GPT-5.2 vs Sonar Deep Research: API Cost Comparison
Compare the API pricing, context windows, features, and real-world cost projections for GPT-5.2 (OpenAI) and Sonar Deep Research (Perplexity). Use the interactive calculator below to compute your exact monthly cost based on your token usage and request volume.
Prices verified Mar 10, 2026
Interactive Cost Calculator
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Showing costs for 2 models. Cheapest: Sonar Deep Research at $60.00/month.
| Alert | |||||||
|---|---|---|---|---|---|---|---|
BestSonar Deep Research | Perplexity | $60.00 | $0.006000 | $20.00 | $40.00 | 31.4% | Alerts coming soon |
GPT-5.2 | OpenAI | $87.50 | $0.008750 | $17.50 | $70.00 | — | Alerts coming soon |
Monthly Cost Comparison
Price Comparison at a Glance
All prices are in USD per 1 million tokens ($/M tokens). Lower is cheaper.
| Pricing Type | GPT-5.2 | Sonar Deep Research | Cheaper |
|---|---|---|---|
| Input (standard) | $1.75/M | $2.00/M | GPT-5.2 |
| Output | $14.00/M | $8.00/M | Sonar Deep Research |
| Cached input | $0.18/M | N/A | — |
Prices last verified: 2026-03-06 – 2026-03-10
Cost Breakdown by Usage Volume
Estimated monthly costs at different request volumes, assuming 1,000 input tokens and 500 output tokens per request. Adjust in the calculator above for your specific use case.
| Volume | GPT-5.2Monthly | Sonar Deep ResearchMonthly | GPT-5.2Per request | Sonar Deep ResearchPer request |
|---|---|---|---|---|
| 1K requests/mo | $8.75 | $6.00 | $0.008750 | $0.006000 |
| 10K requests/mo | $87.50 | $60.00 | $0.008750 | $0.006000 |
| 100K requests/mo | $875.00 | $600.00 | $0.008750 | $0.006000 |
| 1M requests/mo | $8,750.00 | $6,000.00 | $0.008750 | $0.006000 |
Green values indicate the lower-cost option at each volume tier. Cost per request is calculated at 1,000 input + 500 output tokens using standard (non-batch, non-cached) pricing.
Price History
Input price per 1M tokensWhen to Choose GPT-5.2
by OpenAI
- Code generation
- Document summarization
- RAG / Semantic search
Input / 1M tokens
$1.75/M
Output / 1M tokens
$14.00/M
Context window
400,000
Tier
premium
When to Choose Sonar Deep Research
by Perplexity
- RAG / Semantic search
- Document summarization
Input / 1M tokens
$2.00/M
Output / 1M tokens
$8.00/M
Context window
127,072
Tier
reasoning
Key Differences Beyond Price
Cost is only one factor in choosing an AI model. Context window size, rate limits, supported features, and latency all affect whether a model fits your use case.
| Capability | GPT-5.2 | Sonar Deep Research |
|---|---|---|
| Context window | 400,000 tokens | 127,072 tokens |
| Max output tokens | 128,000 tokens | 8,192 tokens |
| Performance tier | Premium | Reasoning |
| Vision / image input | Yes | No |
| Function calling | Yes | No |
| JSON mode | Yes | No |
| Prompt caching | Yes | No |
| Batch API (50% discount) | No | No |
| Extended reasoning | No | Yes |
| Fine-tuning | No | No |
| Rate limit (req/min) | 10,000 | Not published |
GPT-5.2 notes
Highest capability OpenAI model. Supports vision and all API features.
Sonar Deep Research notes
Additional fees: $2/1M citation tokens, $3/1M reasoning tokens, $5 per 1,000 search queries. Designed for exhaustive research tasks.
Frequently Asked Questions
Is GPT-5.2 cheaper than Sonar Deep Research?
At standard usage (1,000 input tokens, 500 output tokens, 100,000 requests/month), Sonar Deep Research costs $600.00/month versus $875.00/month for GPT-5.2 — a 31% saving. Your actual savings will vary based on your token profile; output-heavy workloads amplify differences in output pricing.
Which model has a larger context window, GPT-5.2 or Sonar Deep Research?
GPT-5.2 has a larger context window at 400,000 tokens, compared to 127,072 tokens for Sonar Deep Research. A larger context window is important for processing long documents, multi-turn conversations, or large codebases without truncation.
Do GPT-5.2 and Sonar Deep Research support the Batch API?
Neither GPT-5.2 nor Sonar Deep Research currently supports a batch API with discounted pricing. For batch-eligible alternatives, consider models from OpenAI, Anthropic, or Google that include batch API support.
Which model offers better prompt caching?
GPT-5.2 supports prompt caching at $0.18/M for cached input, while Sonar Deep Research does not offer prompt caching. For RAG applications or chatbots with large, repeated context, GPT-5.2's caching capability can substantially reduce effective costs.
What are the best use cases for GPT-5.2 vs Sonar Deep Research?
Both models are well-suited for Document summarization, RAG / Semantic search. GPT-5.2 is particularly strong for Code generation. Sonar Deep Research is favored for similar workflows. At the same quality level, the lower-cost model is usually preferable; use this page's calculator to compare total monthly spend at your volume.
What is the cost per request for GPT-5.2 vs Sonar Deep Research?
At 1,000 input tokens and 500 output tokens per request — a typical conversational workload — GPT-5.2 costs $0.008750 per request and Sonar Deep Research costs $0.006000 per request. At 100,000 requests/month, that translates to $875.00 and $600.00 respectively. Use the interactive calculator to adjust these parameters for your actual workload.
GPT-5.2 vs Sonar Deep Research: Summary
When comparing GPT-5.2 and Sonar Deep Research for API cost, the right choice depends on your workload's token profile, required features, and tolerance for latency. Sonar Deep Research offers lower total cost at standard usage volumes (1,000 input + 500 output tokens per request at 100,000 requests/month) at $600.00/month, compared to $875.00/month for GPT-5.2.
Both models are priced in USD per million tokens, the standard unit across all major AI API providers. GPT-5.2 charges $1.75/M for input tokens and $14.00/M for output tokens. Sonar Deep Research charges $2.00/M input and $8.00/M output. If your workload is output-heavy (more tokens generated than consumed as input), the model with the lower output price compounds cost savings significantly at scale.
Prompt caching is supported by GPT-5.2 but not Sonar Deep Research. For workloads with large, repeated system prompts or document context — such as RAG pipelines or multi-turn conversations with a fixed knowledge base — prompt caching can reduce effective input costs by 60–90%, which may change the cost ranking between these two models at your specific usage pattern.
Context window capacity differs between the two: GPT-5.2 supports up to 400,000 tokens in a single request, versus 127,072 tokens for Sonar Deep Research. A larger context window is essential for document summarization, large codebase analysis, and multi-document retrieval-augmented generation (RAG) applications.
Use the interactive calculator at the top of this page to enter your actual token usage and monthly request volume for a precise cost comparison tailored to your workload. Adjust for batch API discounts and prompt caching to find the most cost-effective option for your specific deployment.
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Relevant Use Cases
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