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Documentation Index

Fetch the complete documentation index at: https://docs.perplexity.ai/llms.txt

Use this file to discover all available pages before exploring further.

​
Overview

finance_search lets the model pull structured financial and market data for public companies, ETFs, and related instruments. The model decides which fields to fetch based on your prompt. Use it when one answer needs more than one type of financial data, such as valuation, earnings, and context for the same company or list of companies.

​
Capabilities

Data areaWhat it includes
Company basicsQuotes, profiles, peers, and market metadata
FinancialsIncome statement, balance sheet, cash flow (quarterly and annual), key ratios
Valuation and pricingCurrent/near-real-time pricing, 1-minute to 1-month OHLCV ranges, pre-market and after-hours data
EarningsLast earnings call transcript, report filings, beat/miss history, guidance discussion
Segment and KPI trackingRevenue/profit by segment, geography, ARPU, subscriber counts, GMV, and other operating metrics
Analyst coverageForward revenue and EPS estimates, cover count, historical estimate changes
Market activityTop gainers, top losers, and most active symbols
Ownership and corporate actionsInsider activity, ticker-level metadata, splits, and related market events
ETF and index detailsTop constituents, shares, weights, and market values

​
Coverage

finance_search coverage depends on the symbol, exchange, asset class, geography, and source data availability.
Coverage areaCurrent guidance
Ticker and symbol coveragePublicly traded companies, ETFs, and related instruments when a supported symbol can be resolved.
Asset classesPublic equities and ETFs.
Market data may be delayed, incomplete, or unavailable for some symbols. finance_search is for informational use only and does not provide investment, legal, tax, or financial advice.

​
Quickstart

Add finance_search to the tools array. 
from perplexity import Perplexity

client = Perplexity()

response = client.responses.create(
    model="perplexity/sonar",
    input="What's NVIDIA trading at right now, and what is its current P/E?",
    tools=[{"type": "finance_search"}]
)

for item in response.output:
    if item.type == "message":
        print(item.content[0].text)

{
  "background": false,
  "completed_at": 1777644610,
  "created_at": 1777644610,
  "error": null,
  "frequency_penalty": 0,
  "id": "resp_d0476d0f-872d-492a-907e-1daa48eb9e32",
  "incomplete_details": null,
  "instructions": null,
  "max_output_tokens": 8192,
  "max_tool_calls": null,
  "metadata": {},
  "model": "perplexity/sonar",
  "object": "response",
  "output": [
    {
      "categories": ["quote"],
      "results": [
        {
          "category": "quote",
          "content": "## NVDA Quote\nQuote field guide: `price` is the latest quote/current price...\n| symbol | name | timestamp | market_status | price | currency | change | changesPercentage | marketCap | pe | eps | volume | dayLow | dayHigh | yearLow | yearHigh | previousClose | open |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| NVDA | NVIDIA Corporation | 2026-05-01 14:10:07 UTC | open | 200.23 | USD | 0.66 | 0.33 | 4,866,492,706,948 | 40.86 | 4.90 | 28,725,330 | 199.15 | 203 | 110.82 | 216.83 | 199.57 | 201.28 |",
          "sources": [
            "https://www.perplexity.ai/finance/NVDA/historical-data",
            "https://www.perplexity.ai/finance/NVDA"
          ],
          "tickers": ["NVDA"]
        }
      ],
      "tickers": ["NVDA"],
      "type": "finance_results"
    },
    {
      "content": [
        {
          "annotations": [],
          "logprobs": [],
          "text": "NVIDIA (NVDA) is currently trading at **$200.23** per share, and its current P/E ratio is **40.86**.",
          "type": "output_text"
        }
      ],
      "id": "msg_b188058f-8225-4642-90e6-da7112f96b69",
      "role": "assistant",
      "status": "completed",
      "type": "message"
    }
  ],
  "parallel_tool_calls": true,
  "presence_penalty": 0,
  "previous_response_id": null,
  "prompt_cache_key": null,
  "reasoning": null,
  "safety_identifier": null,
  "service_tier": "default",
  "status": "completed",
  "store": true,
  "temperature": 1,
  "text": {
    "format": {
      "type": "text"
    }
  },
  "tool_choice": "auto",
  "tools": [
    {
      "type": "finance_search"
    }
  ],
  "top_logprobs": 0,
  "top_p": 1,
  "truncation": "disabled",
  "usage": {
    "cost": {
      "currency": "USD",
      "input_cost": 0.00189,
      "output_cost": 0.00016,
      "tool_calls_cost": 0.005,
      "total_cost": 0.00705
    },
    "input_tokens": 7570,
    "input_tokens_details": {
      "cached_tokens": 0
    },
    "output_tokens": 63,
    "output_tokens_details": {
      "reasoning_tokens": 0
    },
    "tool_calls_details": {
      "finance_search": {
        "invocation": 1
      }
    },
    "total_tokens": 7633
  },
  "user": null
}

