- Text Generation
Claude Opus 4.7 is Anthropic’s most capable generally available large language model, designed for advanced coding, long-horizon agentic workflows, and high-resolution vision tasks. It emphasizes stronger…
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Seed 1.6
- Text Generation
Seed 1.6 is a proprietary general-purpose multimodal large language model from ByteDance Seed, offering long-context reasoning with a context window around 256K–262K tokens. It is positioned as a capable deep-thinking model for both text and image inputs.
Providers Comparison
Speed
Output tokens per second · Higher is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
End-to-End Response Time
Seconds · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
Price
USD per 1M tokens (blended) · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
About the model
What is Seed 1.6?
Seed 1.6 is a proprietary long-context multimodal LLM by ByteDance Seed that supports text and image inputs with chain-of-thought style reasoning. It is mainly used for complex reasoning tasks such as math and analysis where explicit step-by-step thinking is beneficial despite higher latency and token usage. It is also used for general-purpose chat, content generation, and long-context applications that benefit from its ~256K–262K token context window. Seed 1.6 belongs to the ByteDance Seed model family, which also includes related variants such as Seed 1.6 Flash and successors like Seed 2.0 models.
Model capabilities
5 Core Capabilities
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Advanced Reasoning
Performs complex mathematical and logical reasoning with explicit and adaptive chain-of-thought to handle difficult multi-step problems.
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Long-Context Chat
Supports general-purpose conversational tasks over very long documents using a context window around 256K tokens for dialogue.
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Multimodal Understanding
Accepts both text and visual inputs, enabling analysis and discussion of images alongside natural language instructions or questions.
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Tool and Function Use
Calls external tools and structured functions, enabling agent-style workflows such as retrieval, actions, and structured output generation.
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Translation Support
Handles multilingual text and can translate between languages as part of its general-purpose language understanding capabilities.
Use cases
6 Most Valuable Use Cases
- Long-context Assistants
- Multimodal Q&A
- Advanced Code Help
- Document Analysis
- Business Data Insights
- Tool-using Agents
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and best performance for Seed 1.6–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 120ms | 70 tps | 99.99% | $0.04 | $0.08 | 128K |
| ByteDance Seed | APAC | ~220ms | ~40 tps | ~99.9% | ~$0.10 | ~$0.20 | ~64K |
| OpenAI (closest equivalent) | Global | ~250ms | ~30 tps | 99.9% | ~$0.50 | ~$1.50 | 128K |
| Anthropic (closest equivalent) | US East | ~260ms | ~25 tps | 99.9% | ~$0.40 | ~$1.20 | 200K |
| Google AI Studio (closest equivalent) | US Central | ~240ms | ~28 tps | ~99.9% | ~$0.45 | ~$1.30 | 128K |
Performance benchmarks
Technical Specifications
| Metric | Seed 1.6 | GPT-4.1 Mini | Claude 3 Haiku |
|---|---|---|---|
| Avg Latency | ~180ms | ~220ms | ~250ms |
| Context Window | 128K | 128K | 200K |
| Input Price ($/1M) | $0.20 | $0.15 | $0.25 |
| Output Price ($/1M) | $0.60 | $0.60 | $0.80 |
| Max Output Tokens | 4K | 4K | 4K |
| Throughput | 60 tps | 50 tps | 45 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 11.4B
- Prompt tokens processed (last 30 days)
- 26.8M
- Completion tokens generated (last 30 days)
- 3.1M
- API requests served (last 30 days)
- 99.8%
- Average API uptime (last 30 days)
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
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Intelligent Model Routing
Dynamically route each request to the optimal model across providers using policies, latency and quality signals—no client changes required as your stack evolves.
One endpoint, every model. -
Cost-Aware Orchestration
Automatically balance price and performance with tiered policies, shadow tests, and usage controls so teams can ship fast without surprise cloud bills.
Control spend, not velocity. -
Resilient Fallback Flows
Define multi-provider fallback chains that transparently recover from outages, rate limits, or timeouts while maintaining consistent responses to your application.
Stay online, even when APIs don’t. -
Full-Stack Observability
Trace every LLM call end-to-end with logs, metrics, cost, and latency breakdowns to debug incidents quickly and tune prompts with real traffic data.
See every token, everywhere. -
Task-Native Workflows
Declare higher-level tasks like chat, extraction, tools, or scoring once and let LLM.API handle provider-specific formats, streaming, and retries behind the scenes.
Think tasks, not endpoints. -
High-Throughput Batch Jobs
Fan out millions of LLM calls as managed batches with automatic chunking, concurrency control, and retries so you can process datasets, evals, and backfills at scale.
Batch at production scale.
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a general-purpose chat model for everyday tasks and basic assistance.
- You need to build consumer-facing chatbots that handle casual conversation and FAQs reliably.
- Your use case involves summarizing short articles, emails, or internal knowledge base snippets.
- Your use case involves drafting short marketing copy, social posts, or simple product descriptions.
- You need a model to help with light code edits, comments, and small bug explanations.
- Your use case involves multilingual but simple Q&A where rough fluency is acceptable.
Avoid if...
- You need cutting-edge reasoning on complex math, logic puzzles, or deeply technical proofs.
- Your workload requires extremely long-context processing, like entire codebases or large books.
- You need strong, verifiable domain expertise for medical, legal, or financial decision-making.
- Your workload requires highly optimized code synthesis for large projects or advanced refactoring.
- You need robust tool use, agents, or planning across many steps and external systems.
- Your workload requires best-in-class performance on nuanced safety, policy, or compliance judgments.
FAQ
Frequently Asked Questions
-
What is Seed 1.6?
Seed 1.6 is a ByteDance Seed large language model accessible through LLM.API for general-purpose natural language understanding and generation.
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What is Seed 1.6 best suited for?
Seed 1.6 is best for fast, low-cost chatbots, content generation, and assistant-style tools where balanced quality and efficiency matter.
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How is Seed 1.6 priced on LLM.API?
Seed 1.6 usage is billed per-token via LLM.API; check your LLM.API pricing dashboard for the latest input and output token rates.
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What is the context window of Seed 1.6?
Seed 1.6 supports a multi-thousand token context window; see the LLM.API model reference for the exact maximum tokens per request.
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How fast is Seed 1.6 in terms of latency?
Seed 1.6 typically returns first tokens in under a couple of seconds, with total latency depending on prompt size and output length.
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Which modalities does Seed 1.6 support?
Seed 1.6 supports text input and text output; image, audio, and other modalities are not supported through this model on LLM.API.
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How do I call Seed 1.6 through LLM.API?
Use the LLM.API chat or completion endpoints and set the model parameter to "Seed 1.6" in your HTTP or SDK request.
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How does Seed 1.6 compare to similar models on LLM.API?
Compared with larger flagship models, Seed 1.6 generally offers lower cost and faster responses but slightly lower reasoning and generation quality.
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What are the main limitations of Seed 1.6?
Seed 1.6 can hallucinate facts, lacks real-time knowledge or tools, and may underperform on complex reasoning or specialized domain tasks.
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Can Seed 1.6 handle streaming responses via LLM.API?
Yes, you can enable streaming by setting the stream flag in your LLM.API request when using Seed 1.6.
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