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) | 5/25 |
| Gemini 3.1 Pro (Deep Think) | Feb 12-19, 2026 | 48.4% | 84.6% | 94.3% | 85.9% | — | 1M | 2/12 |
| GPT-5.4 | Mar 5, 2026 | 41.6% | –** | 94.4% | 89.3% | 83.0% | 1M | 2.50/15 |
| GPT-5.2 (budget) | Prior | 29.9% | ~54% | 90.3% | — | — | 400K | 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.
The 2026 Reasoning Model Comparison
At-a-glance comparison of leading AI models across key benchmarks and pricing
** 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
- 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
Gemini 3.1 Pro (Deep Think) — Abstract Reasoning and Knowledge Breadth
- 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
GPT-5.4 — Professional Knowledge Work and Agentic Browsing
- 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
GPT-5.2 — Budget Reasoning for Standard Tasks
- 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
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 | 2/12 — lowest frontier price |
| Math and quantitative reasoning | GPT-5.4 | AIME 100%, leads structured math evaluations |
| Budget reasoning for standard tasks | GPT-5.2 | 1.75/14, 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 TodaySources: 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)