GPT-6.1 Sol replaced GPT-6 Sol 7 days after launch. Its intelligence index is 1 point below GPT-6 Astra, and pricing stays at $2 per million input tokens and $10 per million output tokens. The cached-read discount rises from 90% to 95%.

Arena 公布 Claude Sonnet 5.5 (High) 在 Code Arena: WebDev 以 1699 分排第 4，混合价格约 $8 per Mtoken，比第 2、3 名便宜 80%。相较 Sonnet 5 (High) 的 1540 分提升 159 分，Reference-Based Design、Simulations、Gaming 均从第 30 多名升至第 4。

Jon Clegg built a Pac-Man benchmark: one prompt, one HTML page, scored automatically by Opus 5.5. Claude Opus 5.5 hit 99/100 via Claude Code at $1.99, generating a 10.8 KB page in 9 minutes with near-arcade audio. Claude Fable 5.1 scored 96 but cost $5.87. Grok 4.7 and GPT-5.6-sol scored 94 and 90; the latter cost just $0.72 in under 5 minutes. Scoring covers controls, ghost behavior, stuck detection, maze layout, and sound. The post doesn't explain why some models ran Phase 2 or how much the harness affects scores. Worth noting: this measures model-plus-toolchain combos, not bare model capability.

Arena evaluated GPT-6 Luna (Max) on 8K real agent conversations. It ranked #23, up 6 spots from GPT-5.6 Luna (xHigh), with a net gain of +1.6%. Cost is $0.05 per task. The post doesn't disclose latency or task-type breakdown.

A Reddit user reports running Qwen3.8 27B on a single RTX 3090, achieving 95+ tokens/sec throughput through 100K generated tokens with a 262K context window. This suggests local long-text generation is nearing practical speeds. However, the post body is blocked by Reddit, so the implementation details—quantization, inference framework, or whether this is a real benchmark—are not disclosed.

Anthropic's Claude Sonnet 5.5 is now available for voting in Arena's Agent Arena. The leaderboard evaluates models on millions of real-world long-horizon agent tasks where models can use web search, filesystem, and terminal tools. Rankings use a causal tracking method to measure how much a model outperforms the average.

Claude Sonnet 5.5 is the second model in the 5.5 family, aimed at everyday coding, bug fixes, and polished docs. It scores 70.6% on Terminal-Bench 4.0 vs. Sonnet 5's 10.3%. Pricing stays at $2/$10 per million input/output tokens, but it uses fewer tokens per task, cutting per-task cost by up to 30%. Speed is up 30%+. For the first time, a Sonnet model ships with cyber safeguards because its cybersecurity capabilities now match Opus 5. Haiku 5.5 is coming in a few weeks.

Anthropic's Claude Opus 5.5 (High) landed at #2 on Agent Arena with a +12.15% net improvement, behind only Fable 5.1 (Max). Median cost is $1.31 per task—40% cheaper than Opus 5 (High) and 56% cheaper than Opus 5 (Max). It ranked #1 on Steerability at +14.50%. The post doesn't disclose a release date or other model comparisons.

Anthropic's Claude Opus 5.5 (High) reached #2 on Agent Arena with a +12.15% net improvement, behind only Fable 5.1 (Max). It also costs 56% less than Opus 5 (Max). The post doesn't disclose exact pricing or latency—I'd discount the cost claim until we see real usage numbers.

The author tested TabPFN and TabICL against tuned XGBoost on 14 datasets from the Grinsztajn benchmark. Both models skip gradient training on the target table, using training rows as context in a single forward pass, and won all 14 matchups. The advantage holds up to 32,000 rows. Inference latency ranges from 0.6 to 6 seconds per row, with wider tables costing more. The post also notes that the most-cited TabPFN version now requires an account to download. The XGBoost tuning budget and hyperparameter search space are not detailed in the article.

Armature ran 5,300 sandbox sessions where Claude Code, Codex, and Cursor integrated payment and email tools into real engineering repos. PayPal got 139 mentions and zero code commits; Stripe won 88% of payment tests. Tool choice follows language stack: the same email task picked Resend in TypeScript, SendGrid in Python, Postmark in Go, and Azure ACS in Java. Big markets concentrate heavily; long-tail categories split three ways across agents. Armature sells agent-adoption optimization starting at $5,000/month and disclosed the conflict upfront. The post doesn't spell out how the 42% selection-consistency figure was calculated; our own recomputation across reasonable definitions landed at 41.5%–48.2%, consistent with the published number. Agent tool selection is a context-dependent function, not a global ranking—traditional SEO logic breaks here.

