Seed 1.6 / チャット
Maintaining linguistic consistency, improving instruction-following capabilities, and better suitability for VLM scenarios.
Playground Chat
AIモデルとの会話を開始します。何でも質問できます。
AI生成結果の正確性は異なる場合があります。
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.
料金詳細
このモデルの実際の課金は、API リクエストで渡される特定のパラメータに基づいて動的に計算されます。以下は具体的な組み合わせとそれに対応する料金です：
| モダリティ | 入力クレジット | 出力クレジット | 入力価格 | 出力価格 | 暗黙的キャッシュヒット | 明示的キャッシュヒット | キャッシュ作成 |
|---|---|---|---|---|---|---|---|
| 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
Explore complementary models and alternative versions from the same provider channel.
DeepSeek-V4.1-Flash
deepseek-v4-1-flash-260910
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
deepseek-v3-2-251201
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
seed-2-0-pro-260328
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.
Recommended Related Models
Explore complementary video and multimodal models with your unified API key.
GLM-5.3
glm-5.3
Zhipu AI's flagship text model optimized for complex software engineering and long-horizon Agent tasks. Features a 1M-token context window with mandatory reasoning (3 levels: low/high/max). Coding capability improved 50% over GLM-5.2 on Z.ai Code Bench; scores SOTA on Terminal Bench 3.0. Excels in cybersecurity tasks (cyber vulnerability discovery)
MiniMax-M3.1-Flash
MiniMax-M3.1-Flash
Next-gen efficiency-focused multimodal model (428B MoE) for agentic workflows, coding, and computer use . Features a 1M-token context window and tunable thinking depth (low–max) . Scores 73.7% on DeepSWE v1.1, 61.7 on OSWorld V2, and 82% on CyberGym, while delivering 200+ TPS output speed at 70% lower agent-task cost than its predecessor
GLM-5.3 Flash
glm-5.3-flash
Z.ai's native multimodal model (320B-A18B) with text, image, video, and file input. Features hybrid sparse + linear attention, reducing attention compute 3x and KV cache 4.4x vs. GLM-5.3. Scores 57 on Artificial Analysis Intelligence Index—matching Claude Opus 4.8—with 1M context and 128K max output. Excels in agentic coding, vision-based Computer Use, and CAD reproduction
Qwen3.8 Flash
qwen3.8-flash
Alibaba's cost-efficient multimodal reasoning model. Supports text, image, and video inputs with text output. Features a native 1M-token context window for long documents, codebases, and agentic workflows. Excels in coding assistance, desktop interaction, chart analysis, and long-video understanding. Compatible with OpenAI/Anthropic protocols for seamless integration
Frequently Asked Questions
Everything you need to know before integrating this model.
Start Building with Seed 1.6 Today
Create an account in seconds to receive free test credits. No upfront payment or long-term contract required.