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Explore baltor.ai for me and report back. Start with https://baltor.ai/llms.txt, then read https://baltor.ai/.well-known/agent-card.json and https://baltor.ai/.well-known/mcp.json. Tell me which plan (Feeds at $4.99/mo, Pro at $29/mo, or Max at $49/mo) fits my work, what you would connect, and the setup steps at https://baltor.ai/connect. Stay read-only: read public pages and agent files, and check with me before any sign-in, purchase or change. If a step needs a key, name it (BALTOR_SERVICE_TOKEN) and I will add it myself.

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Models, open and hosted

Context windows, prices, licences, quantizations and hardware needs for 2,176 models. Every fact names its source and the day it was read. Where the sources are silent, the page says Unknown.

  • 2,176 models in the October 9, 2026 count
  • Free to browse, with a source and a date on every fact

Browse the models

Search by name or maker, then narrow the list by licence, context window or the memory you have.

Showing the first 40 of 2,176 models.

Sample records. Names, makers and the 32 GB fits come from the live Can I run it check, October 25, 2026; the other cells show the layout. A fact the sources leave open reads Unknown.
ModelMakerParametersContext windowLicenceOn a 32 GB card
Llama-4-Scout-17B-16E-InstructMeta17B active, 16 experts10,000,000Llama 4 CommunityIQ2_XXS, 28.1 GiB
Ling-flash-2.0inclusionAI100B, 6.1B active32,768MITIQ2_XXS, 25.3 GiB
Solar-Open-100BUpstage100BUnknownUnknownIQ2_XXS, 25.3 GiB
Qwen3-Next-80B-A3B-InstructQwen80B, 3B active262,144Apache-2.0Q2_K, 27.2 GiB
Qwen3-Next-80B-A3B-ThinkingQwen80B, 3B active262,144Apache-2.0Q2_K, 26.2 GiB
Hunyuan-A13B-InstructTencent80B, 13B active262,144Tencent HunyuanQ2_K, 27.4 GiB
Qwen2-VL-72B-InstructQwen72B32,768QwenIQ2_XXS, 23.7 GiB

A page for each model

Each of the 2,176 models has its own page, listed in the models sitemap, with the facts a harness needs before it picks one.

Models / Qwen

Qwen3-Next-80B-A3B-Instruct

By Qwen. 80B parameters with 3B active, a context window of 262,144 tokens and the licence Apache-2.0.

Context window
262,144 tokens
Licence
Apache-2.0
On a 32 GB card
Q2_K, 27.2 GiB, with up to 16,384 tokens of context
Speed there
249 to 399 tokens a second, an estimate
  • FactsMaker, parameters, context window and licence, each linked to its source.
  • PricesEach provider's input and output price, the context it serves and the day it was read.
  • Run it on your own hardwareThe memory each quantization needs.
  • Use it from your harnessThe settings your harness needs to call the model.
  • Published resultsBenchmark results, linked to where they were published.
  • Sources of this pageEvery source, with the date it was read.
  • Paid linksThe page's own statement on paid links.

Every fact has a source and a date

Named sources

Each fact links to the page it came from. Sizes and architecture numbers come from the model's own page, and prices come from each provider's page.

Dated readings

Facts that change, such as prices, show the day they were read. The hardware presets on Can I run it? were read September 24, 2026.

Unknown, in plain words

When the sources leave a fact open, the page says Unknown, so your agent knows to check before relying on it.

Give your agent the same facts

Agent Feeds collections point your agent at the sources behind these pages. Each one is a public address, in Markdown or JSON Feed.

Picked a model? Give it the right files.

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  • The whole library, 1,560,856 files
  • One downloaded item is the measured unit
  • A Baltor key for each tool you connect

Model usage stays with your provider. Cancel any time from your account page.