Hey everyone! I just noticed that Claude Sonnet 4 has gotten a huge upgrade in their API. The context window has been bumped up to 1 million tokens, which is about 5 times more than what we had before.
This is pretty exciting news for anyone working with large documents or complex conversations. I’m wondering if anyone else has started testing this out yet? The increased context should make it way better for handling long research papers, entire codebases, or extended chat sessions without losing track of earlier parts.
Has anyone run into any performance changes or pricing differences with this new expanded context? Would love to hear your experiences!
I’ve been automating this exact problem for years. Instead of manually dumping massive docs or codebases into Claude and watching it crawl, I built workflows that handle it automatically.
The real power isn’t just having 1M tokens - it’s smart API orchestration. Preprocessing content, breaking down big tasks, running them in parallel, then putting results back together without losing context.
Take enterprise codebases like elizabeths mentioned. My workflows automatically pull out dependencies, sort modules, then feed Claude targeted chunks while keeping a knowledge graph of how everything connects. Way faster than dumping it all at once.
Same with legal docs or technical manuals. The system finds key sections, creates summaries, then only does detailed analysis where it matters. Better results, no more crawling near token limits.
Set it up once and it handles new documents automatically. No more manual chunking or token headaches.
I use Latenode for all this because it nails API orchestration and lets me build complex workflows without coding from scratch. Check it out: https://latenode.com
the pricing is key here. 1M tokens sounds awesome, but if it’s way more expensive, then what’s the use? also, how’s streaming performnce? does it lag when you hit that limit or is it still smooth?
Just migrated some ML projects to test the expanded context - results vary a lot depending on what you’re doing. For training data analysis, it’s great having full dataset context since the model won’t forget earlier patterns when you’re discussing later findings. But here’s what’s interesting - response quality doesn’t scale linearly with context size. Around 800k tokens, Claude gets scattered, like it’s overwhelmed by too much info. Sweet spot seems to be 70-80% of available context instead of maxing it out. Also noticed error rates bump up slightly with larger contexts - probably from trying to stay coherent across massive inputs. Still, this upgrade kills major workflow bottlenecks even if you don’t push it to the limit.
Been testing the expanded context window for about a week on large technical docs. There’s definitely a performance hit vs smaller contexts, but it’s manageable for most stuff. What really impressed me - the coherence across entire documents. I fed it a 300-page manual and it could pull specific details from early chapters when discussing later sections. Pricing scales with token usage though, so watch out if you’re processing tons of content regularly. For my legal document analysis work, this killed the need for complex chunking strategies that always missed important cross-references.
whoa, that’s insane! haven’t tried it yet but 1m tokens could be a game changer for my workflow. i’ve been struggling with chunking large datasets and this might solve that headache completely. anyone know if response times are still decent with such massive context?
Been testing this on enterprise codebases since yesterday - results are solid. Before, I had to chop repos into chunks and lost key relationships between modules. Now I can dump whole projects and ask about architecture or trace function calls across files without losing context. Memory’s way better too - it recalls details from hundreds of messages back. One downside: loading gets slow when you’re near that million token limit, especially first requests. Still worth it when you need full context for complex analysis.
I’ve been testing the old vs new context windows for academic research. The difference is huge for systematic literature reviews. Before, I had to juggle separate conversations for different paper groups and constantly copy-paste findings between sessions. Now I just dump entire bibliographies with abstracts and full methodology sections at once. Performance-wise, there’s a sweet spot around 600-700k tokens where quality peaks without bad lag. Go higher and you get diminishing returns plus slower processing. Pricing scales with input tokens, so I stick with smaller contexts for routine stuff. But for deep analysis where cross-document connections actually matter, the extra cost beats manually tracking relationships across multiple conversations.