Claude Opus 4.8 × BibiGPT
On 2026-05-28 Anthropic released Claude Opus 4.8 — a flagship model with a default 1M-token context window, up to 128k tokens of output, high reasoning effort by default, and high-resolution image input (long edge up to 2576px). A million tokens means an entire multi-hour video, a full podcast archive, or a whole playlist can sit in context at once. For BibiGPT users this lines up with an everyday strength: BibiGPT lets you ask questions across an entire long video, pull exact citations, and get deep summaries — and pairs high-res image input with its visual analysis of video frames.
Key facts (90-second read)
On 2026-05-28 Anthropic released Claude Opus 4.8 — a flagship model with a default 1M-token context window, up to 128k tokens of output, high reasoning effort by default, and high-resolution image input (long edge up to 2576px). Pricing is $5 input / $25 output per million tokens. A million tokens means a whole multi-hour video, a full podcast archive, or a complete playlist fits in context at once. For BibiGPT users the takeaway is practical: BibiGPT already lets you ask questions across an entire long video, pull exact citations, and get deep summaries of very long content — all from a single link.
Features
What is Claude Opus 4.8?
Released on 2026-05-28, Claude Opus 4.8 is Anthropic's flagship model with a default 1M-token context window, up to 128k tokens of output, high reasoning effort by default, and high-resolution image input (long edge up to 2576px). Pricing is $5 per million input tokens and $25 per million output tokens. The headline change: a whole multi-hour video or a full archive can sit in context at once.
1M-token context by default
A million tokens of context means an entire long lecture, a full podcast back-catalog, or a multi-video playlist can be held at once — so questions and summaries reason over the whole thing, not a truncated slice.
Deep reasoning, long answers
Reasoning effort defaults to high and output can run up to 128k tokens — enough for a thorough, structured deep-dive that walks through hours of material instead of a few-sentence skim.
High-resolution image input
It accepts high-resolution images (long edge up to 2576px), so detailed frames — slides, charts, dense diagrams pulled from a video — can be read accurately rather than as a blurry thumbnail.
Why this matters for BibiGPT users
Asking questions across an entire multi-hour video, pulling exact citations, and getting a deep summary of very long content is exactly what BibiGPT already does. Claude Opus 4.8's 1M context and high-res image input fit that workflow directly.
Q&A across a whole long video
With room for a multi-hour recording in context, BibiGPT can answer questions that span the entire video — connecting a point made in the first hour to a conclusion three hours later, instead of losing the thread.
Citation extraction you can trust
Ask where something was said and get the exact moment back. BibiGPT pulls precise citations from long content, so a claim in your notes traces straight to the spot in the source.
Deep summaries of very long content
Full lectures, complete podcast archives, and multi-video playlists become structured deep summaries — chapters, key arguments, and takeaways — from one link, without chopping the source into pieces first.
5 key facts (90-second read)
Headline facts from Anthropic's 2026-05-28 release of Claude Opus 4.8.
- 1
Released on 2026-05-28
Anthropic shipped Claude Opus 4.8 as its flagship model, available on 2026-05-28.
- 2
Default 1M-token context window
The defining change: an entire multi-hour video, a full podcast archive, or a multi-video playlist can sit in context at once, so reasoning covers the whole source rather than a truncated slice.
- 3
128k max output, high reasoning effort
Output can run up to 128k tokens and reasoning effort defaults to high — enough for a thorough, structured deep-dive through hours of material instead of a short skim.
- 4
High-resolution image input
It accepts high-resolution images with the long edge up to 2576px, so detailed frames — slides, charts, dense diagrams — are read accurately instead of as a blurry thumbnail.
- 5
Mirrors BibiGPT's long-content workflow
BibiGPT already lets you ask questions across a whole long video, pull exact citations, and get deep summaries — the same long-context strength, available today in the summarize workflow.
3 typical scenarios for BibiGPT users
Where a 1M-token context and high-res image input pay off in a real content workflow.
Q&A across a multi-hour lecture
A student or researcher loads a three-hour lecture into BibiGPT and asks questions that span the whole recording — linking a definition from the first hour to an example near the end — with exact citations back to each moment, no manual scrubbing.
Deep summary of a full podcast archive
A creator points BibiGPT at an entire podcast back-catalog. With room for the whole archive in context, it produces structured deep summaries — recurring themes, key arguments, and standout quotes — instead of one episode at a time.
Reading dense frames from a talk
A marketer turns a conference talk into notes. High-resolution image input lets BibiGPT read the slides and charts on screen accurately, so dense diagrams become usable takeaways rather than a blurry guess.
FAQ'S
Frequently Asked Questions
Ask us anything!
Ask questions across an entire long video with BibiGPT
Paste a Bilibili, YouTube, or podcast link once. BibiGPT reads the whole thing — even a multi-hour video or a full archive — answers questions across it, pulls exact citations, and writes a deep summary. No chopping the source into pieces, no losing the thread.