Perplexity Sonar and OpenAI with web search are two ways to build grounded answers. Compare their retrieval controls, evidence handling and complete request charges, then separate that decision from a general text-model benchmark.
Side by side
This table compares each provider’s lowest documented baseline input-price model. Baseline prices use standard or short-context conditions and exclude separate free-tier and off-peak rows when paid standard rates exist. The full pricing reference preserves all documented conditions.
| Attribute | Sonar | gpt-5.6-luna |
|---|---|---|
| Provider | Perplexity | OpenAI |
| Official identifier | sonar | gpt-5.6-luna |
| Input USD / 1M | $1 | $0.2 |
| Output USD / 1M | $1 | $1.2 |
| Cached input USD / 1M | Not documented | $0.02 |
| Context tokens | 128,000 | 1,050,000 |
| Maximum output tokens | Not documented | 128,000 |
| Modalities | text | text |
| OpenAI-compatible endpoint | Yes | Yes |
| SDK languages | See official documentation | Python, JavaScript, Go, Java, C# |
| Reference | Sonar | gpt-5.6-luna |
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Read the source and pricing condition beside a value before comparing it. The table describes published records; it does not establish which model will perform best on your task.
Pricing differences
Perplexity’s pricing includes operation-specific charges alongside token usage. Read the Sonar model and request conditions before estimating a search-backed answer. Official documentation.
OpenAI web search has its own billing considerations in addition to model usage. A token-only calculator cannot represent every retrieval charge. Official documentation.
Count useful answers with valid supporting sources. A cheaper response that cites irrelevant material still needs correction or another search. Keep retrieval operations, generated output and any repeated attempts in the same task-cost record.
Perplexity pricing and OpenAI pricing explain the full charge categories. Use the cost calculator with the same observed workload for both.
Limits and free tiers
Check the actual Perplexity account and OpenAI project before planning request bursts. A search-backed task can take variable time and consume work beyond the visible answer. Give the queue an overall deadline and preserve a pending state when processing continues.
OpenAI documents model and usage-tier limits separately from spending controls. Read the relevant current account settings. Official documentation.
Review Perplexity API access evidence and OpenAI free-tier evidence for current offers. A subscription associated with another product should not be treated as API credit without documented eligibility.
Models and modalities
Sonar is documented as a search-grounded API surface. Choose the relevant Sonar model and inspect the returned evidence instead of assuming that every answer is adequately supported. Official documentation.
OpenAI enables web search as a tool for supported models. That makes the tool configuration part of the comparison rather than a capability inferred from a model label. Official documentation.
Keep a general text task separate from a grounded research task. For the latter, measure source relevance, freshness and whether each claim is supported. Compare evidence quality directly rather than use eloquent prose as a proxy.
Developer experience (SDKs, compatibility, docs)
Perplexity’s Sonar quickstart documents its request and response flow. The compatible client path should retain citations and search metadata rather than discard them when extracting message text. Official documentation.
For either implementation, represent the answer and its evidence together. Validate links before presenting them as support for a claim. Keep the tool or retrieval result available to the evaluation process so an unsupported assertion can be traced.
Test missing evidence, contradictory sources and a query with no reliable answer. These cases reveal whether the application can report uncertainty, and whether its retry policy creates more cost without improving the evidence.
Which to pick for … (3 scenarios)
A source-backed answer product. Evaluate Sonar as a focused retrieval-and-answer interface against OpenAI with the required search tool. Prefer the route whose returned evidence supports the product’s claims consistently.
An existing multi-tool agent. Inspect whether the retrieval step fits the existing tool lifecycle. Include the effort needed to preserve citations, errors and usage when introducing another service.
A plain text transformation. Compare ordinary model candidates without automatically enabling search. External retrieval adds complexity when the task only needs to classify or rewrite supplied text.
Choose with a saved evaluation set and an explicit operating budget. Perplexity setup and reference and OpenAI setup and reference link the first-request steps.
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Frequently asked questions
Is Sonar the same as an ordinary text completion?
Does an OpenAI model always use web search?
Do citations guarantee that an answer is correct?
Can I estimate a search product with only token rates?
What is the most useful comparison task?
Sources
- Px Api documentation ↗
- Px Price documentation ↗
- Oa Search documentation ↗
- Oa Price documentation ↗
- Oa Limit documentation ↗
- Oa Api documentation ↗
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