LLMbytedance-seed

ByteDance Seed: Seed 1.6

Seed 1.6 is a general-purpose model released by the ByteDance Seed team. It incorporates multimodal capabilities and adaptive deep thinking with a 256K context window.

Anyone in the Space can @-mention ByteDance Seed: Seed 1.6 with the team's shared context - pooled credits, one chat, one memory.

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Starter is free forever - 1 Space, 100 credits/month, 1 MCP. No card.

Verdict

Seed 1.6 is ByteDance's multimodal model with a 262K token context window and support for image, video, and text. At $0.25/$2.00 per Mtok, it undercuts most frontier models on input costs while maintaining competitive output pricing. The lack of public benchmarks makes it harder to position against peers, but the pricing and multimodal support suggest it's built for high-volume applications where cost per inference matters more than squeezing out the last percentage point of accuracy. Reach for this when you need affordable multimodal processing at scale.

Best for

  • Cost-sensitive multimodal workflows
  • Video content analysis at volume
  • Long-context document processing
  • Batch inference on mixed media
  • Prototyping multimodal applications

Strengths

The 262K context window handles long documents, transcripts, or video timelines without chunking. Input pricing at $0.25/Mtok is 4-10x cheaper than Claude or GPT-4o, making it viable for high-throughput pipelines. Native video support means you can pass clips directly rather than extracting frames. ByteDance's production infrastructure suggests this model is built for reliability under load, not just benchmark chasing.

Trade-offs

No public benchmarks means you're flying blind on accuracy relative to Claude Sonnet 4.5 or Gemini 1.5 Pro. Output pricing at $2.00/Mtok is competitive but not a bargain. ByteDance's proprietary license limits deployment flexibility compared to open-weight alternatives. Early adopters will need to run their own evals to confirm it meets quality bars for their use case. The model is new enough that edge-case behavior and failure modes are still being discovered in the wild.

Specifications

Provider
bytedance-seed
Category
llm
Context length
262,144 tokens
Max output
32,768 tokens
Modalities
image, text, video
License
proprietary
Released
2025-12-23

Pricing

Input
$0.25/Mtok
Output
$2.00/Mtok
Model ID
bytedance-seed/seed-1.6

Per-token prices show what the model costs upstream. On Switchy your team draws from one shared org credit pool - one plan, one balance for everyone.

Team cost calculator

Estimated monthly spend
$13.64
17.6M tokens / month
5 seats · 80 msgs/day

Switchy meters this against your org's shared credit pool - one plan, one balance for everyone.

Providers

ProviderContextInputOutputP50 latencyThroughput30d uptime
bytedance-seed262k$0.25/Mtok$2.00/Mtok

Performance

Performance snapshots are collected daily. Check back after the next ingestion run.

Benchmarks

Public benchmark scores are not available yet for this model. Check back after the next ingestion run.

Works well with

Top MCPs

Compatibility data comes from first-party telemetry; once we have enough co-usage signal, top MCPs for this model will appear here.

How Switchy teams use it

Not enough Spaces have used this model yet to share anonymised team stats. We wait for at least 50 distinct Spaces per week before publishing any aggregate.

Starter prompts

Analyze Product Demo Video

Watch this product demo video and create a structured summary: list each feature shown, the timestamp it appears, and a one-sentence description of what the user sees on screen.
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Summarize Long Research Paper

Read this entire research paper and provide a 200-word summary covering the research question, methodology, key findings, and limitations. Cite specific section numbers when referencing claims.
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Extract Data from Invoice Images

Extract the following fields from this invoice image: vendor name, invoice number, date, line items with quantities and prices, subtotal, tax, and total. Return as JSON.
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Generate Video Scene Descriptions

Watch this video and generate a scene-by-scene description. For each scene, include the timestamp, a 1-2 sentence description of what's happening, and any visible text or logos.
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Compare Multiple Screenshots

Compare these three screenshots of the same interface. Identify every visual difference: layout changes, text edits, color shifts, or missing elements. List each difference with the affected screen area.
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Example outputs

Illustrative - representative of the model's voice and quality, not literal recordings.

Prompt

Analyze this product roadmap screenshot and extract all feature names, their assigned quarters, and any dependencies mentioned. Format as a structured table.

Output

Based on the image analysis, here's the extracted roadmap: | Feature | Quarter | Dependencies | |---------|---------|-------------| | API Gateway v2 | Q1 2024 | Auth Service upgrade | | Mobile SDK | Q2 2024 | API Gateway v2 | | Analytics Dashboard | Q2 2024 | None | | Enterprise SSO | Q3 2024 | Auth Service upgrade | | Webhook System | Q3 2024 | API Gateway v2 | The roadmap shows a clear dependency chain: the Auth Service upgrade enables both the new gateway and SSO features, while the gateway itself blocks mobile SDK and webhooks. Three features have no blockers and could potentially be accelerated.

Notes

Demonstrates Seed 1.6's multimodal capability — extracting structured data from visual inputs like screenshots or diagrams. The 262K context window means you can include multiple images plus supporting docs in one request. However, without public benchmarks, accuracy on complex visual reasoning tasks remains unverified.

Prompt

Review this 45-minute support call transcript and draft a technical post-mortem. Include: root cause, customer impact timeline, what we learned, and three concrete prevention steps.

