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The 29× cost gap between frontier models with a 3-point score spread changes the economics of choosing a model for scientific agent workloads. Teams building lab-automation or biotech analysis tools can use the per-task cost and turn counts to pick models that fit their budgets. The 19-point deficit of open weights models like Kimi K3.1 Pro also marks a concrete ceiling for on-premise deployments.

Official accountArtificial Analysis@ArtificialAnlysSource

The striking detail is the cost-efficiency: at US$0.78 per Intelligence Index Task, Hy4 delivers Grok 4.7-level intelligence at a tenth of the price, which shifts the calculus for teams running high-volume agentic workloads. Developers building coding agents should weigh it against Claude Haiku 5.5, which scores higher but costs nearly twice as much; the 89% hallucination rate makes it a weaker pick for knowledge-retrieval tasks. API availability and USD pricing put it in direct reach of Southeast Asian startups comparing China-sourced models.

Official accountArtificial Analysis@ArtificialAnlysSource

For hour-long agent workloads, the cheapest model is not automatically the right one: Claude Opus 5.5 scores higher but costs twice as much per hour as Gemini 4 Carbon. Teams running long-horizon agents can use these per-hour cost and time figures to match model choice to budget and quality needs. The 55-minute spread among frontier models also suggests task duration, not just accuracy, should enter model selection criteria.

lmarena.ai@lmarena_aiSource

Hy4 matches a top open model on agentic performance at roughly a third of the cost per task, shifting the price-performance frontier for long-horizon agent workloads. Teams building finance, frontend, research or office automation agents can try it now via Tencent's preview page and should benchmark it against Kimi K3.1 Pro before committing. If Tencent open-weights the model as signalled, it becomes a strong free-tier candidate for cost-sensitive deployments across ASEAN and beyond.

lmarena.ai@lmarena_aiSource

The heavier weighting on Non-Hallucination and Agentic Tool Use signals which failure modes actually matter for professional work, and leaderboard positions may shift for models evaluated under the older v1 methodology. Hiring teams and enterprises selecting models for legal, finance or medical workflows can use the six indexes to match models to specific occupations rather than relying on a single aggregate score. Chinese labs' strong open-weight showings from Moonshot, MiniMax and DeepSeek also give cost-sensitive buyers credible alternatives to frontier closed models.

Official accountArtificial Analysis@ArtificialAnlysSource

A personality rewrite this large between consecutive versions means teams tuning prompts or evals around Kimi K3's behavioural profile may see different outputs after upgrading. Developers building character-driven agents or customer-facing assistants should re-run behavioural evaluations before switching to K3.1 Pro. The benchmark also offers a reusable method for tracking such shifts across any model lineage.

Official accountArtificial Analysis@ArtificialAnlysSource

The cybersecurity discussion cuts against the usual AI-lab framing, showing cyber experts and AI experts still read the same events very differently. Builders running open-weight models in production get concrete references to PHP2Shell, LiteLLM and trusted-access debates. The Anthropic junior-hiring segment also speaks directly to career planning for early-stage AI engineers.

Nathan Lambert@natolambertSource