--- source_url: "https://teamai.com/blog/large-language-models-llms/best-ai-models-for-complex-reasoning-2026/" title: "Best AI for Complex Reasoning (2026): Opus, GPT-5.4 & Gemini Compared" mirrored_at: 2026-08-29T01:02:44.312Z host: teamai.com cited_in_42a: true mirror_canonical: "https://index.42a.ai/teamai.com/blog/large-language-models-llms/best-ai-models-for-complex-reasoning-2026/index" --- > **Original source:** https://teamai.com/blog/large-language-models-llms/best-ai-models-for-complex-reasoning-2026/ Picking the best AI model for complex reasoning tasks used to mean checking MMLU and GPQA scores. That no longer works. As of early 2026, every frontier model scores above 90% on MMLU. GPQA Diamond is close behind — GPT-5.4 and Gemini 3.1 Pro are virtually tied at 94.4% and 94.3% respectively. When benchmarks saturate, they stop separating models. They stop telling you anything useful about which AI model handles the reasoning problems your team actually faces. The question teams need to answer now is not “which model scored highest?” It’s: - Which model reasons best on problems it has never seen before? - Which model sustains quality across long, multi-step tasks? - Which model handles ambiguity rather than requiring a perfect prompt? - Which model can do this at a cost that doesn’t destroy margin? This guide covers the benchmarks that still have signal in 2026, the models that lead on each, and how to match the right model to each type of reasoning work. ## The Benchmarks That Still Matter in 2026 Benchmark What It Tests Why It Still Has Signal Human Baseline HLE (Humanity’s Last Exam) 2,500 expert questions across math, science, humanities Top models score under 55%. Not saturated. ~85% (expert) ARC-AGI-2 Abstract pattern recognition, novel rules, no memorization Pure LLMs score 0%. Even top reasoning systems are under 85%. ~60% GPQA Diamond 198 PhD-level science questions, Google-proof Approaching saturation (94%+) but still separates extended thinking vs. standard modes ~65% (expert) BrowseComp Agentic web research and multi-step information retrieval Tests real-world reasoning under uncertainty — GDPval Knowledge work across 44 professional occupations Closest public benchmark to enterprise reasoning tasks — Note: BrowseComp and GDPval do not have established human baselines. They are included because they test the types of reasoning tasks most relevant to professional and enterprise use, not because they have a ceiling to compare against. Benchmarks to stop citing: - GSM8K / MATH: top models score near 100%. - MMLU: all frontier models exceed 90%. No separation. - HumanEval: saturated for coding. ## The 2026 Reasoning Model Comparison ### At a Glance Model Release HLE ARC-AGI-2 GPQA Diamond (Thinking) BrowseComp GDPval Context Price (Input / Output per 1M) Claude Opus 4.6 Feb 5, 2026 53.0%\* (w/ tools) 38% 89.6% — 78.0% 200K (1M beta) 5/5/25 Gemini 3.1 Pro (Deep Think) Feb 12-19, 2026 48.4% 84.6% 94.3% 85.9% — 1M 2/2/12 GPT-5.4 Mar 5, 2026 41.6% –\*\* 94.4% 89.3% 83.0% 1M 2.50/2.50/15 GPT-5.2 (budget) Prior 29.9% ~54% 90.3% — — 400K 1.75/1.75/14 \*HLE scores vary by evaluation method and tool access. Opus 4.6’s 53.0% reflects the Glia.ca March 2026 evaluation with tool access. Gemini’s 48.4% reflects the Deep Think mode without tool access. Direct comparisons should use the same eval setup. \*\*GPT-5.4 ARC-AGI-2 results have not been published by OpenAI at time of writing. This does not indicate the model was not tested. Model Performance Overview Benchmark scores across key reasoning tasks Claude Opus 4.6 Release: Feb 5, 2026 • Context: 200K (1M beta) HLE 53.0%\* ARC-AGI-2 38% GPQA Diamond 89.6% BrowseComp , GDPval 78.0% Input / Output $5 / $25 Gemini 3.1 Pro (Deep Think) Release: Feb 12-19, 2026 • Context: 1M HLE 48.4% ARC-AGI-2 84.6% GPQA Diamond 94.3% BrowseComp 85.9% GDPval , Input / Output $2 / $12 GPT-5.4 Release: Mar 5, 2026 • Context: 1M HLE 41.6% ARC-AGI-2 –\*\* GPQA Diamond 94.4% BrowseComp 89.3% GDPval 83.0% Input / Output $2.50 / $15 GPT-5.2 (budget) Release: Prior • Context: 400K HLE 29.9% ARC-AGI-2 ~54% GPQA Diamond 90.3% BrowseComp , GDPval , Input / Output $1.75 / $14 Benchmark Performance Comparison HLE scores across model releases 53.0% Highest HLE (Opus 4.6) 23.1pp Gap between best/worst 41.6% GPT-5.4 HLE score Methodology Notes **\*** HLE scores vary by evaluation method and tool access. Opus 4.6’s 53.0% reflects the Glia.ca March 2026 evaluation with tool access. Gemini’s 48.4% reflects the Deep Think mode without tool access. Direct comparisons should use the same eval setup. **\*\*** GPT-5.4 ARC-AGI-2 results have not been published by OpenAI at time of writing. This does not indicate the model was not tested. ### What the HLE Score Gap Actually Tells You HLE’s top scores are still far from human expert level. That gap is the most important signal in this table. Model HLE Score Gap to Expert Human (~85%) Claude Opus 4.6 (w/ tools) 53.0% 32 points Gemini 3.1 Pro (Deep Think, no tools) 48.4% 36.6 points Gemini 3.1 Pro Preview (Artificial Analysis eval) 44.7% 40.3 points GPT-5.4 (xhigh thinking) 41.6% 43.4 points GPT-5.2 29.9% 55.1 points What this means for teams: - No model reliably handles expert-level novel reasoning autonomously yet - The models with the smallest gap (Opus 4.6, Gemini Deep Think) are the best current proxies for deep reasoning — with the caveat that tool access significantly affects scores - Human review remains essential for high-stakes reasoning outputs - The gap is closing: the field moved from 29.9% on HLE at the benchmark’s launch to 53.0% within roughly six months, across successive model generations * * * ### What “Extended Thinking” Actually Changes Every major lab now ships reasoning modes alongside their base models. These are not just slower responses — they change what the model can solve. Example from GPQA Diamond: Claude Opus 4.6 Mode GPQA Diamond Score Standard (no extended thinking) ~72% Extended Thinking mode 89.6% A 17-point swing on the same model. The reasoning mode matters as much as the model itself — a pattern that mirrors what Morph found with agent scaffolds in coding benchmarks. Investing in how you deploy a model often returns more than upgrading the model itself. What extended thinking enables: - Multi-step verification before outputting an answer - Self-correction mid-reasoning chain - Sustained focus across longer, interdependent problems - Better calibration on problems requiring genuine uncertainty The tradeoff: Extended thinking uses more tokens and costs more per query. For routine tasks, it’s unnecessary overhead. For high-stakes reasoning tasks, the accuracy improvement justifies the cost. See the routing section below for when to use it and when not to. * * * ### Which Model for Which Reasoning Work #### Claude Opus 4.6 — Deep Reasoning and Agent Orchestration Claude Opus 4.6 Deep Reasoning and Agent Orchestration Release: Feb 5, 2026 | Context: 200K (1M beta) 53.0% HLE (w/tools) 89.6% GPQA Diamond 78.0% GDPval 38% ARC-AGI-2 ✓ Best For **Long-horizon research tasks** requiring sustained, interdependent reasoning **Legal, compliance, or financial analysis** where answer quality matters more than speed **Ambiguous or underspecified problems** that require the model to structure the question before answering **Multi-agent workflows** where coordinated parallel reasoning is needed ★ What Sets It Apart - Leads HLE with tools, the most demanding real-world reasoning test currently available - Adaptive