​
Example Prompts

  • Full company brief: “Give me a complete NVIDIA snapshot: valuation, segment revenue for the latest quarter, and management’s latest commentary on margins guidance.”
  • Compare companies in one request: “Compare Apple, Microsoft, and Alphabet on revenue growth, operating margin, and forward P/E for the latest fiscal year.”
  • Earnings + reaction context: “Summarize Tesla’s last earnings call, include actual vs consensus, and describe how the stock and analyst targets moved after publication.”

​
Prompt Guidance

finance_search works best when the prompt states the outcome, not the data shape.
  • Start with the business question first, then include the company or ticker.
  • Add time windows when relevant (latest quarter, fiscal year to date, last 30 days).
  • Let the tool decide which specific report fields to retrieve.
Start with the configuration that matches the shape of the finance question.
ConfigurationBest forLatencyQualityCost
Live Market Data and QuotesReal-time prices, quotes, and latest figuresFastGoodLow
Single-Company Historical LookupsBasic historical financials for one company or tickerBalancedHighMedium
Multi-Step Financial ResearchCross-company comparisons and complex financial analysisThoroughHighestHigh

​
Live Market Data and Quotes

Use this for time-sensitive answers that depend on real-time prices, quotes, or the latest market figures. It is the cheapest and fastest option while maintaining strong quality for live data lookups. 
from perplexity import Perplexity

client = Perplexity()

response = client.responses.create(
    model="perplexity/sonar",
    input="What is Apple trading at right now, and what is its latest market cap?",
    tools=[{"type": "finance_search"}],
    max_steps=1,
    max_output_tokens=1024
)

for item in response.output:
    if item.type == "message":
        print(item.content[0].text)

{
  "id": "resp_541684d6-cc46-4115-9137-bb387088bc32",
  "object": "response",
  "model": "perplexity/sonar",
  "status": "completed",
  "created_at": 1777645562,
  "completed_at": 1777645562,
  "output": [
    {
      "type": "finance_results",
      "categories": ["quote"],
      "tickers": ["AAPL"],
      "results": [
        {
          "category": "quote",
          "tickers": ["AAPL"],
          "content": "## AAPL Quote\n| symbol | name | timestamp | market_status | price | currency | change | changesPercentage | marketCap | pe | eps | volume | dayLow | dayHigh | yearLow | yearHigh | previousClose | open |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| AAPL | Apple Inc. | 2026-05-01 14:26:00 UTC | open | 285.74 | USD | 14.39 | 5.30 | 4,194,988,943,600 | 34.51 | 8.28 | 29,155,124 | 278.37 | 287.21 | 193.25 | 288.62 | 271.35 | 278.86 |",
          "sources": [
            "https://www.perplexity.ai/finance/AAPL/historical-data",
            "https://www.perplexity.ai/finance/AAPL"
          ]
        }
      ]
    },
    {
      "type": "message",
      "id": "msg_d8c03075-799d-4d4d-8feb-cc95824db262",
      "role": "assistant",
      "status": "completed",
      "content": [
        {
          "type": "output_text",
          "text": "Apple (AAPL) is currently trading at **$285.74** per share, up about 5.30% on the day. Its latest market capitalization is approximately **$4.19 trillion**.",
          "annotations": [],
          "logprobs": []
        }
      ]
    }
  ],
  "tools": [{"type": "finance_search"}],
  "max_output_tokens": 8192,
  "tool_choice": "auto",
  "parallel_tool_calls": true,
  "usage": {
    "input_tokens": 7575,
    "output_tokens": 75,
    "total_tokens": 7650,
    "cost": {
      "currency": "USD",
      "input_cost": 0.00189,
      "output_cost": 0.00019,
      "tool_calls_cost": 0.005,
      "total_cost": 0.00708
    },
    "tool_calls_details": {
      "finance_search": {
        "invocation": 1
      }
    }
  }
}

