Seed 1.8 / 채팅
A brand-new model optimized specifically for multimodal agent scenarios. It features enhanced agent capabilities, upgraded multimodal comprehension, and more flexible context
Playground Chat
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Seed 1.8Elevated Foundation Intelligence
Seed 1.8 advances the foundation line with refined reasoning controls, logprobs evaluation, and full OpenAI-compatible tool calling for production agents.
At a Glance
Elevated Foundation Stack
The next step up for general product intelligence.
Refined Reasoning Controls
Four effort levels let you trade latency for depth on a per-request basis.
Evaluation Hooks
logprobs and top_logprobs for monitoring, scoring, and constrained decoding.
Agent Tool Surface
Parallel tool calling and stop sequences for multi-step agents.
Streaming Native
stream defaults to true for interactive paint-as-you-go UX.
How It Works
A production-ready path from brief to finished asset.
Frame the Decision
State what decision or artifact you need, not just a topic.
Dial Reasoning Depth
Use Medium for most knowledge work; High only when stakes or complexity justify it.
Compose Tools
Chain independent tool calls in parallel to cut wall-clock time.
Validate Outputs
Use logprobs or rubric prompts to quality-gate production traffic.
Next-Tier Product Domains
Where foundation quality compounds.
Enterprise Chat
Higher consistency across long conversations.
Research Assist
Medium/high effort for multi-document synthesis.
Code Partners
Tool-enabled coding assistance.
Workflow Agents
Parallel tools for operational automation.
Prompt & Usage Tips
Practical guidance for reliable first-pass results.
One short example of ideal output style dramatically improves format compliance.
Request confidence notes when facts may be incomplete so the model flags gaps.
System messages should state invariants, not essays. Long policies reduce instruction following.
Seed 1.8 Quickstart
Elevated foundation chat.
curl -X POST "https://api.powertokens.ai/v1/chat/completions" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "seed-1-8-251228",
"messages": [
{"role": "user", "content": "Compare these three vendor proposals and recommend one."}
],
"stream": true,
"temperature": 0.7
}'Technical Specifications
Confirmed parameters and runtime execution protocols.
가격 상세
이 모델의 실제 요금은 API 요청에서 전달된 특정 매개변수를 기반으로 동적으로 계산됩니다. 아래는 구체적인 조합과 해당 가격입니다:
| 모달리티 | 입력 크레딧 | 출력 크레딧 | 입력 가격 | 출력 가격 | 암시적 캐시 적중 | 명시적 캐시 적중 | 캐시 생성 |
|---|---|---|---|---|---|---|---|
| 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
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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
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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
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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
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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.
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