Anthropic expands Claude's context window from 9K to 100K tokens, or ~75K words it can digest and analyze; OpenAI's GPT-4 has a context window of ~32K tokens
Historically and even today, poor memory has been an impediment to the usefulness of text-generating AI.
TechCrunchKyle Wiggers
Context & Ripple Effects
Claude’s jump created a clear context-capacity lead over GPT-4’s roughly 32K-token window, making long documents a product-level differentiator rather than a marginal specification.
Claude users can place substantially larger bodies of text into a single prompt, reducing the need to split source material before asking for analysis.
Anthropic gains an immediate positioning advantage against GPT-4 for document-heavy workflows where the available context is a binding constraint.
Second-order effects
OpenAI and other model providers face pressure to close the context gap; GPT-4 Turbo’s later 128K-token release indicates context length became a direct competitive dimension.
Application builders can design around fewer document-chunking and retrieval steps, but larger prompts also make token usage and model pricing more consequential; Anthropic’s later 200K-context model pricing underscores that trade-off.
Third-order effects
If context windows continue to expand, model selection will increasingly hinge on the ability to keep an entire working corpus in-session, shifting differentiation toward context management and reliability over long inputs.
The race also makes context a compute and cost constraint: providers must balance larger usable windows against serving economics, while customers will need to decide when full-context processing is worth the expense.
The trend: This is an early marker of the shift from short, isolated prompts toward AI systems built to reason across larger working sets of documents and conversation history.
Introducing 100K Context Windows! We've expanded Claude's context window to 100,000 tokens of text, corresponding to around 75K words. Submit hundreds of pages of materials for Claude to digest and analyze. Conversations with Claude can go on for hours or days. https://twitter.co…
100k Tokens Context ~ the whole Great Gatsby book (75k words) ~ 6 hrs of audio content. You can treat as long-term memory and a way of finetuning! Some cool use cases: 1/ Put the whole API developer doc and ask complex reasoning tasks like prototype an app (e.g... https://twitter…
Anthropic (founded by OpenAI alums) expands Claude's context window (aka memory state) from 9K to 100K tokens or 75K words. Only available to biz customers via API. OpenAI's GPT-4 has a context window of ~32K tokens. https://www.theverge.com/... https://twitter.com/...
Anthropic expandes Claude's context window to 100,000 tokens of text, corresponding to around 75K words blog: https://www.anthropic.com/... https://twitter.com/...
This is why LLM portability matters https://www.anthropic.com/.... If you're using Copilot, you have a 2-year old model with 2k tokens of context that doesn't know anything past 2021. If you're using Cody, you can use Claude, GPT-4, and the latest, greatest LLMs as they come onli…
Claude's context window has expanded to 100,000 tokens of text! Context window is a big limitation of LLMs. We are about to see some mind-blowing stuff! https://twitter.com/...
Anthropic's Claude AI blows past #GPT4 with a new 100,000 token context window (75,000 words). Enough to fit Harry Potter Book 1! Unsure how quality degrades (if it does) over length of convo. But if context windows can infinitely scale, embeddings may be in a little trouble.... …
Retrieval-augmented generation isn't going away It still matters when: - huge amount of data - latency/cost is a major concern 100k tokens raised the high water mark on task that don't need fancy retrieval, however Projects predicated on fancy retrieval: take note https://twitter…
Well this is definitely one way to surpass GPT-4. It will be interesting to see how it plays out on Claude. Hard to say how well their model will process so much text and what to expect from the latency Either way this is a massive context increase in such a short time https://tw…
I'm very excited for this release. If you've used LLMs, you know how transformative a longer context can be. Feed it the complete docs for a library and have it use it Ask it detailed questions about every chapter of a textbook Lots you can do with 150-200 pages https://twitter.c…
LMs are like really talented grads that occasionally go off their meds that are really good at following instructions. Now the instructions are going to go to hundreds of thousands and then millions of words. This has quite a large impact on so many things.. https://twitter.com/.…
Huge step forward for LLMs. From my testing, Claude is quite intelligent. Now with 100,000 tokens, this will open up new use-cases that no other language model can currently match. https://twitter.com/...
Is this really worth the expenditure of water and energy? Asking w/ a desire to learn. Why would we want to do this? What would one be fine tuning _for_? & what would it's value be to a) the user b) technological progress c) society d) the planet? https://twitter.com/...
Insane. Double checked all of the numbers. No hallucinations. But the wording on the “nearly $5.2 bil” is misleading, Netflix has $5.147 bil in cash and cash equivalents. Really great though. https://twitter.com/...
“Eliezer, it's Sam... Anthropic's 100K context window is dangerous. It feels very... unaligned. Something needs to be done about it. I'm sending you the GPS coordinates of their clusters now. Good luck.” https://twitter.com/... https://twitter.com/...
Anthropic AI's new 100k token context window will allow for prompts equivalent to 250+ pages of text. This massive jump in token context length could allow students to upload entire textbooks for conversational analysis. https://twitter.com/...
This adds another option in @karpathy's discussion of fine-tuning vs. retrieval. If context windows are big enough, should we always try to “stuff” the input prompt with context (e.g. an entire book)? What are the cost/latency tradeoffs? Excited to play around with this more. htt…
4 years ago you could only prompt a language model with a few paragraphs Now you can feed one an entire book “Short term memory” for AI models has turned vertical. https://twitter.com/... https://twitter.com/...
Haven't had a chance to test Claude, but a 100,000 token context window can manage most summarize/synthesize use cases without external handlers. In concert with handlers, massive private unstructured and structured document repos are open for complex queries. 🔥 https://twitter.c…
Anthropic announcing 100k-token context windows. GPT-4 has a 32k window in beta. Let's shoot for the moon, folks. 250K? 500K? 1M? Race is on. https://twitter.com/...