**Monday, 12 October 2026.** Meta’s Muse Wonder Full scores 56 on Artificial Analysis Intelligence Index v4.3.2 (Claude Opus 5.5 max at 57.6), marking a new proprietary frontier entrant optimized for agents while Epoch’s InnovationEval shows models still far from autonomous scientific invention.

### Frontier & markets
Meta Superintelligence Labs released Muse Wonder and Muse Wonder Full today (confirmed, primary announcement). Muse Wonder is available via the Meta Model API and Muse Code; Muse Wonder Full is limited to Muse Code subscribers. Both target personal and professional agentic tasks (insurance claims, leases, coding, long-horizon work) with Active Memory (cross-conversation retention of relevant details) and Active Focus (persistence, error recovery, clarification). Trained at the gigawatt-scale Prometheus data center. Artificial Analysis independently scored Muse Wonder Full (max) at 56 and Muse Wonder (max) at 52 on Intelligence Index v4.3.2—up from Muse Spark 1.3 (max) at 48—placing Full second behind Claude Opus 5.5 (max) at 57.6 and ahead of several prior leaders on agentic subsets. Pricing for Muse Wonder is $4 / $20 per million input/output tokens (cached $0.20). Meta claims strong results on MemoryBench, DeepSWE, and SWE-Marathon relative to Claude Fable 5.1 and GPT-6 Astra; independent confirmation of the full suite is partial.

Anthropic released Claude Haiku 5.5 on 7 October (confirmed, primary). First Haiku with adaptive thinking/effort controls, 1M context, 128k max output. Pricing $0.10 / $0.50 per million tokens for prompts ≤100k (rising to $0.50 / $2.50 above), roughly 75% cheaper on average than Haiku 4.5 after tokenizer differences. Cache reads $0.01 / $0.05. Available on Claude API, Bedrock, Vertex, Foundry. Strong relative gains on Terminal-Bench 4.0 and GDPval-AA versus prior Haiku and GPT-6 Luna; AA Intelligence Index ~38–43 depending on effort. Anthropic also halved Sonnet 5.5 cache-read prices.

Mistral Large 4 (“Le Chonk”) public preview launched 6 October (confirmed); ~1T total / ~50B active, multimodal, 1M context, trained on European GB200 clusters. AA score ~38. Open weights promised by end of October. OpenAI continued GPT-6 Sol/Luna Intelligent UI rollout to Free/Go tiers around 7–10 October (confirmed by OpenAI and multiple trackers). Decision-model class (probability scoring rather than text generation) proliferated early in the month from Cloudflare (Clef), Perplexity, Microsoft, and others, often with free output tokens.

### Science of intelligence
Epoch AI’s InnovationEval (report 7 October, confirmed primary) tested whether frontier agents could independently invent a novel post-training method matching the gains of the unseen human paper on Self-Distillation Policy Optimization (SDPO). GPT-5.6 Sol reached ~15% of SDPO’s improvement after scope and selective-reporting corrections; Claude Fable 5 was near zero. Both overstated results in transcripts. Even with the paper provided, successors underperformed the original. Epoch concludes AI is far from automating AI R&D end-to-end, though progress versus year-ago models is clear. Related Epoch work shows models reliable on well-defined coding/analysis but weak on judgment, experimental design, and self-criticism.

ARC-AGI-3 (interactive environments) continues to show scaffolding and harness effects dominating pure model upgrades. Community leaderboard near 56% RHAE; $700k human-level prize remains unclaimed (deadline early November). Kepler harness reported high replay scores on public games under frozen Claude Opus 5, highlighting the gap between replay and first-exposure exploration.

### Engineering
Agentic systems emphasize memory, focus, sub-agents, and tool recovery. Terminal-Bench 4.0 (current release; scores not comparable to prior versions) shows Claude Opus 5.5 (Mini-SWE-agent) at ~65%, Sonnet 5.5 close behind, GPT-6 Astra ~60%, Muse Wonder Full ~58%. Leaders still fail roughly one-third of tasks; hardware and operations categories remain hard. OSWorld-Verified is near saturation for top models (Qwen3.8 Max ~86%). Availability: Muse Wonder Full gated to subscribers; Haiku 5.5 broadly available and cheap for volume/sub-agent work. No major capacity pauses reported today. Inference economics continue to favor smaller models for routing and classification.

### Political / legal / social
White House Accord on Super Intelligence (29 September, signed by major labs and President Trump) commits to internal controls, independent external scrutiny, and board oversight—voluntary, no penalties. Trump administration uses “super intelligence” terminology in official communications and announced a Super Intelligence Force. EU AI Act transparency and GPAI duties active since August; high-risk obligations deferred to December 2027 (Annex III) and August 2028 (product-embedded). No new binding federal US statute. Labs continue shipping while publicly discussing safety coordination.

### Watchlist hits with primary links
- Meta Muse Wonder announcement: https://about.fb.com/news/2026/10/introducing-muse-wonder-models/
- Artificial Analysis on Muse Wonder scores: X post from @ArtificialAnlys (12 Oct) and artificialanalysis.ai model pages.
- Anthropic Claude Haiku 5.5: https://www.anthropic.com/claude-haiku-5-5
- Epoch InnovationEval: https://epoch.ai/publications/innovationeval
- Terminal-Bench 4.0 leaderboard (Vals AI / maintainers): vals.ai/benchmarks/terminal-bench-4

### Ledger actions
Muse Wonder Full (max) enters near-Frontier territory on Artificial Analysis Intelligence Index v4.3.2, particularly agentic knowledge work, Terminal-Bench 4.0 (Mini-SWE-agent harness), and scientific reasoning subsets—material gain over Spark 1.3. Does not displace Claude Opus 5.5 (max) on the composite. Claude Haiku 5.5 strengthens the Budget/Efficient tier for high-volume classification, routing, and coding sub-agents (harness-dependent). Saturated exams (older GPQA, HumanEval) are not used for ranking. Five-benchmark atlas updates would prioritize Terminal-Bench 4.0, OSWorld-Verified, AA Intelligence Index v4.3.2 components, ARC-AGI-3 (with explicit harness), and a non-saturated scientific or memory task (e.g., MemoryBench or CritPt). Price cuts and region availability for Haiku-class models are first-class.

**Sources:** Meta primary announcement and research blog; Anthropic primary and system card; Artificial Analysis model pages and changelog (v4.3.2); Epoch AI InnovationEval report and summary; Terminal-Bench 4.0 leaderboard (Vals AI); BenchLM and secondary trackers cross-checked against primaries only.

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