Glimpse
Neural Network
Glimpse is a multimodal visual reasoning model by Elorian for analyzing images and video.
Specifications
- Input
- text, images, documents
- Output
- text
- Context size
- 262 144 tokens
- Max answer length
- 256 000 tokens
- Reasoning effort
- low, medium, high
- Capabilities
- reasoning, function calling
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works Glimpse?
Glimpse is a multimodal model from Elorian that reasons directly from images and video, rather than just text descriptions. It is designed for visual understanding and complex inferences about the physical world: the model parses frames, links objects, and builds a chain of reasoning based on what it sees. In BotHub, Glimpse works from Russia without a VPN or foreign card; payment is made with Russian cards in rubles on a pay-as-you-go basis—tokens do not expire, and no subscription is needed. With over 250 models in the same window, you can compare responses for a single task and switch without rewriting integrations. Access is via a unified OpenAI-compatible API, data is encrypted via AES-GCM, and for teams, we offer contracts, invoices, electronic document management, and an admin panel with limits. Useful for analyzing diagrams, blueprints, interface screenshots, assessing equipment status from photos, describing video clips, analyzing camera footage, or embedding visual reasoning into your own service.Frequently asked questions about Glimpse
You can use generated results for commercial purposes. You own all rights to the content you create. The only restriction: make sure your prompt does not include copyrighted third-party material. You are responsible for respecting the rights to any input data.
Glimpse works with text, documents, and images, can reason before answering, and call external functions. It is suitable for analyzing long materials, writing and refactoring code, debugging, STEM tasks, parsing contracts and reports, as well as agentic scenarios with tools.
Up to 256,000 tokens in a single response—this is a very large output, enough for a large technical document, a detailed code base analysis, or a series of related files. The context window is 262,144 tokens, so long input materials will also fit.
Yes, the reasoning mode is confirmed by our data. The model first builds a chain of thought and then formulates an answer—this significantly helps with math, logic, code debugging, and tasks where many conditions need to be synthesized at once.
Yes, both function calling and structured JSON output are supported. You can connect external tools, databases, and your own services to get predictable answers based on a schema. In BotHub, this is available via a unified OpenAI-compatible API, so integration does not need to be rewritten when switching models.
In addition to text, images and documents are accepted as input: you can send an interface screenshot, diagram, chart, PDF, or report and ask to analyze the content. There are no notes in our data regarding audio and video, so rely on support for text, images, and files.
We do not have data on languages, so we cannot provide guarantees—test it on your own tasks; it's faster than any promises. In BotHub, payment is pay-as-you-go for tokens, no subscription, access from Russia without a VPN or foreign card, and over 250 models are available in the same window for comparison.