LLM Landscape 2026: Comprehensive Benchmark Analysis
Three models now dominate the LLM space: OpenAI’s GPT-5, Anthropic’s Claude Opus 4.6, and Google’s Gemini 3 Pro. Each takes a different approach to pushing capabilities forward, and the benchmark data reveals where each one actually excels versus where the marketing claims fall short.
This analysis compares performance across mathematical reasoning, coding, agentic tasks, and multimodal understanding. The numbers show real differences—not just incremental gains, but distinct strategic choices about what to optimize for.
The Three Contenders
GPT-5: Betting on Reliability
OpenAI released GPT-5 in August 2025 with four reasoning configurations: high, medium, low, and minimal. The idea is simple: let developers trade intelligence for cost depending on the task.
The high configuration scores 68 on the Artificial Analysis Intelligence Index. That’s the new top score, but it burns through 82 million tokens to get there. The minimal configuration uses just 3.5 million tokens—a 23x difference that translates directly to your API bill.
GPT-5 hits 100% on AIME 2025 when it can use Python tools. That’s the first perfect score on this benchmark. More interesting: hallucination rates under 1% on open-source prompts and 1.6% on HealthBench medical cases. If you’re building something where wrong answers have consequences, that matters.
Claude Opus 4.6: Built for Agents
Anthropic released Opus 4.6 in February 2026 and made a clear bet: optimize for agentic workflows, even if it means giving up ground elsewhere. The 84.0% BrowseComp score is 16.2 points higher than Opus 4.5 and 6.1 points ahead of GPT-5.2 Pro. For web research and multi-step tasks, nothing else is close.
The 68.8% on ARC AGI 2 nearly doubles the previous version’s 37.6%. ARC AGI 2 tests abstract reasoning on novel problems—the kind of intelligence that should transfer to tasks the model hasn’t seen before. Whether it actually does in production is harder to verify, but the benchmark gap is real.
Opus 4.6 is the first Opus-class model with a 1M token context window. Combined with 76% retrieval accuracy at that length, it can handle larger documents and longer conversations without losing track. The tradeoff: it scores 80.8% on SWE-bench Verified versus 80.9% for Opus 4.5. Anthropic chose to optimize for agents over raw coding performance.
Gemini 3 Pro: Google’s Multimodal Play
Google released Gemini 3 Pro in November 2025 as a true multimodal model. It scores 37.5% on Humanity’s Last Exam without tools, 76.4% on SimpleBench (common-sense reasoning), and 95.0% on AIME 2025 without tools (100% with code execution).
Where Gemini 3 Pro pulls ahead is visual reasoning. The 81.0% MMMU Pro score leads competitors, and the 91.0% on VPCT (Visual Physics Comprehension Test) crushes GPT-5’s 66.0%. If your application involves images, diagrams, or video, Gemini 3 Pro is the obvious choice.
The model scores competitively across most benchmarks without pronounced weaknesses. That makes it a solid general-purpose option, but it doesn’t dominate any single category the way Claude does for agents or GPT-5 does for reliability.
Benchmark Breakdown
Math: Perfect Scores at the Top
Both GPT-5 and Gemini 3 Pro hit 100% on AIME 2025 when they can execute code. Without tools, Gemini 3 Pro scores 95.0% and GPT-5 scores 99.6%. These are high-school level olympiad problems, and perfect performance means the models have mastered systematic problem-solving at that level.
At the PhD level (GPQA Diamond), the scores cluster tightly: Gemini 3 Pro at 91.9%, Claude Opus 4.6 at 91.3%, and GPT-5 Pro at 89.4%. The 2.5 point spread suggests we’re approaching human expert performance on these standardized tests.
FrontierMath is where the gaps open up. Gemini 3 Pro scores 37.6%, GPT-5 (high) scores 26.6%, and GPT-5 (medium) scores 24.8%. These are unpublished research-level problems, and even the leader gets less than 40% right. There’s still room to improve.
Coding: Claude’s Narrow Edge
Claude Opus 4.6 scores 80.8% on SWE-bench Verified (real GitHub issues), GPT-5 scores 80.0%, and Gemini 3 Pro scores 76.2%. The 4.6 point spread between first and third is tight enough that other factors—like context window size or API latency—might matter more in practice.