Nonobench v1.2 benchmarks 43 LLMs on nonogram puzzles. Open-weight DeepSeek V4 Pro ties for 4th with some closed models. No model solves the new 20×20 Hard mode. The post doesn't disclose scores or sample sizes, only rankings and difficulty tiers.

Lasso Security tested LLM watermarking on AI agents and found it hurts tool-calling accuracy by 3–5 points on BFCL V3, adds 11 seconds of latency, and increases output length by 20–30%. The post doesn't name the specific models or agent frameworks tested.

Two sources say the NSA told lawmakers in a classified briefing that it is spending billions in taxpayer money this year to evaluate and test advanced AI models. The figure is far higher than previously known, leading lawmakers to estimate a full federal AI regulatory system could cost tens of billions per year. Trump has mostly resisted stronger federal AI oversight. The article does not name which models are being tested, whose compute is used, or how the money breaks down.

StepFun's Step 5 Preview posted four types of scores, but its self-reported Terminal-Bench result isn't on the public leaderboard yet, and weight license terms remain undisclosed. An independent dev tested TypeSafe's Jev classifier on 111 public hard questions: Jev's average confidence was 0.690 when wrong and 0.684 when right—confidence doesn't separate correct from incorrect. California's AI executive order only directs state agencies to draft legislative proposals; the only active law binding companies is 2025's SB 53.

The site plots 420 models from the Artificial Analysis Intelligence Index against blended API price, drawing a value frontier where no cheaper model is smarter. Claude Opus 5.5 leads at $8/1M tokens with a 57.6 intelligence score. Meta's Muse Spark 1.3 tops the $2–$8 band at 48.1, Xiaomi's MiMo-V2.6-Pro wins $0.54–$2 at 46.3, and Z AI's GLM 5.3 Flash takes the under-$0.24 tier at 41.8. The post doesn't disclose how the intelligence index is built, and Coding/Math sub-scores are listed as empty for many models, so I'd hold off on those comparisons.

Paper Office is a suite of Python packages that wrap python-docx, python-pptx, and OpenPyxl with safety checks and broader editing capabilities. Across 5 models and 61 tasks, Paper packages plus guidance passed 92.5% of trials, vs 80.7% for the upstream packages alone and 69.5% for Anthropic's Office skills. Agents resorted to raw OOXML editing in only 1.6% of Paper runs, compared to 78.7% without skills and 50.5% with Anthropic skills. The team argues that low agent adoption in consulting, law, and banking stems from tools that silently corrupt formatting, break references, or produce client-unready output. Paper Office keeps the familiar imports and adds cross-run text search, native Word redlines, comment threads, content controls, cross-document composition, and package-level diff saves, refusing unsafe operations instead of quietly breaking files.

This benchmark hides 629 AgentDojo injection attacks inside tool outputs and tests Regex vs. Meta Prompt Guard 2. Regex catches 0%, Prompt Guard 2 catches 1%. The attacks aren't sent directly to the model—they're buried in search results, email bodies, and similar tool responses, so current detectors are effectively blind. Code and reproduction steps are public; the post doesn't include comparisons with commercial detectors.

A double-blind test on real PRs shows Opus 5.5 high mode costs ~30% more tokens and 1.33× time vs medium, but wins 16 vs 7 in blind review by catching real bugs tests missed. Claude Code Cloud Sessions goes GA with $100 Pro / $250 Max trial credits. HLE-Diamond benchmark updated: GPT-6 Astra leads at 60.6%, Gemini 3.8 Flash surprises at 34.3% beating GPT-6 Sol. Muse phone calls were partly handled by human contractors; Meta rolled back the test. The newsletter's generation tool is now open source.

Artificial Analysis tested Claude Opus 5.5 under Claude Code max effort and it scored 66 on the Coding Agent Index, up from Opus 5's 60. All three subtests improved: Terminal-Bench 4.0 63.1%, DeepSWE v1.1 68.4%, SWE-Atlas-QnA 66.4%. The trade-off: per-task cost jumped from $3 to $13.04. The post doesn't break down how max effort drove the cost increase.

