#ContextWindows
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temprhq.io
Start with the languages your customers use. Compare model capabilities, keep context windows that fit a full support thread, then estimate monthly token spend from expected volume. #ContextWindows #TokenCostEstimates
AI Model Catalog: Pricing & Context Windows | Tempr
431 models with reference pricing and context windows. Bring your own key and use any of them in Chat or the CLI, and all but the OpenRouter rows in the Gateway.
temprhq.io
November 9, 2026 at 3:24 PM
temprhq.io
Will your real prompt fit the context window? Tally input tokens, leave room for the expected output, then price only models that can handle the whole request. Tempr lists context windows and reference pricing across 442 models. #ContextWindows #LLMPricing https://temprhq.io/models
October 22, 2026 at 12:30 AM
temprhq.io
Your support bot gets a 90-page upload, but most requests are short. Tempr helps compare compact-model costs for routine prompts against reserving a larger context window for the outliers. Price the long-prompt exception. #LLMPricing #ContextWindows https://temprhq.io/models
October 13, 2026 at 3:29 AM
temprhq.io
Before: compare context limits. After: compare the room left after your usual document + instructions. Subtract that prompt’s token count from each model’s context window. Tempr lists windows for 442 models. #ContextWindows #AIModelCatalog https://temprhq.io/models
October 12, 2026 at 3:32 PM
temprhq.io
Budget context for the review too. A long doc shares the window with your instructions and the response. Compare context windows, not just prices, to find a model with room to work. #ContextWindows #LongContextPricing https://temprhq.io/models
October 11, 2026 at 10:44 PM
temprhq.io
2 providers. 1 task. A ready backup.
For each task, note the primary + backup model/provider, context fit, reference cost, and switch trigger. Compare options in Tempr, then use your own keys. #LLMPricing #ContextWindows https://temprhq.io/models
October 9, 2026 at 3:03 PM
temprhq.io
Keep every model review on the same scorecard. For each Tempr shortlist, record provider, reference price, context window and capability fit—then test candidates on one shared task. #AIModelCatalog #ContextWindows https://temprhq.io/models
October 8, 2026 at 8:46 PM
temprhq.io
442 models, one portable shortlist. Compare capabilities, context windows, and reference pricing across providers; your workflow may move from IDE to terminal to API, but those criteria can stay consistent. #AIModelCatalog #ContextWindows https://temprhq.io/models
October 8, 2026 at 7:04 PM
ciscodevnet.bsky.social
🚀 Agentic Impressions Episode 2 is LIVE! Build AI agents with persistent memory that never
forget and self-learning capabilities using SQLite + AGNTCY protocols.

Watch now: cs.co/633237ZhjJ


#DevNet #AgenticAI #NetworkAutomation #AGNTCY #ContextWindows
AGENTIC IMPRESSIONS Agent Memory Systems: Beyond Context Windows with Shereen Bellamy
November 14, 2025 at 7:33 PM
vktrnow.bsky.social
Macro-Reasoning Isn't Yet Here for AI Thinking: Why Enterprise AI needs to move beyond traditional context windows.
Continue reading... #aitechnology #contextwindows
Macro-Reasoning Isn't Yet Here for AI Thinking
Why Enterprise AI needs to move beyond traditional context windows. Continue reading...
www.vktr.com
July 13, 2026 at 1:59 PM
arstechni.ca
How AI coding agents work—and what to remember if you use them https://arstechni.ca... #largelanguagemodels #softwaredevelopment #machinelearning #contextwindows #Programming #ClaudeCode #vibecoding #agenticAI #Anthropic #AIagents #AIcoding #Biz&IT #AIwork #openai #Codex #AI
December 24, 2025 at 2:00 PM
nic221.bsky.social
The huge potential implications of long-context inference - Epoch AI https://epochai.substack.com/p/the-huge-potential-implications-of #AI #ContextWindows (interesting)
To be truly economically useful, LLMs will likely need "continual learning", the ability to keep acquiring knowledge over time.? For example, it's crucial for helping Al systems learn from mistakes or develop research intuitions. But current LLMs don't have much of a "memory" that they can retain over long chats or across multiple user interactions. Part of the issue is that LLM context windows aren't long enough to support much continual learning. For example, if you record work history using screenshots, 1 million tokens of context is only enough for Al agents to do computer tasks for up to half an hour - not nearly enough to acquire much tacit knowledge. 3But we can do a lot more with longer contexts: with 10 million tokens we get around six hours of computer use, and with 10 billion tokens this becomes eight months! More optimistically, if text and audio tokens alone are sufficient to represent work experience, even ~40 million tokens might be enough to acquire multiple months' But if these longer contexts are available, models can learn from previous examples in their context window. For instance, reasoning models have demonstrated some ability to correct their own mistakes in their chain of thought, and retaining these learned corrections in-context could help the model solve problems in the future. This broad approach of "continual learning with giant context windows and in-context learning" has been raised several times. For example, Aman Sanger alludes to this in a discussion with Cursor's team, and Andrej Karpathy has also sketched out how this might work on X:
September 20, 2025 at 2:13 AM
nic221.bsky.social
How to Fix Your Context https://www.dbreunig.com/2025/06/26/how-to-fix-your-context.html #AI #ContextWindows
Text Shot: let’s briefly recap some of the ways long contexts can fail: Context Poisoning: When a hallucination or other error makes it into the context, where it is repeatedly referenced. Context Distraction: When a context grows so long that the model over-focuses on the context, neglecting what it learned during training. Context Confusion: When superfluous information in the context is used by the model to generate a low-quality response. Context Clash: When you accrue new information and tools in your context that conflicts with other information in the prompt.
July 2, 2025 at 3:17 AM
nic221.bsky.social
Doing Real Work With LLMs: How to Manage Context https://www.jonstokes.com/p/doing-real-work-with-llms-how-to (this is so good) #AI #prompting #ContextWindows #search
Token window: orange square representing ungrounded sequence, blue squares representing grounded sequence
April 19, 2025 at 2:53 PM
nic221.bsky.social
Bigger isn’t always better: Examining the business case for multi-million token LLMs https://venturebeat.com/ai/bigger-isnt-always-better-examining-the-business-case-for-multi-million-token-llms/ #AI #ContextWindows (interesting thoughts on large context windows)
Bigger isn’t always better: Examining the business case for multi-million token LLMs
Are we unlocking new frontiers in AI reasoning, or simply stretching the limits of token memory without meaningful improvements?
venturebeat.com
April 14, 2025 at 4:59 AM
nic221.bsky.social
MiniMax unveils its own open source LLM with industry-leading 4M token context https://venturebeat.com/ai/minimax-unveils-its-own-open-source-llm-with-industry-leading-4m-token-context/ #AI #ContextWindows
Text Shot: MiniMax-Text-o1, is of particular note for enabling up to 4 million tokens in its context window — equivalent to a small library’s worth of books. The context window is how much information the LLM can handle in one input/output exchange, with words and concepts represented as numerical “tokens,” the LLM’s own internal mathematical abstraction of the data it was trained on. And, while Google previously led the pack with its Gemini 1.5 Pro model and 2 million token context window, MiniMax remarkably doubled that.
January 15, 2025 at 12:53 AM
nic221.bsky.social
You Exist In The Long Context https://thelongcontext.com/ #AI #ContextWindows
Text Shot: Just two years later, Google introduced a new version of Gemini that featured a context window of two million tokens. It took four years for the language models to increase their long-term memory by a factor of a thousand. But their short-term memory made a comparable improvement in just two years. Anyone who tells you that language models have plateaued since the introduction of ChatGPT is not paying attention to what has happened with the context window. And it turns out that many of the legitimate criticisms that were leveled against language models during the first wave of hype about them were unwittingly responding to how narrow the context window was in those early days.
December 2, 2024 at 3:33 AM
nic221.bsky.social
100M Token Context Windows https://magic.dev/blog/100m-token-context-windows #AI #ContextWindows
Text Shot: We have recently trained our first 100M token context model: LTM-2-mini. 100M tokens equals ~10 million lines of code or ~750 novels.
September 2, 2024 at 3:59 AM
Why It Matters
Context windows enable AI to process lengthy or intricate inputs, analyze structured data, and maintain continuity over multiple interactions. They’re foundational for applications in NLP, coding, and beyond.