GPT-5 dominates Aider Polyglot (multi-language code editing) at 88.0%, with Gemini 2.5 Pro at 83.1%. Claude Opus 4.6 scores weren’t reported for this test.
For terminal operations (Terminal-Bench 2.0), Claude Opus 4.6 scores 65.4%, GPT-5.2 scores 64.7%, and Gemini 3 Pro scores 56.2%. Claude’s lead here aligns with its focus on agentic workflows.
Agents: Claude Wins Decisively
The biggest performance gaps show up in agentic tasks.
BrowseComp (web research): Claude Opus 4.6 at 84.0%, GPT-5.2 Pro at 77.9%, Gemini 3 Pro at 59.2%. That’s a 6.1 point lead over GPT-5 and a 24.8 point lead over Gemini.
τ2-bench (tool use): Claude Opus 4.6 scores 91.9% on retail tasks and 99.3% on telecom tasks. GPT-5.2 scores 82.0% on retail, Gemini 3 Pro scores 85.3%. The near-perfect telecom score is the standout result.
OSWorld (computer control via GUI): Claude Opus 4.6 at 72.7%, Claude Opus 4.5 at 66.3%, Claude Sonnet 4.5 at 61.4%. No scores reported for GPT-5 or Gemini 3 Pro.
If you’re building agents that need to use tools, navigate interfaces, or complete multi-step workflows, Claude Opus 4.6 is the clear choice.
Reasoning: Mixed Results
Humanity’s Last Exam (2,500 questions across disciplines): Claude Opus 4.6 scores 40.0% without tools, Gemini 3 Pro scores 37.5%, GPT-5 scores 25.3%. With tools enabled, GPT-5.2 Pro jumps to 50.0%, suggesting it’s better at leveraging external resources.
SimpleBench (trick questions requiring common sense): Gemini 3 Pro at 76.4%, Claude Opus 4.6 at 67.6%, GPT-5 Pro at 61.6%. Gemini’s 8.8 point lead suggests better practical reasoning.
ARC AGI 2 (abstract reasoning on novel problems): Claude Opus 4.6 at 68.8%, GPT-5.2 Pro at 54.2%, Gemini 3 Pro at 45.1%. Claude’s score nearly doubles its predecessor and significantly outperforms competitors.
Performance Tables
Core Reasoning
| Benchmark | GPT-5 | Claude Opus 4.6 | Gemini 3 Pro | Category |
|---|---|---|---|---|
| AIME 2025 (with tools) | 100% | N/R | 100% | Mathematics |
| GPQA Diamond | 89.4% | 91.3% | 91.9% | Science |
| FrontierMath | 26.6% | N/R | 37.6% | Advanced Math |
| Humanity’s Last Exam (no tools) | 25.3% | 40.0% | 37.5% | Multidisciplinary |
| SimpleBench | 61.6% | 67.6% | 76.4% | Common Sense |
| ARC AGI 2 | 54.2% | 68.8% | 45.1% | Abstract Reasoning |
Coding
| Benchmark | GPT-5 | Claude Opus 4.6 | Gemini 3 Pro | Category |
|---|---|---|---|---|
| SWE-bench Verified | 80.0% | 80.8% | 76.2% | Real-world Coding |
| Aider Polyglot | 88.0% | N/R | 83.1% | Multi-language |
| Terminal-Bench 2.0 | 64.7% | 65.4% | 56.2% | Command-line |
| LiveCodeBench Pro (Elo) | 2,243 | N/R | 2,439 | Competitive Coding |
Agents and Tools
| Benchmark | GPT-5 | Claude Opus 4.6 | Gemini 3 Pro | Category |
|---|---|---|---|---|
| BrowseComp | 77.9% | 84.0% | 59.2% | Web Research |
| τ2-bench Retail | 82.0% | 91.9% | 85.3% | Tool Use |
| OSWorld | N/R | 72.7% | N/R | Computer Control |
| Finance Agent | 56.6% | 60.7% | 44.1% | Domain-Specific |
Multimodal
| Benchmark | GPT-5 | Claude Opus 4.6 | Gemini 3 Pro | Category |
|---|---|---|---|---|
| MMMU Pro (no tools) | 79.5% | 73.9% | 81.0% | Visual Reasoning |
| VPCT | 66.0% | N/R | 91.0% | Visual Physics |
| Video-MMMU | 80.4% | N/R | 87.6% | Video Understanding |
Which Model to Choose
Pick GPT-5 if:
You need reliability. The sub-1% hallucination rate and 1.6% error rate on medical cases make it the safest choice for healthcare, legal, or other high-stakes applications.