Thinking mode automatically sets reasoning depth based on problem complexity, with no manual budget configuration needed - Agent Teams: spawns parallel sub-agents for multi-step tasks , the only major model with native multi-agent orchestration built in - 65% fewer tokens than its predecessor while achieving higher pass rates on complex tasks - 15 percentage point improvement in multi-agent coordination vs. prior generation Note on ARC-AGI-2 Performance Opus 4.6 scores 38% on ARC-AGI-2 in standard mode. ARC-AGI-2 is specifically designed to resist pattern recall and reward genuine abstract generalization , the type of reasoning that large language models are architecturally less suited to. Gemini’s lead on this benchmark (84.6%) reflects a real difference in abstract reasoning capability, not a data gap. * * * ### Gemini 3.1 Pro (Deep Think) — Abstract Reasoning and Knowledge Breadth Gemini 3.1 Pro (Deep Think) Abstract Generalization Leader Release: Feb 12-19, 2026 | Context: 1M tokens 84.6% ARC-AGI-2 94.3% GPQA Diamond 48.4% HLE 85.9% BrowseComp 2/2/12 Price ★ What Sets It Apart - Leads ARC-AGI-2 at 84.6% , the benchmark specifically designed to resist pattern memorization - Virtually tied with GPT-5.4 on GPQA Diamond (94.3% vs. 94.4%) - Largest standard context window (1M tokens, available now, not beta) - Lowest price point of the three frontier models (2/2/12 per 1M tokens) - Deep Think mode optimized for novel pattern recognition and abstract generalization ✓ Best For **Problems requiring genuine generalization** rather than pattern recall **Long-document reasoning:** contracts, research papers, large codebases in context **Cost-sensitive deployments** where high-volume reasoning needs to be commercially viable **Scientific research synthesis** and cross-disciplinary analysis * * * ### GPT-5.4 — Professional Knowledge Work and Agentic Browsing GPT-5.4 Professional Knowledge Work and Agentic Browsing Release: Mar 5, 2026 94.4% GPQA Diamond 89.3% BrowseComp 83.0% GDPval ★ What Sets It Apart - Leads BrowseComp (89.3%) , the strongest available signal for agentic research and multi-step retrieval tasks - Leads GDPval at 83.0% across 44 professional occupations, 5 points above Opus 4.6 (78.0%) - AIME 2025: 100% , complete saturation on mathematical olympiad problems - OSWorld-Verified (computer use): 75.0%, above the human baseline of 72.4% - 5-level reasoning intensity setting (none to xhigh) for granular compute control - Built-in tool search with 47% token reduction on retrieval-heavy tasks ✓ Best For **Professional knowledge work:** legal, finance, consulting, operations **Agentic research pipelines** requiring reliable multi-step web retrieval **Math-heavy workflows:** modeling, forecasting, quantitative analysis **Computer use tasks** where the model needs to navigate real software environments * * * ### GPT-5.2 — Budget Reasoning for Standard Tasks GPT-5.2 Budget Reasoning for Standard Tasks Release: Prior 29.9% HLE ~54% ARC-AGI-2 90.3% GPQA Diamond i Current Context - Included as the established cost-optimized option in the GPT-5 family - GPT-5.4 has since superseded it on most benchmarks - Still scores 90.3% on GPQA Diamond at a lower price point (1.75/1.75/14 per 1M tokens) - Remains a rational choice for high-volume, moderate-complexity reasoning tasks where cost efficiency matters more than leading-edge accuracy ✓ Best For **Structured analysis tasks** that don’t require novel reasoning **High-volume workflows** where consistent, good-enough output beats occasional excellence **Teams with tight API budgets** that still need reliable knowledge work capability * * * ### Reasoning Task Routing Framework Task Type Recommended Model Primary Reason Ambiguous, open-ended research Claude