LLM token costs dominate agent budgets. A search API that returns 2,000 tokens of HTML when you need 200 tokens of structured data is burning 90% of your token budget on parsing. We ranked APIs by token efficiency: how much useful, structured context you get per token the LLM must process.
Top Pick
Scavio wins on token efficiency with structured JSON responses averaging 150-300 tokens per query. No HTML, no boilerplate, no parsing overhead.
Full Ranking
#1Our Pick
Scavio
Agents optimizing for minimal token usage per search
Pros
- Structured JSON (150-300 tokens/response)
- No HTML or boilerplate
- Fields are pre-extracted (title, snippet, URL)
- Multi-platform same format
- Predictable response size
Cons
- No full-page content (snippets only)
- No AI summarization built-in
#2
Tavily
Pre-summarized answers that compress context
Pros
- AI answer is highly compressed
- Reduces tokens vs raw results
- One-shot answer + sources
- Good for simple factual queries
Cons
- Summarization may lose nuance
- Higher cost per query at scale
- Less control over what's included
- Web only
#3
Serper
Minimal structured Google results at lowest cost
Pros
- Structured JSON output
- Minimal response size
- Very fast
- 2,500 free/month
Cons
- Less structured than Scavio
- Google only
- No snippet optimization
- Variable response size
#4
Exa
Semantic search with content extraction control
Pros
- Highlights mode extracts relevant passages
- Control over content length
- Semantic matching reduces irrelevant results
- Good precision
Cons
- Full content mode is token-heavy
- $7/1K with contents
- Complex pricing
- Single platform
#5
Perplexity Sonar
Citation-backed answers for research agents
Pros
- Pre-synthesized answer saves parsing
- Citations included
- Good for factual queries
- Multiple model tiers
Cons
- Per-token + per-request pricing
- Answer can be verbose
- Expensive at scale
- Less control over length
Side-by-Side Comparison
| Criteria | Scavio | Runner-up | 3rd Place |
|---|---|---|---|
| Avg Response Tokens | 150-300 | 200-500 (with answer) | 100-200 |
| Useful Signal Ratio | ~95% (structured) | ~80% (summarized) | ~85% (structured) |
| HTML/Boilerplate | None | None | None |
| Predictable Size | Yes (fixed schema) | No (AI-length varies) | Mostly |
| Full Page Content | No (snippets) | Optional (raw_content) | No |
| Multi-Platform | Yes (5) | No (1) | No (1) |
Why Scavio Wins
- Structured JSON with fixed schema means predictable token usage. Agents can budget exactly how many tokens a search will cost.
- No HTML parsing required. The LLM processes only extracted fields (title, snippet, URL, metadata), not raw markup.
- 150-300 tokens per response fits comfortably in tool-result slots of most agent frameworks, leaving context for reasoning.
- Multi-platform same format. Whether searching Google, Reddit, or YouTube, the response structure is identical. No per-platform parsing logic in the prompt.
- At $0.005/query and ~200 tokens/response, the combined cost (API + LLM tokens) is lower than alternatives that return more verbose results.