set a minimum acceptable quality level before testing; otherwise, a slightly cheaper or faster result can look attractive even when it is not usable.
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Which models should you put on the shortlist?
Use each provider’s model descriptions to form a shortlist, not to declare a winner. OpenAI’s catalog positions GPT-6 Astra for the most demanding reasoning and coding, GPT-6.1 Sol as a balance of intelligence and cost, and GPT-6 Luna for cost-sensitive, high-volume work. OpenAI says its latest models support text and image input, text output, multilingual capabilities and vision, and are available through the Responses API and client SDKs. Verify that the particular model and interface support the features your application needs in the model catalog.
Anthropic describes Claude Opus 5.5 as its strongest Opus model for coding, agents and knowledge work. Its Haiku 5.5 announcement positions Haiku for scoped, high-volume tasks such as summarization, compaction and subagent work, while saying Sonnet 5.5 and Opus 5.5 remain better choices for complex agentic coding. These are provider descriptions, not independent head-to-head results. Confirm current model names, availability and feature support on the Opus page and the Haiku 5.5 announcement.
Compare published API prices carefully
The following are provider-listed standard token prices in materials checked in 2026, expressed per million tokens. They are a price snapshot, not a performance ranking or a complete estimate of what an application will spend.
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| Model | Provider-stated positioning | Input price | Output price | Important qualification |
|---|---|---|---|---|
| GPT-6 Astra | Most demanding reasoning and coding | $10 | $50 | Standard API text pricing; see OpenAI’s model catalog. |
| GPT-6.1 Sol | Balance of intelligence and cost | $2 | $10 | Standard API text pricing; see OpenAI’s model catalog. |
| GPT-6 Luna | Cost-sensitive, high-volume work | $0.10 | $0.50 | Standard API text pricing; see OpenAI’s model catalog. |
| Claude Opus 5.5 | Coding, agents and knowledge work | $4 | $20 | The page also lists cache reads at $0.20 per million tokens; see the Opus page. |
| Claude Haiku 5.5 | Scoped, high-volume tasks | $0.10 | $0.50 | For prompts up to 100,000 tokens; above that threshold, the announcement lists $0.50 input and $2.50 output per million tokens. See the announcement. |
Token rates alone do not establish which option costs less for your workload. Estimate input and output separately, then include cache reads and writes, reasoning-token effects, tool calls, retries, and any relevant long-context, batch, fast-mode or regional adjustments. Divide the resulting spend by the number of tasks that met your quality bar. A model with a lower token rate can cost more per successful task if it needs more tokens, retries or human correction.
OpenAI’s pricing page and Anthropic’s live pricing page list prices and modifiers. Check them when budgeting or purchasing: rates and model availability can change.
Run a fair, workload-specific comparison
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Build a representative test set. Include common requests, edge cases, long inputs and tool-dependent tasks if those occur in production. Use realistic input sizes and expected output formats, and apply the same success criteria to every provider.
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Match the conditions. Use the same prompts, tools, constraints and comparable settings wherever the APIs allow. Record any differences that cannot be matched, such as a required feature or service configuration, rather than treating the results as directly equivalent.
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Measure quality, latency and reliability. Track task success, correctness, instruction and format adherence, and failures. Measure median and tail response times under realistic concurrency and the service tier you expect to use; a single quick response does not establish production throughput.
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Calculate cost per successful task. Capture input, output, reasoning and cache usage where available, along with tool calls and retries. Apply current pricing modifiers, then divide total evaluation spend by successful tasks—not by requests sent.
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Repeat enough to expose variation. Model outputs can vary. Use repeated runs for tasks where a change in answer could affect the decision, and compare failure patterns as well as average scores. Keep the prompts and scoring consistent when you rerun the evaluation after a model or application change.
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Choose the lowest-cost option that clears the bar. If a workload includes both simple and difficult requests, test whether routing simple, high-volume work to an efficient tier and reserving a more capable model for harder cases improves end-to-end results. Evaluate the routing policy itself, including misroutes, latency and retries.
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Check integration and deployment fit
A strong model result is useful only if it can run within the application’s technical and organizational limits. For each candidate, confirm the current model-specific documentation for:
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- Capabilities: required text, image or other modality support; tool calling; structured output; and context length.
- Integration: API and SDK compatibility, migration effort, rate limits, and any differences in request or response handling.
- Access: whether the model is available through the API, a cloud marketplace or platform you already use, and whether your account is eligible.
- Data and operations: required data location, regional inference options, service controls and operational availability.
- Lifecycle: model IDs, retirement notices, replacement options and the work needed to migrate.
Do not infer a feature set from a model-family name. Anthropic publishes dated model deprecation and replacement notices, making lifecycle checks a practical part of a production decision. Recheck model IDs and notices before launch and as part of ongoing maintenance.
Account for reasoning and operating modes
Some model settings trade speed and token use against the ability to handle multi-step work. OpenAI describes reasoning effort as such a trade-off: lower effort is faster and uses fewer reasoning tokens, while higher effort can help with planning, debugging, synthesis and multi-step trade-offs. Include the setting you intend to deploy in your evaluation, since results from one effort level do not automatically predict results at another. The deployment checklist also recommends comparing latency and relevant token categories, not just output quality.
Anthropic’s Opus page lists fast mode at different rates and US-only inference at a price multiplier; its pricing materials also describe prompt-caching modifiers. Include a mode or regional option only if you expect to use it, and price that actual configuration rather than the headline rate. The Opus page and pricing page are the places to verify current details.
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There is no provider-wide result established by the listed prices or vendor model descriptions. For coding, agents, summarization, multimodal input or other work, the relevant answer is which tested configuration meets your quality and operational requirements at an acceptable cost and latency. If different task categories favor different models, a validated routing setup—or using one provider only for the tasks where it wins—may fit better than selecting a single provider for everything.
Record the chosen model IDs, settings, test results, cost assumptions and fallback behavior. Re-run the comparison when a model changes, a price or lifecycle notice affects your deployment, or your task mix shifts. That turns the choice into a decision you can revisit with evidence rather than a permanent bet on a provider name.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
