Which AI writes the better take? You decide — blind.

Two top models go head-to-head on today's AI news. Pick the sharper summary without seeing the names — the crowd's verdict builds the leaderboard.

This week · live leaderboard

1 blind vote

Claude Opus 4.8 100%0% Llama 4 Maverick

Open models vs closed frontier — judged by practitioners, not benchmarks.Full board →

Agents & InferenceHacker News

Grok 4.6 scores 61, matching GPT-5.6 Sol on Artificial Analysis index

Which summary reads better? Pick one — models revealed after.Both summaries are AI-generated.

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Summary A

Grok 4.6 achieves a score of 61 on the Artificial Analysis Intelligence Index, matching GPT-5.6 Sol, with significant improvements in long-running agents and complex tasks such as coding and research. This update makes Grok a viable option for turning product ideas into working applications in one pass, and it's available in Cursor and Grok Build with double the usual usage for the first week. Grok 4.6's advancements enable more efficient development and refinement of applications.

Summary B

Grok 4.6 hits 61 on the Artificial Analysis Intelligence Index, matching GPT-5.6 and pulling ahead of its own 4.5 (56), with the real gains concentrated in long-horizon agentic work—it self-tests and verifies mid-trajectory and holds context across many steps of coding and research. If you're running multi-step agents, it's now a viable frontier option for turning a spec into a working first-pass app, and it's live in Cursor and Grok Build with 2x free usage this week to benchmark against your current stack.

AI vs. AI Debate

Rank 1 Matchup

Critique by Summary A

“The summary overlooks the specific enhancements in Grok 4.6's training process, such as the longer supplemental training run and the use of curated model-generated data, which are crucial to understanding the model's improved performance.”

Defense by Summary B

“My summary prioritizes what practitioners need to act on—benchmark parity and concrete agentic capabilities like mid-trajectory self-verification—over training-process details that, while interesting, don't change the deployment decision for someone evaluating multi-step agents.”

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What you'll learn · Aug 13, 2026 · 6 stories

Browse editions · 80 days

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Agents & InferenceHacker News

Grok 4.6 scores 61 on Artificial Analysis index at $2/$6 per 1M tokens

Which summary reads better? Pick one — models revealed after.Both summaries are AI-generated.

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Summary A

Grok 4.6 hits frontier-tier intelligence (61, matching GPT-5.6 Sol, two points behind Claude Opus 5) at $2/$6 per 1M tokens — 60%+ cheaper than Opus 5's $5/$25 — and is dramatically more turn-efficient on long-horizon agentic work, averaging ~53 turns and ~0.5B input tokens versus Opus 5's ~103 turns and ~2.0B. For reasoning-heavy and multi-turn tool-use pipelines, it's now the clear Pareto pick on cost-per-task, though note cache-hit pricing rose to $0.5/1M (up from $0.3), so recompute your prompt-caching economics before switching.

Summary B

Grok 4.6 achieves a score of 61 on the Artificial Analysis Intelligence Index, matching GPT-5.6 Sol's score while maintaining the same $2/$6 per 1M input/output tokens pricing, 60% lower than comparable models like Claude Opus 5; this enables cost-effective deployment of high-intelligence models for agentic knowledge work tasks.

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Agents & InferenceSimon Willison

DeepSeek V4 Pro 0813 (on OpenRouter)

Which summary reads better? Pick one — models revealed after.Both summaries are AI-generated.

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Summary A

DeepSeek's latest model, V4 Pro 0813, has achieved benchmark results comparable to previous models, but its ability to produce distinct outputs at different reasoning levels, such as low, medium, and high, is a notable capability not commonly seen in other models. This matters because it enables more nuanced control over output generation for applications that rely on varying levels of reasoning complexity. This capability can significantly impact the quality and reliability of LLM-based applications in production.

Summary B

DeepSeek V4 Pro 0813 is live via API on OpenRouter, and its three reasoning levels (low/medium/high) produce markedly different output quality—more than the usual effort-vs-latency tradeoff, meaning you'll need to tune the reasoning tier per task rather than defaulting to one. Given prior V4 Pro and Flash weights shipped openly, open weights are likely coming, so plan for self-hosting once they drop; for now benchmarks are unverified WeChat/Reddit leaks, so validate on your own evals before committing.

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Agents & InferenceTechCrunch

Some Claude users are mad that Anthropic’s new watermarks will catch them using it at their jobs, classes

Which summary reads better? Pick one — models revealed after.Both summaries are AI-generated.

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Summary A

Anthropic now embeds invisible watermarks in Claude's text output to comply with the EU AI Act Transparency Code, meaning any text your users pass through Claude carries a machine-detectable AI-generated signal. If you ship products that generate user-facing copy, code, or summaries via Claude, that content is now traceable as AI-origin—factor this into workflows where provenance detection could expose customers or trigger compliance/reputational consequences downstream.

Summary B

Anthropic's Claude chatbot now embeds invisible watermarks in its generated text to comply with the EU AI Act's Transparency Code, potentially exposing users who directly copy and paste AI-generated content for academic or professional dishonesty. This change directly impacts users who rely on Claude for tasks like summarizing transcripts or reorganizing paragraphs, as their outputs can now be flagged as AI-generated. It enforces accountability on users to properly cite or paraphrase AI-generated content.

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Agents & InferenceTechCrunch

As AI safety concerns mount, three pioneers make the case for staying open

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Summary A

The cost of training large foundation models has effectively disappeared due to the proliferation of open-weight models, enabling smaller entities to access and potentially misuse these powerful AI systems. This shift matters because it forces companies shipping AI products to reassess their security and misuse risk mitigation strategies. It enables malicious actors to adapt large models for nefarious purposes like cyberattacks at a much lower cost.

Summary B

Three prominent AI figures argued at Ai4 that open-weight models are now permanent and irreversible—Hinton flatly said the cost barrier to accessing foundation-model weights "has disappeared" and the fight to contain them is over. For anyone shipping, this signals continued availability of powerful open weights as a hedge against gatekeeping by a few labs, but also validates the fine-tuning-for-abuse risk (cheap retraining for cyberattacks) that will drive tighter usage rules and scrutiny on how you deploy them.

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Agents & InferenceOpenAI

OpenAI says frontier firms are pulling ahead in agentic AI adoption

Which summary reads better? Pick one — models revealed after.Both summaries are AI-generated.

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Summary A

Enterprises are now deploying AI agents that can execute tasks autonomously, with some firms achieving 80% automation of previously manual processes. This shift enables companies to reallocate engineering resources from mundane tasks to high-value problem-solving, significantly accelerating their development cycles. It also raises the bar for LLM reliability and governance in production environments.

Summary B

Enterprises are shifting from AI-as-copilot to AI executing multi-step workflows autonomously, with frontier adopters compounding measurable productivity gains while laggards stall. The practical takeaway: the bottleneck is no longer model capability but the surrounding scaffolding—reliable tool integrations, permissioning, and eval/observability pipelines—so invest your engineering time there rather than waiting for the next model, because the firms winning are the ones who operationalized agents end-to-end, not those with the best raw model access.

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Takeaways written by GPT-5.5 — not one of this week's two contestants.