Gemini 3 Deep Think: Reasoning Benchmarks & Complete Guide
Gemini 3 Deep Think scores 84.6% on ARC-AGI-2 and 3455 Elo on Codeforces. Full benchmark analysis vs Claude Opus 4.6 and GPT-5.2 with access details.
ARC-AGI-2
ARC-AGI-1
Codeforces Elo
Physics Olympiad
Key Takeaways
Google DeepMind released Gemini 3 Deep Think on February 12, 2026 — a specialized reasoning mode that achieves record-breaking scores across mathematics, science, and competitive programming benchmarks. The headline number: 84.6% on ARC-AGI-2, the benchmark designed to test genuine abstract reasoning rather than pattern recall.
Deep Think represents a different approach to AI capability improvement. Rather than training a larger model, Google is scaling inference-time compute — giving the model more time and resources to reason through problems before answering. The results suggest this approach has significant room to run, particularly for businesses already adapting to the Gemini 3 ecosystem.
What's New in Gemini 3 Deep Think
Deep Think builds on the Gemini 3 architecture with a reasoning layer that activates when the model encounters complex problems. Instead of generating a response in a single forward pass, Deep Think constructs internal reasoning chains, evaluates multiple solution paths in parallel, and verifies its work before producing a final answer.
Extended Reasoning Chains
- • Multi-step internal analysis before responding
- • Self-verification and error correction loops
- • Structured decomposition of complex problems
- • Chain-of-thought visible in API responses
Parallel Hypothesis Exploration
- • Multiple solution paths generated simultaneously
- • Best-of-N selection with consistency checks
- • Particularly effective for math and code problems
- • Configurable compute budget per query
The practical effect is a model that excels at tasks requiring genuine reasoning — mathematical proofs, competitive programming, scientific analysis — at the cost of higher latency and compute per query. Google positions this as complementary to standard Gemini 3 Pro, not a replacement.
How Deep Think Reasoning Works
Deep Think's architecture centers on inference-time compute scaling — a technique that allocates additional processing during response generation. This contrasts with the traditional approach of making models larger during training. The reasoning pipeline has three stages.
1. Problem Decomposition
When Deep Think receives a query, it first breaks the problem into sub-problems. For a math olympiad question, this might mean identifying the relevant theorem, determining the proof strategy, and planning the logical steps. This decomposition happens in the model's internal reasoning chain before any output is generated.
2. Parallel Solution Search
Multiple solution paths are explored simultaneously. For a Codeforces problem, Deep Think might consider a dynamic programming approach, a greedy algorithm, and a graph-theoretic solution in parallel. Each path is evaluated for correctness and efficiency before the best candidate is selected.
3. Verification and Output
The selected solution undergoes self-verification — the model checks its own work for logical consistency, edge cases, and potential errors. Only after this verification step does Deep Think produce its final response. This is similar to how Alibaba's Qwen3 Max Thinking approaches reasoning, though Google's implementation differs in its parallel search strategy.
Complete Benchmark Results
Google published comprehensive benchmark results comparing Deep Think against Gemini 3 Pro (the standard model without extended reasoning), Claude Opus 4.6, and GPT-5.2 across nine benchmarks spanning reasoning, mathematics, science, and coding.
| Benchmark | Deep Think | Gemini 3 Pro | Claude Opus 4.6 | GPT-5.2 |
|---|---|---|---|---|
| ARC-AGI-2 | 84.6% | 31.1% | 68.8% | 52.9% |
| Humanity's Last Exam (No tools) | 48.4% | 37.5% | 40.0% | 34.5% |
| Humanity's Last Exam (Search+code) | 53.4% | 45.8% | 53.1% | 45.5% |
| MMMU-Pro | 81.5% | 81.0% | 73.9% | 79.5% |
| Intl Math Olympiad 2025 | 81.5% | 14.3% | — | 71.4% |
| Codeforces (Elo) | 3,455 | 2,512 | 2,352 | — |
| Intl Physics Olympiad 2025 | 87.7% | 76.3% | 71.6% | 70.5% |
| CMT-Benchmark | 50.5% | 39.5% | 17.1% | 41.0% |
| Intl Chemistry Olympiad 2025 | 82.8% | 69.6% | — | 72.0% |
What the Numbers Tell Us
The ARC-AGI-2 result is the most significant. This benchmark, created by François Chollet, specifically tests abstract reasoning — the ability to identify patterns in novel problems the model has never seen. Deep Think's 84.6% represents a 15.8-percentage-point lead over the next best AI system (Claude Opus 4.6 at 68.8%) and a massive 53.5-point improvement over standard Gemini 3 Pro (31.1%).