Output

**Post-Mortem: Database Connection Pool Exhaustion** **Root Cause:** Deployment script failed to apply connection pool limits to new app servers (lines 234-267 of transcript). Default unlimited connections caused cascade failure when traffic spiked at 14:23 UTC. **Customer Impact:** 14:23-14:58 UTC (35 minutes). 847 users saw timeout errors; 12 enterprise customers filed tickets. **What We Learned:** Our deployment checklist doesn't verify runtime config matches infrastructure-as-code declarations. **Prevention Steps:** 1. Add automated config validation to CI/CD (compare deployed values vs. Terraform state) 2. Implement connection pool monitoring with <80% utilization alerts 3. Update runbook: connection errors now trigger immediate pool size audit before other debugging

Notes

Showcases long-context processing — 45 minutes of transcript likely exceeds 50K tokens, well within the 262K limit. Seed 1.6 maintains coherence across extended documents and produces actionable technical writing. The $2/Mtok output cost means a 1000-token post-mortem like this costs $0.002, reasonable for this use case.

Prompt

Generate a 15-second product demo video concept: our API monitoring tool detecting a latency spike, sending a Slack alert, and auto-scaling the affected service. Describe camera angles, UI transitions, and timing.

Output

**0-3s:** Wide shot of dashboard, three service health cards in green. Camera slowly pushes in on the middle card ("Payment API"). **3-5s:** Card flips red, latency graph spikes from 120ms to 2400ms. Smooth zoom to graph detail. **5-8s:** Split screen: left shows graph, right shows Slack notification sliding in with alert text and "View Details" button. Notification pulses once. **8-11s:** Cut to infrastructure view — three server icons, fourth icon fades in with a subtle glow (auto-scale triggered). **11-15s:** Return to dashboard, card transitions back to green, latency graph descends. Text overlay: "Detect. Alert. Resolve. Automatically."

Notes

Illustrates Seed 1.6's video modality — it can generate or conceptualize video content, not just analyze it. This example produces a storyboard-style description suitable for handing to a motion designer. The model's multimodal training means it understands visual narrative structure, though actual video generation quality and style consistency aren't documented in available benchmarks.

Use-case deep-dives

Multi-format campaign asset review

When you need one model to QA video, images, and copy together

A 4-person creative shop running paid social for DTC brands needs to review 30-40 ad variants daily—video hooks, static images, caption copy—before client approval. Seed 1.6 handles all three modalities in a single 262k-token context window, so you can batch an entire campaign (15 videos, 20 statics, all copy) into one review pass instead of juggling three tools. At $0.25 input / $2.00 output per Mtok, a typical 50k-token review run costs under $0.15. The trade-off: no public benchmarks yet, so you're flying without third-party quality signals. If your workflow already involves manual spot-checks and you value consolidation over proven scores, this is the multimodal play. Run a 2-week pilot on non-critical campaigns before committing.

Long-thread support ticket triage

When support history exceeds what single-modality models can hold

A 12-person SaaS support team handles tickets with 80+ message threads, screenshots, and screen recordings attached. Seed 1.6's 262k-token window fits roughly 200k words or 30+ minutes of video context, so the entire ticket history—including that 8-minute Loom the customer sent in message 47—stays in scope when the model drafts a response. You avoid the context-pruning errors that plague 128k-window models on these marathon threads. At $2.00 output per Mtok, a 2k-token draft costs $0.004, well under the $0.50/ticket labor threshold for triage automation. The risk: without benchmark data, you'll need to A/B test response quality against a known model for 2 weeks before rolling out to all agents. If your median ticket is under 50 messages, a cheaper text-only model likely suffices.

Investor deck feedback loops

When pitch decks need iterative critique across slides and video rehearsals

A 3-person startup accelerator reviews 60 pitch decks per cohort, each with 12-18 slides plus a 5-minute video walkthrough from the founder. Seed 1.6 ingests the PDF slides and video in one pass, then generates slide-specific feedback tied to what the founder said on camera—catching mismatches between deck claims and verbal explanations that slide-only models miss. The 262k-token window handles 10 decks in a single batch review, and at $0.25 input per Mtok, processing 10 decks (roughly 150k tokens) costs $0.0375 total. The boundary: if you're only reviewing slide content without video, a text+image model at half the output cost wins. For true multimodal feedback where video delivery matters as much as slide content, Seed 1.6 is the call.

Frequently asked

Is ByteDance Seed 1.6 good for multimodal tasks?

Yes, Seed 1.6 handles text, image, and video inputs in a single model, which makes it useful for applications that need to process mixed content types. The 262k token context window gives you room for long documents plus multiple images or video frames. No public benchmarks exist yet, so you're relying on ByteDance's internal testing until independent evaluations appear.

Is Seed 1.6 cheaper than GPT-4o for image understanding?

Seed 1.6 costs $0.25 input and $2.00 output per million tokens. GPT-4o runs $2.50 input and $10.00 output, making Seed roughly 10x cheaper on input and 5x cheaper on output. If you're processing large volumes of images or video frames, that price difference compounds quickly. Trade-off is GPT-4o has proven benchmark performance while Seed lacks public validation.

Can Seed 1.6 handle video analysis at scale?

The 262k context window theoretically fits several minutes of video frames, but without published benchmarks on video understanding tasks, you don't know how well it actually performs. ByteDance built this for their own products, so video capability is likely real, but you'll need to test your specific use case before committing to production workloads.

How does Seed 1.6 compare to previous ByteDance models?

ByteDance hasn't publicly released earlier Seed versions through API providers, so there's no direct comparison available. This appears to be their first widely accessible multimodal model. The version number suggests internal iterations exist, but without benchmark history or migration guides, you're evaluating it as a new entry rather than an upgrade path.

Should I use Seed 1.6 for production chatbots with images?

Only if you're comfortable with zero public benchmark data and willing to run extensive internal testing first. The pricing is attractive and the multimodal support is real, but production deployments need reliability guarantees. Test it against GPT-4o or Claude 3.5 Sonnet on your actual use cases before switching. The lack of published performance metrics is a red flag for mission-critical applications.

Data last verified 7 hours ago.Sources aggregated hourly to weekly. See docs/architecture/model-directory.md.