#TermOfTheWeek #AI #ContextWindows #LLMs #DataProcessing #Technology
January 22, 2025 at 3:00 PM
aidailypost.com
Engineers are walking a tightrope between ultra‑short prompts and flooding LLMs with context. The new method aims to curb hallucinations while keeping chat assistants & code generators sharp. Curious? Dive in! #PromptEngineering #ContextWindows #Hallucination

🔗 aidailypost.com/news/enginee...
December 1, 2025 at 3:57 PM
william8000.bsky.social
New AI Model "Thinks" Without Using a Single Token youtu.be/ZLtXXFcHNOU?... #AI #LLM #ImplicitReasoning #LatentSpace #Tokens #ChainOfThought #Reasoning #ContextWindows #TestTimeComputation #RecurrentDepthApproach #ThinkingInContinuousSpace #Verbalizing
New AI Model "Thinks" Without Using a Single Token
YouTube video by Matthew Berman
youtu.be
February 13, 2025 at 7:25 PM
william8000.bsky.social
Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach arxiv.org/abs/2502.05171 #AI #LLM #ImplicitReasoning #LatentSpace #Tokens #ChainOfThought #Reasoning #ContextWindows #TestTimeComputation #RecurrentDepthApproach #ThinkingInContinuousSpace #Verbalizing
Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach
We study a novel language model architecture that is capable of scaling test-time computation by implicitly reasoning in latent space. Our model works by iterating a recurrent block, thereby unrolling...
arxiv.org
February 13, 2025 at 7:23 PM
hulio-ai.bsky.social
#AIBreakthroughs
🤖 Agentic AI: Models act with native computer skills.
📚 Context Windows: Instant access to vast data stores.
💊 Drug Design: AI predicts protein folds, cuts costs.
#AIBreakthroughs #AgenticAI #ContextWindows #DrugDesign
View in Timelines
March 12, 2026 at 2:01 PM
wellfunded.bsky.social
Long Context vs. Short Context Models: When Should You Actually Use One Over the Other?

Long context models cost more and run slower.

#LLMs #ContextWindows #AIPerformance #MachineLearning

https://wellfunded.news/articles/long-context-vs-short-context-models-when-does-long-context-win
Long Context vs. Short Context Models: When Should You Actually Use One Over the Other?
Longer context windows sound like a straight upgrade — but the tradeoffs in cost, latency, and data complexity tell a more nuanced story. Here's a practical breakdown of when long context models actually win, and when they don't.
wellfunded.news
July 3, 2026 at 7:00 PM
shopergamer.bsky.social
Claude ขยาย Context Window เป็น 1 ล้านโทเค็น คิดราคาเท่าเดิมตลอด

#ShoperGamer #ClaudeAi #Ai #ContextWindows #Feed
[Shoper Gamer] Claude ขยาย Context Window เป็น 1 ล้านโทเค็น คิดราคาเท่าเดิมตลอด โดย
โดย
www.blockdit.com
March 15, 2026 at 4:22 AM