​
Single-Company Historical Lookups

Use this for a single company’s historical figures or basic questions that benefit from both structured finance data and web context. GPT-5.5 is strong at simple web search and token-efficient for historical lookups. 
from perplexity import Perplexity

client = Perplexity()

response = client.responses.create(
    model="openai/gpt-5.5",
    input="What was Microsoft's revenue last fiscal year, and how did it compare with the prior year?",
    tools=[
        {"type": "web_search"},
        {"type": "finance_search"},
        {"type": "fetch_url"}
    ],
    max_steps=5,
    max_output_tokens=2048,
    reasoning={"effort": "low"}
)

for item in response.output:
    if item.type == "message":
        print(item.content[0].text)

{
  "id": "resp_1be7ab7e-0dda-4949-9578-1462f9557a6b",
  "object": "response",
  "model": "openai/gpt-5.5",
  "status": "completed",
  "created_at": 1777645563,
  "completed_at": 1777645563,
  "output": [
    {
      "type": "finance_results",
      "categories": ["financials"],
      "tickers": ["MSFT"],
      "results": [
        {
          "category": "financials",
          "tickers": ["MSFT"],
          "content": "## MSFT FY 2024\n| date | period | income_statement_total_revenues |\n| --- | --- | --- |\n| 2024-06-30 | 2024 FY | 245,122,000,000 |",
          "sources": [
            "https://www.perplexity.ai/finance/MSFT/financials?period=annual&category=INCOME_STATEMENT&fromYear=2024&toYear=2024"
          ]
        }
      ]
    },
    {
      "type": "finance_results",
      "categories": ["financials"],
      "tickers": ["MSFT"],
      "results": [
        {
          "category": "financials",
          "tickers": ["MSFT"],
          "content": "## MSFT FY 2025\n| date | period | income_statement_total_revenues |\n| --- | --- | --- |\n| 2025-06-30 | 2025 FY | 281,724,000,000 |",
          "sources": [
            "https://www.perplexity.ai/finance/MSFT/financials?period=annual&category=INCOME_STATEMENT&fromYear=2025&toYear=2025"
          ]
        }
      ]
    },
    {
      "type": "message",
      "id": "msg_99ccfbfd-bce8-4b9b-b412-b01ef45c7842",
      "role": "assistant",
      "status": "completed",
      "content": [
        {
          "type": "output_text",
          "text": "Microsoft's revenue in its last completed fiscal year, **FY2025 ended June 30, 2025**, was **$281.724 billion**.\n\nCompared with the prior year, **FY2024 revenue was $245.122 billion**, so Microsoft revenue increased by:\n\n- **$36.602 billion**\n- **About 14.9% year over year**",
          "annotations": [],
          "logprobs": []
        }
      ]
    }
  ],
  "tools": [
    {"type": "web_search"},
    {"type": "fetch_url"},
    {"type": "finance_search"}
  ],
  "max_output_tokens": 8192,
  "tool_choice": "auto",
  "parallel_tool_calls": true,
  "usage": {
    "input_tokens": 12522,
    "input_tokens_details": {
      "cached_tokens": 3840,
      "cache_read_input_tokens": 3840
    },
    "output_tokens": 500,
    "total_tokens": 13022,
    "cost": {
      "currency": "USD",
      "input_cost": 0.04341,
      "cache_read_cost": 0.00192,
      "output_cost": 0.015,
      "tool_calls_cost": 0.01,
      "total_cost": 0.07033
    },
    "tool_calls_details": {
      "finance_search": {
        "invocation": 2
      }
    }
  }
}

​
Multi-Step Financial Research

Use this for cross-company comparisons, longer historical investigations, and analysis that needs several tool calls across financial statements, filings, transcripts, and web sources. Opus performs best on complex multi-step reasoning when paired with the full tool suite. 
from perplexity import Perplexity

client = Perplexity()

response = client.responses.create(
    model="anthropic/claude-opus-4-7",
    input="Compare Apple, Microsoft, and Alphabet on revenue growth, margin trends, and management commentary over the last three fiscal years.",
    tools=[
        {"type": "web_search"},
        {"type": "finance_search"},
        {"type": "fetch_url"}
    ],
    max_steps=10,
    max_output_tokens=4096
)

for item in response.output:
    if item.type == "message":
        print(item.content[0].text)

