Gemini 2.5 Deep Think

Access, Pricing, and Limits

• Deep Think is only available via the $250/month Gemini Ultra plan, with no API access yet and unclear EU availability.
• Usage is heavily rate-limited: users report ~5–10 Deep Think prompts per day, each potentially running for 30+ minutes.
• Many find this “bizarrely uncompetitive” versus o3-pro and Grok 4 Heavy, especially given generous free Gemini tiers elsewhere.
• People criticize opaque quotas across AI subscriptions in general and want clearer, even if approximate, limits.

How Deep Think Likely Works (Parallel Reasoning)

• Commenters interpret “parallel thinking techniques” as multiple reasoning runs/agents in parallel whose answers are compared/merged.
• Idea: instead of 10k thinking tokens in a single chain, run ten 1k-token chains and aggregate, avoiding long-context degradation.
• Debate over cost: parallel agents are more expensive overall, but shorter sequences can be cheaper than extremely long single traces.
• Comparisons drawn to Grok 4 Heavy and OpenAI reasoning models; some argue benchmarks should compare against other “heavy” multi-pass systems.

Technical Comparisons and Alternatives

• Clarifications that this is not Mixture-of-Experts (MoE); MoE is about sparse parameter use, not multiple full agents.
• Discussion of alternative multi-path methods like Tree-of-Thoughts and planning/brainstorming phases before final generation.
• Local-LLM enthusiasts note you can emulate parallel agents on a single GPU (up to bandwidth/VRAM limits), potentially making this style cheaper at home.
• Tools like llm-consortium are cited as DIY “parallel reasoning” aggregators across many models.

User Experience with Gemini Models

• Mixed reports: some say Gemini 2.5 Pro/Flash have improved and excel at long-context reasoning, code design docs, and multi-step planning.
• Others say Gemini has degraded: more hallucinations, stubbornly wrong answers, language mix-ups, and overly aggressive code edits.
• Gemini CLI is seen by some as weaker than the raw model due to agent behavior; careful structuring (requirements → specs → plans → code) can make it powerful.

Adoption, Ergonomics, and Broader Concerns

• Many dislike aggressive Gemini promotion in Workspace and Android; comparisons made to Copilot nagging.
• Worries that ultra-slow, ultra-expensive “deep thinking” moves cost in the wrong direction, even if they help benchmarks.
• Some see slow “deep” models as a temporary bridge until faster models reach similar quality; others think a separate class of slow, high-stakes “supermodels” may persist.