NeoSyntropyPredictable by design
Prepay a yearly bank of AI input usage. Your unit cost falls as volume grows, while your AI budget stays forecastable.
PRODUCT SURFACE / OPENAPI
Choose the integration surface that fits your stack. The API is available today; Framework and Proxy are being prepared for release.
/api/v1Direct HTTP access to schema, reasoning, and route operations.
python://frameworkCompose typed AI workflows and control execution in Python.
/proxyConnect an existing agent and route traffic through NeoSyntropy.
From $10minimum deposit / year
$0.10per 1M input tokens
For individuals and side projects. Start with a predictable input-token budget.
Input-token billing with no per-node fees.
Start free$60minimum deposit / year
$0.08per 1M input tokens
For growing startups and product teams.
Input-token billing with no per-node fees.
Buy credits$200minimum deposit / year
$0.065per 1M input tokens
For high-traffic apps and automation.
Input-token billing with no per-node fees.
Buy creditsFrom $2,000minimum deposit / year
$0.05per 1M input tokens
Custom banks for production fleets.
Input-token billing with no per-node fees.
Talk to salesHow billing works: you pre-purchase a credit balance. API usage is measured per 1M input tokens. Higher-volume tiers reduce the unit rate.
Input-token cost comparison
Compare illustrative reasoning and JSON-extraction workloads. NeoSyntropy API usage is billed per 1M input tokens, with the same application logic enforced regardless of the underlying model.
Short graphs: classify, extract, decide.
| Stack | Price / 1M input tokens | Accuracy | Latency |
|---|---|---|---|
| NeoSyntropy API | $0.05 | 94% | 180 ms |
| NeoSyntropy API · baseline | $0.10 | 82% | 320 ms |
| LangGraph · OpenAI large | $0.08 | 93% | 1.9 s |
| LangGraph · OpenAI small | $0.02 | 81% | 720 ms |
| LangGraph · Claude large | $0.09 | 94% | 2.1 s |
| LangGraph · Claude small | $0.025 | 83% | 780 ms |
| Agno · OpenAI large | $0.075 | 92% | 1.8 s |
| Agno · OpenAI small | $0.018 | 80% | 690 ms |
| Agno · Claude large | $0.085 | 93% | 2.0 s |
| Agno · Claude small | $0.022 | 82% | 750 ms |
Multi-step workflows with branching and tool hops.
| Stack | Price / 1M input tokens | Accuracy | Latency |
|---|---|---|---|
| NeoSyntropy API | $0.25–0.35 | 90% | 0.7 s |
| NeoSyntropy API · baseline | $0.50–1.00 | 78% | 1.2 s |
| LangGraph · OpenAI large | $0.45 | 90% | 8.4 s |
| LangGraph · OpenAI small | $0.12 | 74% | 3.6 s |
| LangGraph · Claude large | $0.52 | 91% | 9.1 s |
| LangGraph · Claude small | $0.14 | 76% | 3.9 s |
| Agno · OpenAI large | $0.42 | 89% | 7.8 s |
| Agno · OpenAI small | $0.11 | 73% | 3.4 s |
| Agno · Claude large | $0.48 | 90% | 8.6 s |
| Agno · Claude small | $0.13 | 75% | 3.7 s |
Deep production graphs: retries, gates, and long paths.
| Stack | Price / 1M input tokens | Accuracy | Latency |
|---|---|---|---|
| NeoSyntropy API | $0.50–1.20 | 87% | 1.4 s |
| NeoSyntropy API · baseline | $1.00–5.00 | 76% | 2.8 s |
| LangGraph · OpenAI large | $2.10 | 86% | 28 s |
| LangGraph · OpenAI small | $0.55 | 66% | 12 s |
| LangGraph · Claude large | $2.40 | 87% | 31 s |
| LangGraph · Claude small | $0.62 | 68% | 13 s |
| Agno · OpenAI large | $1.95 | 85% | 26 s |
| Agno · OpenAI small | $0.50 | 65% | 11 s |
| Agno · Claude large | $2.20 | 86% | 29 s |
| Agno · Claude small | $0.58 | 67% | 12 s |
Figures are illustrative baselines for reasoning and JSON-extraction workloads. Framework stacks pay per model call along the path; NeoSyntropy API prices input usage per 1M tokens.
The ROI equation