{
  "id": "resp_466bc636-cbad-43ce-9f66-c8b296712f05",
  "object": "response",
  "model": "anthropic/claude-opus-4-7",
  "status": "completed",
  "created_at": 1777645564,
  "completed_at": 1777645564,
  "output": [
    {
      "type": "finance_results",
      "categories": ["financials"],
      "tickers": ["AAPL", "MSFT", "GOOGL"],
      "results": [
        {
          "category": "financials",
          "tickers": ["AAPL", "MSFT", "GOOGL"],
          "content": "## AAPL FY 2025\n| date | period | total_revenues | gross_profit | operating_profit | net_income | gross_margin | operating_margin | net_margin |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| 2025-09-27 | 2025 FY | 416,161,000,000 | 195,201,000,000 | 133,050,000,000 | 112,010,000,000 | 0.47 | 0.32 | 0.27 |\n\n## MSFT FY 2025\n| 2025-06-30 | 2025 FY | 281,724,000,000 | 193,893,000,000 | 128,528,000,000 | 101,832,000,000 | 0.69 | 0.46 | 0.36 |\n\n## GOOGL FY 2025\n| 2025-12-31 | 2025 FY | 402,836,000,000 | 240,301,000,000 | 129,039,000,000 | 132,170,000,000 | 0.60 | 0.32 | 0.33 |"
        }
      ]
    },
    {
      "type": "finance_results",
      "categories": ["financials"],
      "tickers": ["AAPL", "MSFT", "GOOGL"],
      "results": [
        {
          "category": "financials",
          "content": "## AAPL FY 2024\n| 2024-09-28 | 2024 FY | 391,035,000,000 | ... | 0.46 | 0.32 | 0.24 |\n\n## MSFT FY 2024\n| 2024-06-30 | 2024 FY | 245,122,000,000 | ... | 0.70 | 0.45 | 0.36 |\n\n## GOOGL FY 2024\n| 2024-12-31 | 2024 FY | 350,018,000,000 | ... | 0.58 | 0.32 | 0.29 |"
        }
      ]
    },
    {
      "type": "finance_results",
      "categories": ["financials"],
      "tickers": ["AAPL", "MSFT", "GOOGL"],
      "results": [
        {
          "category": "financials",
          "content": "## AAPL FY 2023 — total revenue 383,285,000,000 (GM 0.44 / OpM 0.30 / NM 0.25)\n## MSFT FY 2023 — total revenue 211,915,000,000 (GM 0.69 / OpM 0.42 / NM 0.34)\n## GOOGL FY 2023 — total revenue 307,394,000,000 (GM 0.57 / OpM 0.27 / NM 0.24)"
        }
      ]
    },
    {
      "type": "finance_results",
      "categories": ["transcript"],
      "tickers": ["AAPL", "MSFT", "GOOGL"],
      "results": [
        {
          "category": "transcript",
          "tickers": ["AAPL"],
          "content": "AAPL Q2 2026: Revenue $111.184B (est. $109.46B), EPS $2.01 (est. $1.92). 17% revenue growth in March quarter; June guide +14-17%. GM guide 47.5-48.5%. Management flagged rising memory costs as an increasing headwind. Accelerating AI investment via Apple Intelligence; install base 2.5B devices.",
          "sources": ["https://www.perplexity.ai/finance/AAPL/earnings"]
        },
        {
          "category": "transcript",
          "tickers": ["MSFT"],
          "content": "MSFT Q3 2026: Revenue $82.886B (est. $81.44B), EPS $4.27 (est. $4.07). Revenue +18% YoY; AI ARR reached $37B (+123% YoY). CapEx >$40B next quarter, ~$190B for CY2026. Capacity-constrained through 2026. Expects double-digit revenue and operating-income growth in FY27.",
          "sources": ["https://www.perplexity.ai/finance/MSFT/earnings"]
        },
        {
          "category": "transcript",
          "tickers": ["GOOGL"],
          "content": "GOOGL Q1 2026: Revenue $109.896B (est. $106.98B), EPS $5.11 (est. $2.64). 22% revenue growth; Search +19%, Cloud +63%. Operating margin 36.1%. 2026 CapEx raised to $180-190B; 2027 CapEx to significantly increase. Cloud margin headwind from Wiz acquisition.",
          "sources": ["https://www.perplexity.ai/finance/GOOGL/earnings"]
        }
      ]
    },
    {
      "type": "message",
      "id": "msg_90e83fe4-f020-47f0-990e-f82aa0b655da",
      "role": "assistant",
      "status": "completed",
      "content": [
        {
          "type": "output_text",
          "text": "# Apple vs. Microsoft vs. Alphabet — 3-Year Comparison\n\n## 1. Revenue Growth\n| Company | FY2023 | FY2024 | FY2025 | 2-yr CAGR | YoY FY25 |\n|---|---|---|---|---|---|\n| Apple (AAPL)   | $383.3B | $391.0B | $416.2B | +4.2%  | +6.4%  |\n| Microsoft (MSFT)| $211.9B | $245.1B | $281.7B | +15.3% | +14.9% |\n| Alphabet (GOOGL)| $307.4B | $350.0B | $402.8B | +14.5% | +15.1% |\n\n## 2. Margin Trends\n- Apple: GM 44% → 47%, OpM steady ~32%, NM 25% → 27%.\n- Microsoft: GM ~69% (slightly compressed by AI capex), OpM 42% → 46%, NM 34% → 36%.\n- Alphabet: GM 57% → 60%, OpM 27% → 32% (largest expansion), NM 24% → 33%.\n\n## 3. Management Commentary (latest calls)\n- Apple (Q2 FY26): Revenue +17%, June guide +14-17%; flagging memory-cost headwind.\n- Microsoft (Q3 FY26): Revenue +18%; AI ARR $37B (+123%); CY26 capex ~$190B.\n- Alphabet (Q1 2026): Revenue +22%; Cloud +63%; CY26 capex raised to $180-190B; 2027 capex to significantly increase.\n\n## Bottom Line\n- Revenue growth: Microsoft (closely followed by Alphabet)\n- Margin level: Microsoft\n- Margin expansion: Alphabet\n- Capital intensity: Apple is lightest; MSFT and GOOGL each spending $180-190B on 2026 capex.\n\nKey tension: AI investment is fueling top-line acceleration (especially MSFT and GOOGL) but creating depreciation and component-cost headwinds that are starting to weigh on gross margins.",
          "annotations": [],
          "logprobs": []
        }
      ]
    }
  ],
  "tools": [
    {"type": "web_search"},
    {"type": "fetch_url"},
    {"type": "finance_search"}
  ],
  "max_output_tokens": 8192,
  "tool_choice": "auto",
  "parallel_tool_calls": true,
  "usage": {
    "input_tokens": 61887,
    "input_tokens_details": {
      "cached_tokens": 36778,
      "cache_creation_input_tokens": 25100,
      "cache_read_input_tokens": 36778
    },
    "output_tokens": 3456,
    "total_tokens": 65343,
    "cost": {
      "currency": "USD",
      "input_cost": 0.00005,
      "cache_creation_cost": 0.15688,
      "cache_read_cost": 0.01839,
      "output_cost": 0.0864,
      "tool_calls_cost": 0.02,
      "total_cost": 0.28172
    },
    "tool_calls_details": {
      "finance_search": {
        "invocation": 4
      }
    }
  }
}

