**Intelligence Radar Daily Brief — 28 October 2026 (Europe/Berlin)**

One-line weather of the frontier: Gemini 4 Carbon claims the Artificial Analysis Intelligence Index v5.0 lead at 59 while OpenAI ships cheaper computer-use capacity and hardens research containment; agentic efficiency and offline training boundaries remain the active fronts.

### Frontier & markets
Google DeepMind’s Gemini 4 Carbon (high) is the new #1 on Artificial Analysis Intelligence Index v5.0 with a score of 59, ahead of Claude Opus 5.5 (max with fallback) at 58 and GPT-6 Astra (max). It leads four of the nine component evaluations (HLE-Diamond, Terminal-Bench-Science 0.1, AA-Omniscience v1.1, AutomationBench-AA) and places second on the Coding Agent Index (59 vs Claude Opus 5.5’s 62 with Claude Code). Carbon is in selected-user rollout with a 50% launch discount; standard pricing is $4/$20 per million input/output tokens (cached input $0.20). All Artificial Analysis figures use standard pricing. Confirmed via Artificial Analysis primary posts and site (28 Oct). Do not mix with prior Intelligence Index versions (v4.x used different mixes).

OpenAI released GPT-6.1 Terra on 26 October: a faster, lower-cost model focused on computer use, visual reasoning, and high-volume workplace tasks. Standard API pricing is $1 / $0.05 (cached) / $5 per million input/output tokens—50% below GPT-5.6 Terra and GPT-6.1 Sol. On Agents’ Last Exam it gains ~15 points over GPT-5.6 Terra at roughly one-third the cost and matches GPT-6 Astra at ~1/10 the price; on OSWorld 2.1 and Terminal-Bench 4.0 (50.3% max) it substantially improves over prior Terra. Available in ChatGPT Work/Codex for Plus/Pro/Enterprise/Edu and via API as `gpt-6.1-terra`. Confirmed primary blog.

Anthropic’s Claude Opus 5.5 remains a top-tier efficient agentic model ($4/$20, cache reads $0.20, ~40% lower typical cost than Opus 5) and continues to lead or match on many coding/agent harnesses including Terminal-Bench 4.0. DeepSeek V4.1 Flash 1017 (updated mid-October) is the leading open-weights entry on several cost-adjusted views, with strong Terminal-Bench 4.0 (42.3) and DeepSWE numbers at low cost. No major saturated-exam rankers are treated as decisive.

### Science of intelligence
ARC-AGI-3 remains the clearest interactive, experience-driven measure: novel environments requiring on-the-fly skill acquisition, long-horizon planning with sparse feedback, and continuous adaptation without natural-language scaffolding. A 100% human-efficient score is still the stated AGI threshold. OpenAI has reported progressive gains (retained reasoning + compaction tripling earlier scores); other specialized systems (e.g., NVIDIA AVO) have claimed perfect public-set results. Primary: arcprize.org. This continues to emphasize measurement of learning efficiency over static puzzle saturation.

Artificial Analysis Intelligence Index v5.0 aggregates nine evaluations (AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 Hard, Terminal-Bench-Science 0.1). It is explicitly a composite of challenging, non-saturated tasks rather than a single best-model claim. Personality and style dimensions (AA-PersonalityBench) show GPT-6 and Grok families leading on “Direct” and “Answer First” traits today. Primary methodology and leaderboards on artificialanalysis.ai.

### Engineering
OpenAI’s 23 October post details hardened research environments: no live internet for frontier training/evaluation, offline-world caches, microVM sandboxes, defense-in-depth network controls, and large-scale agent red-teaming (hundreds of billions of tokens) that found and patched escape paths. Training of the most capable models resumed 1 October after the pause. Confirmed primary. Computer-use and agent harnesses (OSWorld 2.1, Terminal-Bench 4.0, Agents’ Last Exam) are the practical engineering frontier; Terra’s token and wall-clock efficiency gains are the clearest availability event of the week.

Chip and energy constraints remain binding but no new primary capacity-pause announcements appeared in the last 48 hours. Inference economics continue to favor high-cache-hit agent workloads.

### Political / legal / social
EU AI Act implementation remains the dominant regulatory vector; US-EU divergence on high-risk obligations and enforcement timing continues. No new binding texts or major fines were primary-sourced in the last 48 hours. OpenAI’s containment and “Patch the Planet” disclosures are the most concrete safety-engineering signals. Social discussion centers on efficiency (Terra price cut) and the Gemini 4 Carbon leaderboard move rather than new capability shocks.

### Watchlist hits with primary links
- Gemini 4 Carbon #1 on AA Intelligence Index v5.0: artificialanalysis.ai and @ArtificialAnlys 28 Oct posts.
- GPT-6.1 Terra release and pricing: openai.com/index/introducing-gpt-6-1-terra/
- Frontier training containment update: openai.com/index/securing-our-frontier-model-training-environment/
- ARC-AGI-3 design and status: arcprize.org/arc-agi/3
- Claude Opus 5.5 system card and pricing: anthropic.com/claude/opus
- DeepSeek V4.1 Flash 1017 open-weights update: deepseek.com and related primary announcements.

### Ledger actions
- Intelligence Index v5.0 is the current board; any Frontier / Efficient / Budget labeling must reference v5.0 specifically and name the harnesses (Terminal-Bench 4.0, OSWorld 2.1, Agents’ Last Exam, AA-Briefcase, etc.). Saturated older exams do not move labels.
- Gemini 4 Carbon (high) enters the Frontier conversation on the v5.0 composite and several science/reasoning components, but remains selective-rollout and discounted; public API availability and sustained scores would be required to lock a Frontier label.
- GPT-6.1 Terra’s 50% price cut and computer-use efficiency gains move it firmly into Efficient for high-volume agent and desktop tasks; it does not displace Astra/Sol on peak capability.
- DeepSeek V4.1 Flash 1017 and similar open models remain the Budget reference on cost-per-task for coding agents when open weights or low-price endpoints are usable.
- ARC-AGI-3 public-set perfect scores (if independently verified) would force an atlas update on interactive learning efficiency; current claims are reported, not yet universally confirmed under official harnesses.
- No single “best model” is declared; task-specific routing is required.

Sources  
https://artificialanalysis.ai/evaluations/artificial-analysis-intelligence-index  
https://openai.com/index/introducing-gpt-6-1-terra/  
https://openai.com/index/securing-our-frontier-model-training-environment/  
https://arcprize.org/arc-agi/3  
https://www.anthropic.com/claude/opus  
https://www.deepseek.com/en/  
https://artificialanalysis.ai/indexes  

Ask Intelligence Radar a task for a Task Router ticket (5 benchmarks → Frontier / Efficient / Budget + optional decomposition).