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DeepSeek V4 Pro is DeepSeek’s flagship open-weights Mixture-of-Experts language model with a 1 million token context window and strong reasoning and coding capabilities. It is notable…
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Seed 1.6 Flash
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Seed 1.6 Flash is an ultra-fast multimodal "deep thinking" large language model from ByteDance Seed, offering long-context reasoning with support for both text and visual 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 Flash?
Seed 1.6 Flash is a proprietary ByteDance Seed large language model optimized for high-speed, long-context multimodal reasoning over text and images. It is mainly used for interactive chatbots, question answering, and content generation that benefit from its large context window and fast inference. It is also applied in vision-language tasks such as image understanding, document analysis, and tool-using agents that combine visual and textual information. Seed 1.6 Flash belongs to the Seed model family from ByteDance, alongside models such as Seed 1.6 and other Seed variants released between 2024 and 2026.
Model capabilities
5 Core Capabilities
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Multimodal Reasoning
Supports deep reasoning across text and visual inputs for analysis, explanation, and complex problem solving with high throughput.
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Fast Text Chat
Provides ultra-fast conversational responses for assistants, coding help, drafting, and question answering with long, coherent context handling.
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Large Context Handling
Works with context windows around 256K–262K tokens, enabling long-document analysis, summarization, and cross-reference of extensive inputs.
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Visual Understanding
Processes images for tasks like description, classification, and multimodal question answering as part of its vision-enabled capabilities.
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Language Translation
Handles multilingual text inputs, enabling transformation and localization workflows that depend on strong cross-language understanding.
Use cases
6 Most Valuable Use Cases
- Customer Support Chatbot
- Invoice Data Extraction
- Legal Case Search
- Compliance Case Monitoring
- E-commerce Product Assistant
- Code Generation Helper
Transparent pricing
Cost Comparison
LLM API offers the lowest Seed 1.6 Flash–class pricing with the largest context window.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 120ms | 80 tps | 99.99% | $0.04 | $0.08 | 128K |
| ByteDance Seed | Global | ~180ms | ~40 tps | ~99.9% | ~$0.06 | ~$0.12 | ~64K |
| OpenAI | Global | ~200ms | ~50 tps | 99.9% | ~$0.50 | ~$1.50 | ~128K |
| Anthropic | US East | ~220ms | ~35 tps | 99.9% | ~$0.40 | ~$1.20 | ~200K |
| Google Cloud | Global | ~210ms | ~45 tps | 99.9% | ~$0.45 | ~$1.30 | ~128K |
Performance benchmarks
Technical Specifications
| Metric | Seed 1.6 Flash | OpenAI gpt-4.1-mini | Gemini 1.5 Flash |
|---|---|---|---|
| Avg Latency | ~180ms | ~220ms | ~200ms |
| Context Window | 128K | 128K | 1M |
| Input Price ($/1M tokens) | $0.10 | $0.15 | $0.075 |
| Output Price ($/1M tokens) | $0.40 | $0.60 | $0.30 |
| Max Output Tokens | 4K | 4K | 8K |
| Throughput | ~70 tps | ~60 tps | ~65 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 11.8B
- Prompt tokens processed (last 30 days)
- 320M
- Completion tokens generated (last 30 days)
- 9.4M
- API requests served (last 30 days)
- 99.8%
- Avg 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
Automatically route each request to the optimal model across providers based on latency, capability, and policy—without changing your integration or redeploying code.
One endpoint, any model -
Cost-Aware Orchestration
Dynamically balance quality and price by tiering models, setting budget caps, and offloading to cheaper options while keeping SLAs and accuracy under control.
Lower spend, same output -
Resilient Fallback Flows
Define provider-agnostic fallback chains so timeouts, rate limits, or model failures transparently retry on backups, keeping your production workloads online.
Never fail on 500s -
Deep LLM Observability
Trace every call across providers with logs, metrics, and structured events so you can debug prompts, compare models, and tune performance in one place.
See every token -
Task-Level Abstractions
Describe tasks—chat, extraction, tools—once, and let LLM.API pick the best models, prompts, and parameters so teams ship AI features faster.
Ship tasks, not wiring -
High-Throughput Batch Jobs
Run large-scale inference jobs across providers with automatic chunking, retries, and concurrency control, turning millions of records into reliable outputs.
Batch at cloud scale
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a lightweight, fast model for high-volume, low-complexity chat or Q&A.
- You need inexpensive API calls for simple assistant features across many users.
- Your use case involves basic content generation like captions, summaries, or short replies.
- Your use case involves integrating an LLM into mobile or bandwidth-constrained applications.
- You need rapid prototyping of AI features without requiring top-tier reasoning performance.
- You need a fallback model to handle overflow traffic from heavier primary models.
Avoid if...
- You need state-of-the-art reasoning for complex multi-step tasks or formal proofs.
- Your workload requires highly reliable code generation for production-grade software systems.
- You need long-context understanding over very large documents or multi-file codebases.
- Your workload requires nuanced domain expertise in specialized fields like law or medicine.
- You need top-tier performance on complex data analysis, planning, or multi-agent orchestration.
- Your workload requires consistently high-quality creative writing comparable to frontier flagship models.
FAQ
Frequently Asked Questions
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What is Seed 1.6 Flash?
Seed 1.6 Flash is a fast, cost-efficient generative AI model from ByteDance Seed designed for latency-sensitive text applications.
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What is Seed 1.6 Flash best suited for?
Seed 1.6 Flash is best for real-time chatbots, autocomplete, lightweight agents, and high-traffic applications where low latency and low cost matter most.
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What is the context window of Seed 1.6 Flash?
Seed 1.6 Flash supports a 16K token context window, suitable for moderately long conversations and documents.
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How fast is Seed 1.6 Flash when called through LLM.API?
Typical end-to-end latency is in the low hundreds of milliseconds for short prompts when streaming is enabled, excluding network overhead.
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What modalities does Seed 1.6 Flash support via LLM.API?
Seed 1.6 Flash currently supports text-in, text-out interactions; it does not process images, audio, or video.
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How is pricing for Seed 1.6 Flash handled on LLM.API?
Pricing for Seed 1.6 Flash is usage-based per 1,000 tokens and is billed through LLM.API’s unified billing, not directly by ByteDance.
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How do I access Seed 1.6 Flash through the LLM.API gateway?
You call the standard LLM.API chat or completion endpoint and specify the model name "seed-1.6-flash" in the request payload.
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How does Seed 1.6 Flash compare to larger Seed models?
Compared to larger Seed variants, Seed 1.6 Flash is cheaper and faster but somewhat weaker on complex reasoning and long-context analytical tasks.
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Are there any notable limitations of Seed 1.6 Flash?
Seed 1.6 Flash can struggle with very long multi-step reasoning, precise tool-calling logic, and tasks requiring deep domain expertise.
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Can I fine-tune Seed 1.6 Flash via LLM.API?
Direct fine-tuning is not supported; instead, you should use prompt engineering and retrieval-augmented generation with your own data sources.
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