Gemini 4 Argon Leads All Frontier Models in Reasoning at 95.1 and Data Analysis at 85.2 — Last in Design at 73.7
The October 28 LiveBench 2.0 leaderboard puts Gemini 4 Argon in a contradictory position: it scores higher than any other model on reasoning (95.1) and data analysis (85.2), yet sits sixth overall at 79.3. The drag comes from its design category score (73.7), which is 8.3 points below the next-lowest model, and its agentic score (61.2), which leaves it near the bottom of that dimension.
The pattern is not accidental. Google launched Gemini 4 Argon through its Fairwind Program — a phased rollout aimed at cybersecurity defenders. The model was built and positioned for analytical and forensic workloads, not general agentic orchestration or design generation.
Leaderboard, October 28 Snapshot
| Model | Overall | Reasoning | Data Analysis | Design | Agentic | $/task |
|---|---|---|---|---|---|---|
| Claude Fable 5.1 | 85.7 | 91.7 | 80.3 | 83.4 | 77.9 | $6.904 |
| GPT-6 Astra | 85.6 | 92.7 | 83.0 | 82.0 | 77.5 | $4.548 |
| Claude Opus 5.5 | 85.1 | 92.2 | 80.3 | 86.9 | 72.8 | $4.291 |
| GPT-6.1 Sol | 84.1 | 92.6 | 82.7 | 82.1 | 68.7 | $0.761 |
| Claude Sonnet 5.5 | 80.0 | 91.6 | 59.5 | 85.0 | 63.4 | $3.718 |
| Gemini 4 Argon | 79.3 | 95.1 | 85.2 | 73.7 | 61.2 | $2.073 |
| GPT-6.1 Terra | 77.8 | 88.7 | 82.3 | 82.3 | 48.6 | $0.665 |
LiveBench 2.0 generates fresh tasks each run to resist contamination. Scores can shift run to run.
Reasoning and Data Analysis: Where Argon Leads
On reasoning, Argon’s 95.1 is 2.4 points clear of GPT-6 Astra (92.7) and 3.5 points ahead of Claude Fable 5.1 (91.7). GPT-6.1 Sol (92.6) and Claude Opus 5.5 (92.2) sit in between. The gap between Argon’s reasoning score and its overall score is 15.8 points — the largest such gap in the table.
On data analysis, Argon’s 85.2 leads Astra (83.0), GPT-6.1 Sol (82.7), and GPT-6.1 Terra (82.3) by 2–3 points. Claude Sonnet 5.5 sits at 59.5, the only model with a meaningful deficit in this category.
Design: The Floor
Every other model in the top seven scores between 82.0 and 86.9 on design. Argon scores 73.7 — an 8.3-point gap to the field’s floor. Claude Opus 5.5 leads design at 86.9, with Claude Sonnet 5.5 second at 85.0.
Design tasks on LiveBench 2.0 involve agentic UI and layout generation work. Argon’s phased release trajectory has focused on security and analytical tasks. There is no indication Google is optimising it for design workloads.
The Cost Angle for Analytical Buyers
For workloads that are reasoning-heavy or data-analysis-heavy, Argon’s cost efficiency is notable. At $2.073 per successful task, it is less than half the cost of GPT-6 Astra ($4.548) and Claude Opus 5.5 ($4.291) — two models that score lower on both reasoning and data analysis. GPT-6.1 Sol and Terra are cheaper ($0.761 and $0.665 respectively), but their reasoning scores (92.6 and 88.7) and data analysis scores (82.7 and 82.3) both trail Argon.
For teams running scientific analysis, financial modelling, or structured data tasks, Argon is currently the highest-performing model in both relevant categories at a middle-tier price point.
What It Means for Agentic Deployments
Argon’s agentic score of 61.2 places it second-last among the models shown, ahead only of GPT-6.1 Terra at 48.6. That is a material consideration for users building multi-step agentic pipelines. The model that leads on reasoning will not carry that lead when orchestrating complex tool-use sequences. For agentic workloads, Claude Fable 5.1 (77.9) and GPT-6 Astra (77.5) remain the benchmark leaders.
The specialisation visible in Gemini 4 Argon’s LiveBench 2.0 profile reflects a deliberate positioning choice by Google: strong analytical performance for security-adjacent use cases, not a bid for general agentic supremacy.