Seed 1.6 / Chat
Maintaining linguistic consistency, improving instruction-following capabilities, and better suitability for VLM scenarios.
Playground Chat
Start a conversation with the AI model. You can ask anything.
AI-generated responses may vary in accuracy.
Seed 1.6Reliable General-Purpose Intelligence
Seed 1.6 is the balanced foundation chat model with configurable thinking, full sampling controls, logprobs, and OpenAI-compatible tool calling.
At a Glance
Balanced Foundation Capabilities
A dependable default for broad product surfaces.
Balanced Quality Profile
Tuned for consistent quality across chat, extraction, summarization, and light reasoning.
Full Evaluation Surface
logprobs, top_logprobs, and stop sequences for production monitoring and constrained output.
Structured Extraction
Stop sequences and explicit schemas make Seed 1.6 reliable for form filling, CRM enrichment, and ticket routing.
Agent Tool Loops
Parallel tool calling supports multi-step assistants that fetch, decide, and act without serial bottlenecks.
How It Works
A production-ready path from brief to finished asset.
Load Context Clearly
Provide source material up front, then state the transformation you want.
Choose Effort
Minimal for labeling, Medium for summaries, High for multi-step analysis.
Attach Tools if Useful
Use parallel tools for lookups that do not depend on each other.
Monitor Quality
Sample logprobs in evaluation sets to catch drift after prompt changes.
General Product Domains
A solid default across surfaces.
Knowledge Chat
Everyday Q&A with optional light thinking.
Content Summarization
Reliable summaries with structured output.
Data Extraction
Field extraction with stop-constrained replies.
Agent Backends
Tool calling for multi-step workflows.
Prompt & Usage Tips
Practical guidance for reliable first-pass results.
Bullets, JSON fields, or a decision + rationale. Explicit shapes reduce follow-up turns.
Split “summarize AND rewrite AND extract” into separate calls for cleaner results.
Pick Flash when TTFT dominates; pick Seed 1.6 when consistency matters more.
Foundation Quickstart
Balanced chat completions.
curl -X POST "https://api.powertokens.ai/v1/chat/completions" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "seed-1-6-250915",
"messages": [
{"role": "user", "content": "Extract the action items from this meeting transcript."}
],
"stream": true,
"temperature": 0.7
}'Technical Specifications
Confirmed parameters and runtime execution protocols.
Pricing Details
The actual billing for this model is dynamically calculated based on the specific parameters passed in your API request. Below are the specific combinations and their corresponding pricing:
| Modality | Input Credits | Output Credits | Input Price | Output Price | Implicit Cache Hit | Explicit Cache Hit | Cache Creation |
|---|---|---|---|---|---|---|---|
| 0 - 128K | 238/ 1M Tokens | 1,900/ 1M Tokens | $0.238 | $1.900 | $0.050 50/ 1M Tokens | -- | -- |
| 128K - 256K | 475/ 1M Tokens | 3,800/ 1M Tokens | $0.475 | $3.800 | $0.050 50/ 1M Tokens | -- | -- |
Models from the Same Channel
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DeepSeek-V4.1-Flash
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Lightweight flagship of DeepSeek's new Causal Encoder-Decoder family. 552B-parameter MoE with asymmetric activation—8B for input, 16B for output—delivering flagship-level intelligence at significantly lower inference cost . Native multimodal vision understanding. 1M-token context window with 384K max output. KV cache compressed to 1/4 HBM and 1/8 SSD of previous generation . Outperforms V4-Pro on Terminal-Bench 2.1 (90.6), DeepSWE v1.1 (74.2), and CyberGym (88.1)
DeepSeek V3.2
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The official version of DeepSeek-V3.2 balances reasoning capability and output length, making it suitable for daily use—such as question-answering scenarios and general Agent task scenarios.
Seed 2.0 Pro
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Focused on long-chain reasoning and stability in complex task execution, designed for complex real-world business scenarios.
Seed 2.0 Lite
seed-2-0-lite-260228
Balances generation quality and response speed, making it a strong general-purpose production model.
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Frequently Asked Questions
Everything you need to know before integrating this model.
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