Google makes Gemini 4 Carbon generally available
Paid users can access Carbon now; Google says it complements Argon but spends more tokens on hard tasks.
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Paid users can access Carbon now; Google says it complements Argon but spends more tokens on hard tasks.

Version 3.9 tests 59 agents on 327 challenges with a 1,000-second budget and deterministic grading.

Artificial Analysis ranks Fable 5.2 first at 65, ahead of Gemini 4 Carbon at 60.

Scale Labs says that is up from about 16 percent for Fable 5 in July and 2.5 percent for Manus a year ago.
Weights stay private; Meta prices it at $0.10/$0.50 per million tokens and about $0.136 per AA task.
With the model fixed, generated verifiers matched official verdicts better than seven harnesses on all four execution-scored benchmarks.
Artificial Analysis Intelligence Index v5.0.1 rates Claude Fable 5.2 at 65, and the model has no public API.
Best hard multi-select accuracy is 36.81%; Decider-V1.2 falls from 78.75% on world knowledge to 47.95% on constraint planning.
A judge trained on 3,788 videos tracks human ratings more closely than other open judges and matches Gemini 4 Argon.

Handshake says its AI training business reached $1 billion in gross annualized revenue about a year after launch.
The retrieved page is a cookie notice, so scores, methods and release status are not stated.
On 5,507 MME-Emotion items, Recognition rose 7.6-13.4 points, mostly from transcripts and prosody rather than evolved edits.
GPT-6 Astra in Codex, strongest of eight agents, succeeds on 34% of 456 core tasks.
On MindCube it raises Qwen3.5-27B from 52.70% to 71.14%, and to 98.25% when poses are annotated.
The open-source stack also lists 74 benchmarks and 62 metrics, separating execution evidence from scores.
A solver-judge-repairer pipeline revises tests and descriptions; nine model setups were evaluated and artifacts released.

Hy4 is API-only for now, scores 35 on Artificial Analysis, and trails Kimi K3.1 Pro by a wide margin.
On LLM serving design problems, spotting a simulator limitation did not guarantee the right design, and a review turn added new errors.
Across 1,029 tasks from 387 experiments, refined artifacts also cut mean API spending by 19.7%.

Opus 5.5 leads across 206 simulated tasks; 98 percent of cyber runs broke a behavioral rule.
In 150 sessions no run passed; agents surfaced only 54-64% of untold details, and experts blamed them for 88.33% of failures.
Five open models follow number words only up to ten objects; SD3.5-M and FLUX.1-dev ignore counts beyond that.
Thirteen models correctly calculate elapsed time in 62.1% of requests but fail to apply constraints.
Best model solves only 26.47% of 204 tasks, showing challenges in linking visual evidence.
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