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squad 0.8B · Labqoat
Make local choices for games, web-app testing, multilingual tasks and documents. This revision changes the underlying model as well as adding specialists, so retest existing workflows before upgrading.
First reviewed 2026-10-23 · Checked 2026-11-03. The launch date has not been confirmed.
We still need comparable independent tests. You can compare the confirmed features and limits below, but we cannot yet give this version an Agent Fit or Practical Value rating. Test it on records with known answers, including cases that should go to a person.
What you can use today
You download the model files and supply the hardware and software to run them. Capacity is the amount of information a request can contain; settings and available memory can limit what fits.
Exact download ID: labqoat/squad-0.8b. The publisher revised its web-app specialist again and reports improved tests. These remain publisher evaluations; retest your own complete workflow before replacing an earlier download.
Apache 2.0-labelled weights, with CUDA, MLX and GGUF paths. The publisher still reports poor reliability on real web apps. Image input was not re-evaluated after the base model changed; test it before relying on it.
A validated task input limit is not established here. The base model’s configured capacity is not a guarantee for long testing workflows.
Output: Choices, yes/no probabilities and ordered scores; no written answer. Compare decision models, limits and input costs ↗
No hosted token price. Check whether its confidence numbers match actual success on your examples; they were adjusted using the previous version. The publisher’s tests do not establish independent performance, and a web-testing setting was chosen using those same tests.
Provider release and specifications ↗
Check availability and plans ↗
Test it before switching
Give each model the same examples, tools and definition of success. Record correct outcomes, response time, retries and total cost. Include the time and hardware needed to run it.
Compare caching and long-context costs ↗