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There is no evidence-based universal winner between OpenAI and Anthropic. Choose by running both providers on the same representative tasks, then compare successful task completion, output quality, latency and total cost per successful task. Model descriptions and advertised strengths can help you decide what to test; they cannot predict which model will work better for your workload.
What the published models and prices can—and cannot—tell you
Provider materials are useful for building a shortlist, not for ranking the providers. OpenAI’s catalog currently positions GPT-6 Astra for demanding reasoning and coding, GPT-6.1 Sol as a balance of capability and cost, and GPT-6 Luna for cost-sensitive, high-volume work. OpenAI says its latest models support text and image input, text output and vision, and are available through the Responses API and client SDKs. These are provider descriptions, not independent performance findings. See the OpenAI model catalog.
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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 work such as summarization, compaction and subagent tasks, while identifying Sonnet 5.5 and Opus 5.5 as better choices for complex agentic coding. These descriptions can guide which tiers to include in a test, but they do not establish how either provider will perform on your tasks. See the Opus page and Haiku 5.5 announcement.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsThe following are published standard API prices, not a like-for-like performance comparison. The listed rates were checked in 2026; prices and pricing conditions can change, so confirm them on the live pages before budgeting.
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| Model | Published standard input price | Published standard output price | Important qualification |
|---|---|---|---|
| GPT-6 Astra | $10 per million tokens | $50 per million tokens | OpenAI catalog listing checked in 2026; see the model catalog. |
| GPT-6.1 Sol | $2 per million tokens | $10 per million tokens | OpenAI catalog listing checked in 2026; see the model catalog. |
| GPT-6 Luna | $0.10 per million tokens | $0.50 per million tokens | OpenAI catalog listing checked in 2026; see the model catalog. |
| Claude Opus 5.5 | $4 per million tokens | $20 per million tokens | The product page also lists cache reads at $0.20 per million tokens; see Anthropic’s Opus page. |
| Claude Haiku 5.5 | $0.10 per million tokens for prompts up to 100,000 tokens; $0.50 above that threshold | $0.50 per million tokens for prompts up to 100,000 tokens; $2.50 above that threshold | Rates and threshold are from Anthropic’s 2026 announcement; see the announcement. |
Do not use these rates alone to choose a provider. They do not include every possible modifier, and input and output prices are not the same as the cost of completing useful work. OpenAI’s pricing page and Anthropic’s pricing page list current pricing details and modifiers, including caching-related options. Recheck them for the model, platform and usage pattern you plan to deploy.
How to run a fair comparison
Use the same conditions for each provider. A small collection of hand-picked prompts can overstate quality if it misses difficult inputs or common failure cases, so build the evaluation around the work the system will actually do.
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- Define the task and what counts as success. Record the real prompt, input size and format, tools available, required output format, and acceptable failure rate. Score correctness or task completion—not just fluency.
- Build a representative test set. Include routine work, edge cases, long inputs and tool-using cases if tools are part of production. Keep the inputs and expected outcomes identical across providers. OpenAI recommends experimentation and representative task evaluation in its model-selection guidance and deployment checklist; the specific test set and scoring rubric should reflect your own workload.
- Choose comparable candidates and settings. Select models that could realistically serve the same job, and hold prompts, tool definitions, output constraints and other settings constant where the APIs allow. If a provider-specific setting is necessary, document it rather than silently changing the test.
- Run enough cases to expose variation. Use the same task set and realistic concurrency for both, and track failures as well as successes. If the application depends on a particular tool call, modality or structured output, include that behavior in the test instead of assuming support from a model-family description.
- Measure quality, speed and usage together. Track task success, factual accuracy, instruction following, format adherence, failure rate and response latency. Record token use—including reasoning and cache usage where applicable—plus tool calls and retries.
- Calculate cost per successful task. Add the relevant model charges and usage modifiers for the tested workload, then divide by the number of tasks that met your success criteria. This reveals whether a lower token price is offset by longer outputs, retries or a lower success rate. OpenAI’s deployment checklist likewise recommends evaluating cost per successful task alongside performance and usage.
What to compare beyond the model’s headline price
Quality and reliability on your tasks
Score each output against the task’s actual requirements. For a data extraction workflow, that might mean correct fields and valid formatting; for coding, it might mean passing tests and following repository constraints. Keep separate scores for correctness, instruction following, format adherence and failure rate so a polished but incorrect answer does not win by impression.
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Latency and throughput at realistic load
Measure response times under the concurrency and service conditions you expect in production. Consider both typical and slow responses, not only a single average. A model that meets your quality bar but misses a user-facing latency target may not be a viable choice for that task.
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End-to-end cost
Include input and output tokens, reasoning-token effects, cache reads and writes, tool calls, retries, and any batch, fast-mode, long-context or regional price adjustments that apply. Use the same traffic mix and success definition when comparing providers. A token-rate comparison is meaningful only when paired with observed usage and task outcomes.
Required capabilities and integration
Check the current documentation for the specific model and platform you intend to use. Confirm required modalities, context size, structured-output and tool support, API compatibility, SDKs, rate limits and access through your cloud marketplace. A capability listed for a provider or model family does not by itself prove that a particular model exposes it in the form your application needs.
Rank #4
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Check deployment, data and model lifecycle constraints
A model that scores well in a test still has to fit your deployment. Verify which API or cloud platform offers the chosen model, whether your account can access it, and whether the data location and operational controls meet your requirements. Anthropic’s Opus page names the Claude plans, Claude Platform and cloud platforms for access; its Haiku announcement names the Claude Platform, AWS, Google Cloud and Microsoft Azure. Confirm current model and regional availability directly with the platform you expect to use.
Before launch, check the exact model ID, lifecycle notices and migration requirements. Anthropic publishes model deprecation and replacement notices, illustrating why production plans should account for model changes rather than relying on a family name alone. Re-test after a model or platform change if it could affect quality, latency, cost or behavior.
Choose the lowest-cost option that clears your bar
Set minimum acceptable thresholds before comparing results: for example, the task-success rate, maximum latency and maximum cost per successful task that the application can tolerate. Among candidates that meet those thresholds and your deployment requirements, choose the option with the best overall fit—not automatically the most capable model or the lowest listed token rate.
If different task types have different needs, test a routing policy: send routine, high-volume work to an efficient tier and reserve a more capable model for cases that need it. Evaluate the full routed system, including classification mistakes, fallback calls, retries and added latency. A routing design is only worthwhile if it maintains the required quality while improving measured cost or throughput.
Finally, keep a fallback plan for outages, access changes or model retirement. The official materials reviewed do not establish which provider performs better for an individual workload; that answer comes from matched evaluation on your tasks, followed by ongoing checks as prices, availability and models change. Anthropic’s Sonnet 4.6 announcement describes results tied to its specified evaluations, not a universal cross-provider ranking.
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