Math is central to your use case. Perfect AIME 2025 performance and strong GPQA Diamond scores make it ideal for scientific computing, financial modeling, or quantitative analysis.
You work across multiple programming languages. The 88% Aider Polyglot score shows strong multi-language capabilities.
Cost matters. The four reasoning configurations let you dial down intelligence (and cost) for simpler tasks. The minimal configuration uses 23x fewer tokens than high while maintaining GPT-4.1-level performance.
Pick Claude Opus 4.6 if:
You’re building agents. The 84% BrowseComp, 91.9% τ2-bench Retail, and 72.7% OSWorld scores make it the best choice for systems that use tools, navigate interfaces, or complete multi-step workflows.
Abstract reasoning is important. The 68.8% ARC AGI 2 score—nearly double the previous version—suggests real advances in novel problem-solving.
You need long-context understanding. The 1M token window with 76% retrieval accuracy handles large documents, codebases, or long conversations.
Real-world coding is your focus. The narrow lead on SWE-bench Verified (80.8%) and strong Terminal-Bench 2.0 performance (65.4%) make it solid for software engineering tasks.
Pick Gemini 3 Pro if:
Your application is multimodal. The 81% MMMU Pro and 91% VPCT scores show superior visual reasoning. If you’re working with images, diagrams, charts, or video, this is the obvious choice.
Common-sense reasoning matters. The 76.4% SimpleBench score suggests better practical reasoning and resistance to trick questions—useful for customer service, content moderation, or general-purpose assistants.
You need advanced math. The 37.6% FrontierMath score leads all competitors on research-level mathematics.
You want breadth over specialization. Gemini 3 Pro scores competitively across most benchmarks without pronounced weaknesses.
What the Benchmarks Tell Us
The 2026 benchmark data reveals several trends:
Specialization is the new strategy. Claude Opus 4.6’s optimization for agents—even at the cost of slight regressions elsewhere—shows that frontier labs are making strategic choices about what to prioritize. The days of trying to improve everything at once are over.
The reasoning-cost tradeoff is real. GPT-5’s four configurations acknowledge that not all tasks need maximum intelligence. The 23x token difference between high and minimal shows that smart resource allocation can cut costs dramatically.
Multimodal is now expected. Gemini 3 Pro’s strong performance across MMMU Pro, VPCT, and Video-MMMU suggests future models will need to handle text, images, video, and audio seamlessly.
Some benchmarks are saturated. GPQA Diamond scores cluster between 87% and 91.9%, suggesting these tests are approaching their limits. We need harder benchmarks like FrontierMath (where the leader scores just 37.6%) to keep measuring progress.
2026 is the year of agents. The dramatic improvements on BrowseComp, τ2-bench, OSWorld, and Terminal-Bench signal a shift from models that answer questions to models that take actions.
Bottom Line
GPT-5, Claude Opus 4.6, and Gemini 3 Pro each excel in different areas. GPT-5 prioritizes reliability and math. Claude Opus 4.6 optimizes for agents. Gemini 3 Pro emphasizes multimodal understanding and breadth.
The choice isn’t about finding “the best”—it’s about finding the best fit for your specific needs. As these systems evolve, expect even more specialization, with models optimized for narrow domains achieving superhuman performance while general-purpose models provide solid baseline capabilities.
The benchmark data is clear: AI systems now reason, plan, and act. The question isn’t whether they can match human performance on standardized tests—it’s how we’ll use these capabilities to solve real problems at scale.