Documentation Index
Fetch the complete documentation index at: https://docs.kong.fyi/llms.txt
Use this file to discover all available pages before exploring further.
Anthropic models
Pricing (per 1M tokens):
| Model | Input | Output | Cache Write | Cache Read |
|---|
claude-opus-4-6 | $5.00 | $25.00 | $6.25 | $0.50 |
claude-sonnet-4-6 | $3.00 | $15.00 | $3.75 | $0.30 |
claude-haiku-4-5-20251001 | $1.00 | $5.00 | $1.25 | $0.10 |
Model limits:
| Model | Max Prompt (chars) | Max Chunk Functions | Max Output Tokens |
|---|
claude-opus-4-6 | 400,000 | 120 | 16,384 |
claude-sonnet-4-6 | 400,000 | 120 | 16,384 |
claude-haiku-4-5-20251001 | 400,000 | 120 | 16,384 |
Anthropic models have a 200k token context window (~800k chars). Kong uses prompt caching to reduce costs on repeated system prompt content.
OpenAI models
Pricing (per 1M tokens):
| Model | Input | Output | Cached Input |
|---|
gpt-4o | $2.50 | $10.00 | $1.25 |
gpt-4o-mini | $0.15 | $0.60 | $0.075 |
o1 | $15.00 | $60.00 | $7.50 |
o3-mini | $1.10 | $4.40 | $0.55 |
Model limits:
| Model | Max Prompt (chars) | Max Chunk Functions | Max Output Tokens |
|---|
gpt-4o | 350,000 | 80 | 16,384 |
gpt-4o-mini | 350,000 | 80 | 16,384 |
o1 | 400,000 | 40 | 32,768 |
o3-mini | 400,000 | 60 | 32,768 |
OpenAI standard models have 128k token context (~512k chars). Reasoning models (o1, o3-mini) have 200k context but use smaller batch sizes due to higher per-call cost.
Custom providers
No pricing is tracked for custom endpoints. Token counts are still recorded and displayed in the analysis summary. See Custom Endpoints for configuration.
Cost estimation
Rough cost ranges by binary size (using Claude Opus as baseline):
| Binary size | Typical cost | Typical time |
|---|
| ~300 functions | $10-50 | 5-15 min |
| ~1,000 functions | $40-150 | 20-60 min |
| ~3,000+ functions | $150-500+ | 1-3 hours |
These vary significantly with:
- Model choice —
gpt-4o-mini is ~30x cheaper than claude-opus-4-6
- Binary complexity — obfuscated code requires more tool calls
- Signature matches — known library functions skip LLM analysis entirely
Further reading