​
Parameters

ParameterTypeRequiredDescription
typestringYesMust be "finance_search".

​
Response Shape

When finance_search runs, the response can include finance_results output items before the final assistant message. Each finance_results item includes the requested finance categories, ticker symbols, structured content, and source URLs when available. The final usage object includes token counts, cost details, and tool_calls_details.finance_search.invocation when tool-call usage is reported. 
{
  "output": [
    {
      "type": "finance_results",
      "categories": ["quote"],
      "tickers": ["NVDA"],
      "results": [
        {
          "category": "quote",
          "tickers": ["NVDA"],
          "content": "Structured quote data returned by the finance search tool.",
          "sources": [
            "https://www.perplexity.ai/finance/NVDA"
          ]
        }
      ]
    },
    {
      "type": "message",
      "role": "assistant",
      "content": [
        {
          "type": "output_text",
          "text": "The answer generated from finance data."
        }
      ]
    }
  ],
  "usage": {
    "tool_calls_details": {
      "finance_search": {
        "invocation": 1
      }
    }
  }
}

​
Pricing

finance_search is billed at $5 per 1,000 invocations. Model token usage is billed separately according to Agent API token pricing.
Pricing follows the same pattern as other tool calls: pay for invocations plus model tokens. See Pricing.

​
Limits / Quotas

finance_search uses the same Agent API request flow as other tools. Limits depend on your account tier, request configuration, and the number of tool invocations needed to answer the prompt. See Rate Limits & Usage Tiers for tier-based request limits.
Limit areaWhat to expect
API rate limitsAgent API rate limits apply to requests that use finance_search.
Tool invocationsEach time the model calls finance_search, it counts as one billable tool invocation. Multi-company or multi-step prompts may require more than one invocation.
Step limitsUse max_steps to cap multi-step requests that combine finance_search with tools such as web_search and fetch_url.
Output limitsLarge comparisons, long transcripts, and multi-year financial tables can hit max_output_tokens or response truncation settings.
Coverage limitsSome symbols, exchanges, regions, asset classes, or fields may be unavailable depending on source coverage and data freshness.

​
Next Steps

Web Search

Search the web for source-grounded context.

Fetch URL Content

Fetch full content from known URLs.

People Search

Search for professionals and employees.

Agent API Quickstart

Get started with the Agent API.