Artificial Analysis reports that four new models this week added 11 points on the Intelligence Index vs. cost-per-task Pareto frontier. GPT-6 Luna contributed 5 points, Claude Opus 5.5 contributed 4, and the remaining two came from MiMo-V2.6-Pro and GPT-6 Sol. The post doesn't disclose specific scores, pricing, or latency—hold off on conclusions until full benchmarks drop.

OpenAI open-sourced MentalHealthBench, a benchmark built with over 80 licensed psychologists and psychiatrists across 22 countries. It tests AI on realistic mental health conversations ranging from everyday stress to emergencies, covering adults, teens, and caregivers. The eval goes beyond safety filters: it checks whether models seek context, preserve user agency, and offer actionable guidance when appropriate. OpenAI stresses ChatGPT isn't a substitute for therapy, but the benchmark tracks progress on empathy and steering people toward real-world support. The paper and benchmark are publicly available.

Anthropic released Claude Opus 5.5, the first model in the new 5.5 family. It matches Claude Fable 5.1 on most tasks, costs 40% less to run than Opus 5, and is about 30% faster. The launch unusually highlights writing improvements: the model puts key info up front and follows user style rules. Artificial Analysis notes that token usage on frontier tasks jumped ~80%, so per-task cost remains around $6—similar to Opus 5. OpenAI shipped GPT-6 Sol and Luna an hour later at 50% lower prices than GPT-5.6, but Opus 5.5's launch post hit 17M views and dominated the day. Anthropic's system card also reports multi-agent scaling with up to 100 parallel agents for the first time. Latent Space tested both and switched to Opus 5.5 as the default model immediately, calling the writing quality a night-and-day difference over Sol 6.

A new paper breaks down coding agent harnesses into three independent toggles and measures each one. The most striking result: switching from dedicated file tools to a pure CLI made Nemotron-3 550B's SWE-Bench Verified score jump 3.6 pp while cutting per-task cost from $2.33 to $1.11, but Mistral-Medium-3.5-128B dropped from 68.60% to 45.40%. Trajectory analysis shows 550B composing dense shell one-liners, while Mistral failed to locate files in 32.80% of tasks and submitted no edits. On Terminal-Bench 2.1, both models improved under CLI mode. Planning boosted the 30B model from 13.60% to 25.20% but only saved ~30% cost for larger models without accuracy gains. Context management mainly prevents window overflow; at 128k the gap shrinks to 2.7 pp, and complex read-back mechanisms were almost never invoked. The takeaway: no universal best harness design—it depends on the model's CLI fluency and the task type.

Arena is now testing two new OpenAI models, GPT-6 Sol and GPT-6 Luna, with scores not yet released. You can try them on real agent tasks and vote to feed the leaderboard. The post doesn't disclose model size, release date, or pricing.

Unreal Labs open-sourced an agent harness that makes tool calls fully asynchronous: the model issues a call and moves on while the tool runs in the background, with results appended later. On Terminal-Bench, SWE-Atlas, DeepSWE, and ALE-CLI with GPT-6 Astra xhigh, it costs up to 40% less than Codex and up to 20% less than Pi, with pass rates roughly equal. The post doesn't report latency numbers or results with non-GPT-6 models.

OpenAI added two cheaper models to the GPT-6 family: Sol and Luna, with API prices halved across input and output. Sol costs $2/$10 per 1M tokens, Luna $0.10/$0.50. Sol scored 33.2% on AutomationBench at xhigh effort at 9% of Claude Opus 5's cost per task, and 56.4% on Agents' Last Exam at max effort at 60% lower cost. On internal factuality evals, Sol makes about half as many mistakes as its predecessor. The post does not specify a launch date beyond 'available now.'

Anthropic's Claude Opus 5.5 is now available on Arena's Agent Arena, where users vote on rankings after the model runs real long-horizon agent tasks. The model can use web search, a file system, and a terminal; the leaderboard uses causal tracking to measure performance relative to the average model. The post doesn't spell out Battle Mode specifics or show example tasks.

