Seed 2.0 Mini
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ByteDance Seed | 262,144 token context | $0.10/M input, $0.40/M output. Seed-2.0-mini targets latency-sensitive, high-concurrency, and cost-sensitive scenarios, emphasizing fast response and flexible inference deployment. It delivers performance comparable to ByteDance-Seed-1.6, supports 256k context, four reasoning effort modes (minimal/low/medium/high), multimodal understanding, and is optimized for lightweight tasks where cost and speed take priority.
Guides for this model
The 10 cheapest ChatGPT alternatives in 2026: free local models, free tiers and flat-rate plans
How to choose an AI model in October 2026: the closed source news, then the open weights worth downloading
Open weights vs open source vs API-only: what you are actually allowed to do
Captain's notes
Notes from 2026-10-13
Seed-2.0-Mini is ByteDance Seed's lightweight, fast inference model built for latency-sensitive and high-concurrency workloads. It supports a 256k token context, four selectable reasoning effort levels, and multimodal input including vision and video, at $0.10 per million input tokens. If you need something cheaper and faster than a frontier model and can accept a step down in raw capability, this is the practical choice.
Known for
- Four reasoning effort modes let you trade compute for speed on demand.
- 256k token context window handles long documents without chunking.
- Multimodal: accepts image and video inputs alongside text.
- Priced at $0.10/M input, $0.40/M output — well below most frontier models.
- Designed explicitly for high-concurrency, latency-sensitive production deployments.
Watch out for
- Closed weights, API-only — you cannot run this locally or inspect the model.
- Capability is pegged to ByteDance Seed-1.6, not a top-tier frontier model.
- ByteDance publishes little about training data, safety evaluations or licence terms.
Prompts to try
Minimal vs high reasoning
I need to decide whether to rewrite a legacy Python codebase in Go or keep it in Python and modernise it. The team has five engineers, the service handles 8,000 requests per minute, and we have six months. Walk me through the key trade-offs and give me a recommendation. Use high reasoning effort.
Long document digest
Here is a 200-page technical specification. Identify every place where a requirement is ambiguous or contradicts another requirement. List each issue as a numbered item with the section reference, the problem, and a suggested resolution.
Video scene analysis
I'm attaching a short product demo video. List every distinct UI screen shown, the action performed on each screen, and any error states or loading delays visible. Format the output as a numbered log with timestamps if you can infer them.
Compare with
- Seed 2.1 TurboAPI-only model262,144 token context · $0.50/M input, $2.50/M output
- Seed 1.6API-only model262,144 token context · $0.25/M input, $2.00/M output
- Seed 1.6 FlashAPI-only model262,144 token context · $0.07/M input, $0.30/M output
- Seed-2.0-LiteAPI-only model262,144 token context · $0.25/M input, $2.00/M output
More text models from bytedance-seed
Questions people ask
- Can I run Seed-2.0-Mini locally?
- No. ByteDance Seed has not released the weights, so it is API-only and cannot be run on your own hardware.
- What does it cost and how long is the context?
- Pricing is $0.10 per million input tokens and $0.40 per million output tokens, with a 262,144-token (256k) context window.
- How does Seed-2.0-Mini differ from a full frontier model?
- It is explicitly a speed-and-cost optimised model comparable to ByteDance Seed-1.6, not a top-tier reasoner — the trade-off is lower latency and price for somewhat lower capability on hard tasks.
Captain's verdictA genuinely cheap, fast multimodal model with a long context and adjustable reasoning — sensible for production pipelines where cost per call matters.
Captain writes these notes from the model's own description and model card. Prices and context sizes are the provider's at the time of writing and can change.
Ranking badge
Updated Oct 22, 2026
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How this score adds up
This model has no popularity data from any source yet, so it carries a stand-in number below 7,000° that keeps the lists complete. It never ranks above a model with data. The breakdown appears here as soon as a source reports it.
Glossary: the terms used here
- Heat score
- One number for how much a model is used and wanted right now, from 0° to 9,999°. Every list on this site that says hottest is sorted by it and nothing else.
- Floor
- The 7,000° every model with real data starts from. The scale runs from 0° to 9,999°; the part below the floor is reserved for stand-ins (models without data), so that a model with data always ranks above one without. The floor is the same for everyone and decides nothing; the degrees above it are the measurement.
- Download-equivalents
- Everything the score treats as a download, added up: downloads themselves, each torrent seeder as 1,000, each AI Horde image and each run on a generation site as one.
- Log scale
- Counting by tens, not by ones: going from 1,000 to 10,000 downloads adds the same degrees as going from 10,000 to 100,000. It keeps giants from crowding out everything else.
- Likes
- Thumbs up and likes from every source, worth half a download each on the log scale.
- Adoption rate
- How many downloads a day a model gets right now, from the difference between two readings of a source. A quarter of the weight of the download term.
- Measured and estimated
- Measured means two readings of a source exist and the difference is real. Estimated means only today's total is known and the past is spread over the model's life; the site says which one it is showing.
- Seeder
- Someone who keeps a copy of the weights online for others to download over a torrent, or a hosted mirror doing the same. Each one counts as 1,000 downloads.
- AI Horde
- A volunteer network that generates images for free on donated computers. The number of images it has made with a model is public, and each counts as one download.
- Generation sites
- Sites that host their own copies of a model and let people generate with it, such as PixAI, SeaArt and Yodayo. Their downloads count like Civitai's and each generation run as one download.
- OpenRouter tokens
- A text model served through an API, such as Claude or GPT, has no weights to download, so its usage is measured in tokens: the pieces of text it read and wrote through OpenRouter, a gateway many apps use, over the last 30 days. Every 10,000,000 tokens count as one download. Source: OpenRouter (openrouter.ai/rankings), CC BY 4.0.
- Full model (checkpoint)
- The weights themselves, the file that generates on its own. It gets a bonus because add-ons depend on it.
- LoRA and embedding
- Small add-on files that steer someone else's weights toward a style, a character or a concept. They cannot run alone.
- Family and weights
- The base a model is built on, such as SD 1.5, SDXL, Flux or Wan. Every model inherits the release date of its family's weights.
- Age
- Degrees taken off for how long ago the family's weights came out: 150° a year, up to 750°. It keeps old downloads that had years to pile up from freezing the top of the list.
- Family boost
- A small, bounded nudge for LoRAs of a family this site is pushing forward right now, always listed in the breakdown.
- Stand-in score
- A stable number below the floor given to a model no source reports on yet, so lists stay complete. It never ranks above a model with data.
- New! (grace period)
- A model less than 7 days old shows New! instead of a score and holds no place in the rankings. Its score is computed from day one, but a model one day old grows faster than anything, so without the wait the week's #1 would always be whatever came out yesterday rather than what held up for a week.
- Trend
- How a model's growth over a period compares with the typical model's growth over the same period. Faster than typical reads as a rise, slower as a fall.
- Typical model
- The middle of the catalog: half the models with a figure grew faster over the period, half slower.
Seed 2.0 Mini is a text model for the Text / LLM family listed on ModelTrends.ai, a read-only catalog of open source AI image, video and text models. Compare it with other Text / LLM models and check its heat score to see how it is trending.
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