Inside Gemini Deep Think: Google’s Gold-Medal Reasoning Engine with a 16-Minute Brain-Cycle

 When Google DeepMind quietly flipped the switch on Gemini 2.5 Deep Think, it wasn’t just another toggle in the Gemini app. The same enhanced-reasoning mode had already notched a gold-medal-level score at the 2025 International Mathematical Olympiad (IMO)—solving five of six notoriously brutal problems and tying the human cutoff for gold. That feat put DeepMind shoulder-to-shoulder with OpenAI’s own experimental “gold-IMO” model, announced the very same week .

What makes the IMO special?

Founded in 1959, the IMO pits six pre-university prodigies from each country against six problems spanning algebra, geometry, number theory, and combinatorics. Every question is worth seven points, so 42 is perfection; a score of 35 secured this year’s gold cutoff. DeepMind’s best 2024 system managed silver, but needed more time than the four-and-a-half hours allotted to humans. In 2025, Deep Think achieved the same result within the human time window, using only plain-language prompts instead of formal proof assistants .

Under the hood: parallel minds at work

Deep Think is Gemini 2.5 Pro running in a multi-agent “parallel thinking” mode. Instead of one chain-of-thought, it spins up dozens, scores them against intermediate goals, and fuses the strongest ideas into a final answer. Google says the approach boosts benchmark scores for math, logic, and coding, at the cost of far longer inference times .

A field test from the transcript

In the YouTube walkthrough, the host pastes a 2025 IMO geometry problem into Deep Think. The clock ticks 16 minutes before the first full token arrives—but the model nails the official solution, listing the only valid values of k as 0, 1, 3. A second experiment on an AIME-25 algebra question takes 13 minutes yet again lands the correct answer (204) with detailed derivations. The lesson: breakthroughs come after a coffee break, not in real time.

Beyond math: voxel temples and half-baked Angry Birds

Deep Think’s slow-burn genius extends to generative tasks. Asked to script a colorful 3D “Sala Thai” pavilion in Three.js, the model architected a fully navigable voxel scene—complete with stylized roof eaves—on the first pass. A tougher challenge—re-creating Angry Birds in Pygame—showed its iterative potential: the first build lacked obstacles, but a follow-up prompt produced pigs, wood, glass, and workable physics. Still, each refinement added another ten-plus minutes to the wait.

When speed matters more than brilliance

Because Deep Think withholds partial streams until it has weighed all candidate thoughts, users stare at a blank screen for up to ten minutes. Google engineers admit the mode “isn’t practical for everyday coding” unless you fire a prompt and walk away—then return to review the answer or receive a push notification. For everyday tasks, plain Gemini 2.5 Pro or Flash-Lite may offer better latency-to-value ratios.

How to try it—and what’s next

Deep Think is already live for Gemini Ultra subscribers inside the consumer app, and Google says an API endpoint will roll out in the “next few weeks” to AI Studio and Vertex AI . Once that lands, developers can add a “deep-think” flag to long-form reasoning jobs—think automated theorem proving, contract analysis, or multi-step coding agents.

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Bottom line: Gemini Deep Think proves massive parallel reflection can push public models into Olympiad territory, but it also shows there’s no free lunch—each extra IQ point costs time and compute. The next frontier won’t just be smarter LLMs; it will be orchestration layers that decide when a 16-minute think-tank is worth the wait and when a quick, cheaper model will do.

Gemini “Deep Think” Hits Gold-Medal Performance at the International Mathematical Olympiad

 

From Silver to Gold in Twelve Months

Last year, DeepMind’s AlphaGeometry and AlphaProof systems collectively solved four of six IMO problems, earning a silver-medal equivalent. In July 2025 the research team leap-frogged that result: an advanced version of Gemini running in “Deep Think” mode solved five of six tasks for 35 points—crossing the 2025 gold-medal threshold and setting a new AI milestone.

International coordinators graded Gemini’s written solutions using the same rubric applied to student competitors. According to IMO President Gregor Dolinar, the proofs were “clear, precise, and, in several cases, easy to follow”.

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What Makes Deep Think Different?

Technique	Purpose	Impact on Performance
Parallel Thinking	Explores multiple proof avenues simultaneously, then merges the strongest ideas.	Avoids dead-end, single-thread chains of thought.
Reinforcement-Learning Fine-Tune	Trains on curated theorem-proving and problem-solving data with reward signals for conciseness and rigor.	Raises success rate on multi-step reasoning challenges.
High-Quality Solution Corpus	Ingests expertly written IMO proofs plus heuristic “tips & tricks.”	Gives the model stylistic and structural templates for clearer presentation.

These upgrades let Gemini run longer “scratch-pads” internally while staying within a feasible compute budget—no multi-day cluster runs were required, unlike earlier systems.

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Benchmark Significance

• 35 / 42 points → comparable to a top-25-percent human gold medalist.

• Perfect scores on five problems; only one combinatorics task eluded the model.

• Order-of-magnitude speed-up vs. AlphaGeometry 2 + AlphaProof, which needed days of inference in 2024.

While specialized theorem solvers have mastered narrow domains, Gemini Deep Think is a general LLM—capable of chat, code, and multimodal tasks—now showing elite mathematical reasoning.

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Broader Implications

1. Curriculum Design for AI
   Gemini’s success underscores the value of domain-targeted reinforcement learning on top of large-scale pre-training.

2. Parallel Thinking as a New Primitive
   Instead of a single “chain of thought,” future models may default to branch-and-merge reasoning, akin to how human teams brainstorm proofs.

3. Human–AI Collaboration
   DeepMind notes the technique could become a “proof assistant” for mathematicians—surfacing lemmas or counter-examples at gold-medal quality within minutes.

4. Educational Outreach
   Publishing the solutions provides a free study resource for aspiring IMO contestants and teachers, potentially leveling the global playing field.

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Limitations & Next Steps

• Interpretability: Despite clearer written proofs, the internal decision tree remains opaque—researchers are now probing why certain branches survive the merge.

• Generalization: Performance on under-represented areas (e.g., functional equations) still lags; future training will widen topic coverage.

• Trust & Verification: Formal proof checkers like Lean are being integrated to machine-verify each Gemini output before publication.

DeepMind plans to open selected Deep Think capabilities via its Gemini API later this year, with safeguards to prevent misuse in academic competitions.

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Key Takeaway

Gemini Deep Think’s gold-medal performance doesn’t just raise the bar for AI mathematics—it redefines what general-purpose language models can achieve when armed with structured parallel reasoning and tailored RL training. The achievement brings researchers a step closer to AI systems that can tackle longstanding open problems and act as partner mathematicians rather than mere calculators.