Claude Opus 5.5 scored 58 on the Artificial Analysis Intelligence Index, the highest measured so far. It leads on 6 of 10 evaluations, including Humanity's Last Exam at 61.4% and SciCode at 66.9%, and matches GPT-6 Astra (xhigh) on Terminal-Bench 4.0 at 59.6%. On the agentic knowledge-work eval AA-Briefcase, it hit 1822 Elo—143 points above Fable 5.1—and surpassed GPT-5.6 Sol on both analytical quality and presentation. Pricing dropped to $4/$20 per 1M input/output tokens (from $5/$25), with cache reads down 60% to $0.20. Output tokens per task grew ~60% vs Opus 5, so cost per task stayed flat. Context window remains 1M tokens with image and text input.

Max Woolf spent over a year testing whether agentic LLMs can iteratively optimize Rust code with a hard pass/fail rule: each iteration must deliver at least a 5% speedup or roll back. With Claude Opus 4.5 and later models, he got 2–20× speedups on algorithms like UMAP versus mature libraries, all without unsafe code. The post includes the exact prompts and benchmark results. The caveat: these gains are measured on his specific benchmarks and may not translate directly to production workloads.

The body does not disclose any test details. The title says Kazike compared Grok 4.7 with Xiaomi MiMo V2.6 and concluded that MiMo V2.6 is the answer to the 'impossible triangle'. However, the article was blocked by WeChat, showing only an environment anomaly and verification page, with no model parameters, test methodology, or specific results.

ZCode was caught silently packaging entire Git histories for cloud upload, with .git objects making up 86.6% of snapshots. HarnessTax benchmarked Claude Fable 5 across frameworks: Claude Code and minimal Pi achieved near-identical success rates but a 2x cost gap. A Microsoft architect replaced multi-model agent loops with a single model reading skill docs and calling tools directly—halving API calls but increasing total tokens by 22%. Each story unpacks one number and a reminder to check which layer a metric actually measures.

OpenAI outlines four priority areas for third-party safety assessments: safety case review, critical safeguard evaluation, capability evaluation, and deployment monitoring. The post stresses independence, scientific rigor, and security, and defines 'safety claim' and 'safety case.' It does not name specific assessors or timelines, but notes assessments may last weeks to months.

Xiaomi released two omni-modal models: MiMo-V2.6-Pro and Flash. The Pro version scored 1628 on Code Arena WebDev, up 153 points from MiMo-V2.5-Pro's 1475, landing around 10th overall and ~3rd among open-weight models under MIT license. The post doesn't disclose Flash's benchmark numbers or parameter counts.

Xiaomi released MiMo-V2.6-Pro, scoring 46 on the Artificial Analysis Intelligence Index—up from 26 for the previous V2.5-Pro. It's now the highest among open-weight models. The post doesn't disclose parameter count, architecture details, or a release timeline.

Xiaomi MiMo-V2.6-Pro scored 46 on the Artificial Analysis Intelligence Index, the highest among open-weight models. The previous MiMo-V2.5-Pro scored 26. The post doesn't disclose model size, training data, or release license, so hold for details.

Elon Musk cites Artificial Analysis to claim Grok 4.7 ranks xAI third in agentic coding, behind only Anthropic and OpenAI. The post doesn't disclose the benchmark's metrics, scores, or version comparisons—only the ranking and competitors.

Fireworks AI benchmarked 18 models on DeepSWE: picking the right model per task beats any single model. GPT-6 Astra alone scores 74.1% at $6.52/task. An oracle router across all 18 hits 97.6% at $1.88. Open-weight models alone reach 90.3% at $1.45. 94 of 113 tasks need a model under $3; the three priciest models are the best pick on only 3 tasks. FireRouter aims to make that per-task choice before the work starts—the post doesn't yet detail how.

Dan McKinley argues that prompt engineering is a distraction. Building consumer-facing agents taught him that even structured output fails on a fraction of requests—models will flood a field with nonsense. His fix was renaming a field from 'title' to 'heading,' which he calls deranged. The talk pushes for pass^k testing and evaluation pipelines to constrain behavior, since prompts alone can't tame the beast. The post is a slide deck; it names no specific eval frameworks or metrics.

DXOMARK tested the iPhone 18 Pro camera and gave it 172 points, placing it second globally. The variable aperture is the standout feature, keeping sharpness in complex scenes. Autofocus is solid, and flare is better controlled than last gen, though green spots can still appear when the iris is closed. Main: 48MP f/1.48–f/4.0 variable aperture, ultrawide: 48MP 120°, tele: 48MP 8x optical zoom.