Opus 4.6 Leads HLE (w/ tools); handles underspecified prompts Abstract or novel pattern problems Gemini 3.1 Pro (Deep Think) Leads ARC-AGI-2 at 84.6% Professional knowledge work GPT-5.4 Leads GDPval across 44 occupations Long-document analysis (1M+ tokens) Gemini 3.1 Pro 1M context standard, lowest cost at scale Multi-agent reasoning workflows Claude Opus 4.6 Only model with native Agent Teams orchestration Agentic web research GPT-5.4 Leads BrowseComp at 89.3% High-volume reasoning at low cost Gemini 3.1 Pro 2/2/12 — lowest frontier price Math and quantitative reasoning GPT-5.4 AIME 100%, leads structured math evaluations Budget reasoning for standard tasks GPT-5.2 1.75/1.75/14, still 90.3% GPQA Diamond * * * ### The Extended Thinking Cost-Benefit Decision Not every query needs extended thinking. Routing reasoning intensity to the right tasks is where teams recover cost. Task Complexity Reasoning Mode Why Simple lookup or summarization Standard mode Extended thinking adds cost with no accuracy benefit Structured analysis with clear criteria Standard mode Model doesn’t need to self-correct Ambiguous multi-step research Extended thinking Verification loops improve accuracy meaningfully High-stakes decisions (legal, financial) Extended thinking Calibration and error-checking matter Long-horizon agent tasks Extended thinking Prevents reasoning drift across many steps * * * ### Staying Current: The 2026 Reasoning Timeline Date Event 2025 MMLU, HumanEval, and GSM8K declared saturated by the eval community Jan 2026 HLE published in Nature — becomes the new standard hard benchmark Feb 5, 2026 Claude Opus 4.6 released — Adaptive Thinking, Agent Teams, leads HLE w/ tools at 53.0% Feb 12-19, 2026 Gemini 3.1 Pro and Deep Think released — leads ARC-AGI-2 at 84.6% Mar 5, 2026 GPT-5.4 released — leads GDPval, BrowseComp, virtually ties GPQA Diamond at 94.4% Mar 10, 2026 GPQA Diamond top score: 94.4% (GPT-5.4) — benchmark approaching saturation Ongoing ARC-AGI-3 with interactive environments expected; HLE-Rolling adds new questions monthly Why flexibility matters more than picking a winner: the models leading each benchmark today are different models. Opus 4.6 leads HLE. Gemini leads ARC-AGI-2. GPT-5.4 leads GDPval and BrowseComp. No single model dominates all reasoning task types, and that is unlikely to change as the landscape continues to move. * * * ### Stop Benchmarking Models. Start Routing Them. The routing logic in this article only works in practice if your team has access to all the models without managing separate API keys or subscriptions for each. TeamAI gives your team access to Claude Opus 4.6, GPT-5.4, Gemini 3.1 Pro, and more — all in one workspace, with the ability to route tasks to the right model without managing separate subscriptions or API keys. What you can build: - Custom Agents for research, legal analysis, and knowledge work - Automated Workflows that escalate reasoning intensity based on task complexity - Multi-model routing so every query goes to the model best suited for it Workspace-based pricing means your whole team gets full model access at a flat cost, not per seat. [Run Your Hardest Reasoning Tasks in TeamAI Today](https://app.teamai.com/login) * * * _Sources: Glia.ca Frontier Model Benchmark Comparison (March 7, 2026), Artificial Analysis HLE Leaderboard (March 2026), PricePerToken GPQA Leaderboard (March 10, 2026), OfficeChai GPT-5.4 benchmark analysis (March 5, 2026), EvoLink GPT-5.4 vs Claude Opus 4.6 vs Gemini 3.1 Pro (March 6, 2026), APIYI 12-benchmark comparison (March 6, 2026), ARC Prize Foundation Leaderboard, Scale AI SEAL HLE Leaderboard, Multibly Claude Opus 4.5 reasoning analysis (February 20, 2026), Reddit r/LocalLLaMA benchmark signal analysis (March 2026)_