Preset Deep Research
Perplexity responsesPreset Deep Research is a Perplexity responses-API model. Capabilities include function calling. Route Preset Deep Research via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
We don't have verified per-token pricing for Preset Deep Research yet. If you have a source from Perplexity's documentation, help us add it — your submission gets reviewed within 48 hours.
Pricing
Per-token rates, expressed in USD per 1M tokens. Verified May 12, 2026.
| Input | — | |
| Output | — |
Limits
- Context window
- —
- Max input
- —
- Max output
- —
- Modalities
- text
Capabilities
- Function calling ✓ supported
- Parallel tool calls — not advertised
- Vision input — not advertised
- Audio input — not advertised
- Audio output — not advertised
- PDF input — not advertised
- Streaming ✓ supported
- Structured output — not advertised
- Prompt caching — not advertised
- Reasoning — not advertised
Where it's strong
- +agentic workflows that depend on reliable tool calls
Watch out for
- No major caveats flagged from public spec.
Benchmarks pending
We haven't logged public benchmark scores for Preset Deep Research yet. Have one to contribute? Submit a source — citations help us prioritise.
Call Preset Deep Research via Agent Command Center
One OpenAI-compatible endpoint. Routing, fallback, semantic caching, guardrails, and cost tracking come along for the ride. First 100K requests + 100K cache hits free every month.
agentcc / @agentcc/client). Per-call metadata — provider, cost, latency, cache hit, request id — is returned on x-agentcc-* response headers, so any HTTP client can read it.# Preset Deep Research via the Agent Command Center Python SDK
# pip install agentcc
import os
from agentcc import AgentCC
client = AgentCC(
api_key=os.environ["AGENTCC_API_KEY"], # from app.futureagi.com → Settings → API Keys
base_url="https://gateway.futureagi.com/v1",
)
resp = client.chat.completions.create(
model="perplexity/preset-deep-research",
messages=[{"role": "user", "content": "Hello, Preset Deep Research!"}],
)
print(resp.choices[0].message.content)
print(f"Tokens: {resp.usage.total_tokens}")
# Per-call gateway metadata is returned on x-agentcc-* response headers.
# When you need it programmatically, use .with_raw_response to get them:
raw = client.chat.completions.with_raw_response.create(
model="perplexity/preset-deep-research",
messages=[{"role": "user", "content": "Same call, but I want the headers."}],
)
print("Provider:", raw.headers.get("x-agentcc-provider"))
print("Latency:", raw.headers.get("x-agentcc-latency-ms"), "ms")
print("Cost: ", raw.headers.get("x-agentcc-cost"), "USD")
print("Cache: ", raw.headers.get("x-agentcc-cache"))AGENTCC_API_KEY with a key fromapp.futureagi.com.Gateway docs ↗Compare with similar models
Preset Deep Research doesn't have a public Arena ELO score yet, so we group by provider only — quality-tier comparisons need a benchmark.
FAQ
How much does Preset Deep Research cost?
Public per-token pricing for Preset Deep Research is not yet published. Submit a source on this page to help us add it.
What is the context window of Preset Deep Research?
Context window for Preset Deep Research is not currently public.
Does Preset Deep Research support function calling?
Yes — Preset Deep Research supports function (tool) calling.
Is Preset Deep Research good for production?
Preset Deep Research is well-suited for agentic workflows that depend on reliable tool calls.
How can I route to Preset Deep Research with fallback?
Use Agent Command Center: a single OpenAI-compatible endpoint that supports cost-optimized routing, latency-aware retries, model fallback, and shadow traffic. Configure once, swap models without app changes.
Useful links for Preset Deep Research
Official sources, independent benchmarks, and pricing aggregators — no random search-engine guesses.
Third-party evals — verify the marketing.
Cross-check our number against the rest of the ecosystem.