The Codeforces Elo of 3,455 places Deep Think in the top competitive programming tier — above the vast majority of human competitors. The improvement from Gemini 3 Pro (2,512) to Deep Think (3,455) demonstrates that inference-time compute scaling is particularly effective for code generation tasks that require algorithmic reasoning.
Deep Think vs Claude Opus 4.6 vs GPT-5.2
The competitive landscape for reasoning-focused AI models has three primary contenders as of February 2026. Each has distinct strengths that matter for different use cases.
- Leads ARC-AGI-2, Codeforces, science benchmarks
- Strongest abstract reasoning capability
- Higher latency due to extended reasoning
- Competitive on Humanity's Last Exam with tools (53.1%)
- Strong general-purpose reasoning and coding
- Lower ARC-AGI-2 (68.8%) vs Deep Think
- Strong multimodal (MMMU-Pro 79.5%)
- Broad ecosystem integration and ChatGPT access
- Lower ARC-AGI-2 (52.9%) and missing Codeforces data
For detailed analysis of the competing models, see our coverage of Claude Opus 4.6's release and benchmarks and GPT-5.3 and Codex capabilities.
The key takeaway: Deep Think's advantage is concentrated in reasoning-heavy domains. For general-purpose tasks, code generation without competitive programming constraints, and tool-augmented workflows, the gap between models narrows significantly. The right model choice depends on the specific task profile of your application.
Access and Availability
Google is offering Deep Think through three distinct access tiers, each targeting a different user profile.
- • Deep Think included in subscription
- • Access through Gemini web and mobile apps
- • Usage limits per day
- • Best for individual users and researchers
- • Pay-per-token pricing
- • Configurable reasoning depth
- • Streaming reasoning chain output
- • Best for applications and integrations
- • SLAs and enterprise support
- • Data governance and compliance controls
- • Custom deployment options
- • Best for production enterprise workloads
For developers, the Gemini API provides the most flexible integration path. The configurable reasoning depth is particularly useful — you can set a compute budget per query, balancing accuracy against latency based on task difficulty. Simpler queries get fast responses while complex reasoning tasks receive the full Deep Think treatment.
Practical Implications for Developers
Deep Think's benchmarks are impressive, but production impact depends on how the capabilities map to real-world development tasks. Here's where inference-time compute scaling matters most.
Complex Code Generation
The 3,455 Codeforces Elo translates to superior performance on algorithmically complex code tasks: graph algorithms, dynamic programming, optimization problems, and systems architecture. For development teams working on computationally intensive features, Deep Think produces more correct first-attempt solutions.
Scientific and Data Analysis
The science olympiad scores (87.7% Physics, 82.8% Chemistry) indicate strong quantitative reasoning. For applications involving data analysis, financial modeling, or scientific computation, Deep Think provides more reliable reasoning over complex multi-step calculations.
Model Routing Architecture
The most practical architecture uses Deep Think selectively. Route simple queries to standard Gemini 3 Pro for fast, cheap responses, and escalate to Deep Think only for queries that require multi-step reasoning. This gives you top accuracy where it matters without paying the latency and cost penalty on every request.
What This Means for AI Strategy
Gemini 3 Deep Think demonstrates that inference-time compute scaling is a viable path to frontier AI performance. Rather than simply training larger models, Google is showing that giving models more time to reason produces substantial accuracy gains on the hardest problems — with the ARC-AGI-2 score suggesting we're approaching human-level abstract reasoning in specific domains.
For businesses building AI-powered products, the implication is clear: the best AI strategy is no longer about choosing one model. It's about building routing architectures that select the right model for each task. Deep Think for complex reasoning, Claude Opus 4.6 for general-purpose tasks, GPT-5.2 for multimodal workflows — each has its strengths.
The benchmark race will continue to intensify, but the practical winners will be teams that focus on integration quality and task-model matching rather than chasing the highest scores on any single benchmark.
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