Claude Opus 4.8 vs Gemini 3.1 Pro: premium reasoning or massive context?
Anthropic's refined flagship versus Google's context-window king.
Claude Opus 4.8 is Anthropic's latest flagship reasoning model with refined instruction following. Gemini 3.1 Pro is Google's premium model with an unmatched 2-million token context window. Opus 4.8 costs more than double, but the choice isn't just about price — it's about whether you need the biggest context window or the deepest reasoning.
Cost Comparison
Based on 100,000 input tokens (50% cached), 5,000 output tokens, and 100 requests.
Side-by-side specs
| Spec | Claude Opus 4.8 | Gemini 3.1 Pro |
|---|---|---|
| Input Cost (per M) | $5.00 | $2.00 (better on this spec) |
| Output Cost (per M) | $25.00 | $12.00 (better on this spec) |
| Cached Input (per M) | $0.50 | $0.50 |
| Context Window | 500K | 2M (better on this spec) |
How they differ
Claude Opus 4.8 costs $5.00 per million input tokens and $25.00 per million output tokens, with a 90% caching discount. Gemini 3.1 Pro (<=200k) costs $2.00 per million input tokens and $12.00 per million output tokens, with a 75% caching discount. Gemini's 2M context window dwarfs Opus 4.8's 500K. For long-document analysis, RAG with massive context, and multimodal tasks, Gemini's architecture has native advantages. Opus 4.8 counters with deeper reasoning, better instruction following, and Anthropic's mature tool-use ecosystem.
Verdict
Gemini 3.1 Pro for workloads that need huge context windows, multimodal processing, or the lowest price per token in the premium tier. Claude Opus 4.8 for tasks where reasoning depth is the primary requirement and you're willing to pay a premium for Anthropic's best model.
Which should you pick?
Choose Claude Opus 4.8
Deep reasoning tasks, complex code synthesis, multi-step planning, and workloads where instruction-following precision matters more than context size or per-token cost.
Choose Gemini 3.1 Pro
Massive context workloads (up to 2M tokens), multimodal processing, long-document analysis, and teams prioritizing cost efficiency in the premium tier.