--- title: "Claude Opus 4.6 vs 4.5 Benchmarks (Explained)" description: "Explore this breakdown of Claude Opus 4.6 and how it stacks up to Opus 4.5 and OpenAI and Google models." canonical_url: "https://www.vellum.ai/blog/claude-opus-4-6-benchmarks" md_url: "https://www.vellum.ai/md/blog/claude-opus-4-6-benchmarks" type: "blog" published_at: "2026-02-05T00:00:00.000Z" read_time: "4 min" category: "Model Comparisons" featured_image: "https://cdn.sanity.io/images/ghjnhoi4/production/f5d4e8be63d15e54856cbc8ca33d470ea54effed-2880x1620.png" authors: - "Nicolas Zeeb" --- # Claude Opus 4.6 vs 4.5 Benchmarks (Explained) Explore this breakdown of Claude Opus 4.6 and how it stacks up to Opus 4.5 and OpenAI and Google models. ## Introduction The AI community is enthusiastically discussing Anthropic's latest Claude Opus 4.6 release. A notable example showcases the model "managing a ~50-person organization across 6 repositories" while handling both product and organizational decisions. This upgrade delivers meaningful improvements in agentic workflows and reasoning tasks, with some notable trade-offs in certain benchmarks. Significantly, Opus 4.6 is the first Opus-class model featuring a 1M token context window, enabling agents to work across larger problems without losing context. The model is available through Anthropic's API, major cloud providers. ## Key Observations from Benchmarks While benchmarks have inherent limitations in capturing real-world utility, they provide quantifiable progress measurement. Key findings include: **Agentic capabilities excel:** Terminal-Bench 2.0 (65.4%), OSWorld (72.7%), τ2-bench Retail (91.9%), and BrowseComp (84.0%) show significant leaps over Opus 4.5 and competing models. **Novel problem-solving dominates:** ARC AGI 2 score of 68.8% nearly doubles Opus 4.5's 37.6% and surpasses Gemini 3 Pro's 45.1%, signaling major abstract reasoning advances. **Multidisciplinary reasoning leadership:** Humanity's Last Exam (without tools) achieves 40.0%, beating Opus 4.5's 30.8% and Gemini 3 Pro's 37.5%, though GPT-5.2 leads at 50.0%. **Coding trade-off:** SWE-bench Verified scores 80.8%, slightly down from Opus 4.5's 80.9%, suggesting optimization focus elsewhere. **Visual reasoning steady progress:** MMMU Pro reaches 73.9% (without tools) and 77.3% (with tools), trailing GPT-5.2's 79.5%/80.4%. ## Coding and Software Engineering ### Agentic Terminal Coding (Terminal-Bench 2.0) Terminal-Bench evaluates command-line navigation, shell command execution, and development operations. Opus 4.6: 65.4% Opus 4.5: 59.8% Sonnet 4.5: 51.0% Gemini 3 Pro: 56.2% GPT-5.2: 64.7% Opus 4.6 achieves the strongest performance in Anthropic's lineup for command-line proficiency, though slightly trails GPT-5.2. ### Agentic Coding (SWE-bench Verified) SWE-bench Verified tests real-world software engineering through GitHub issue resolution across production codebases. Opus 4.6: 80.8% Opus 4.5: 80.9% GPT-5.2: 80.0% Sonnet 4.5: 77.2% Gemini 3 Pro: 76.2% Near-parity with its predecessor suggests Anthropic prioritized other capabilities while maintaining elite coding performance. ## Agentic Tool Use and Orchestration ### Agentic Tool Use (τ2-bench) τ2-bench evaluates sophisticated tool-calling across Retail (consumer scenarios) and Telecom (enterprise support) domains. Retail Results: Opus 4.6: 91.9% Opus 4.5: 88.9% Sonnet 4.5: 86.2% Gemini 3 Pro: 85.3% GPT-5.2: 82.0% Telecom Results: Opus 4.6: 99.3% Opus 4.5: 98.2% Sonnet 4.5: 98.0% Gemini 3 Pro: 98.0% GPT-5.2: 98.7% These results position Opus 4.6 as the strongest model for complex tool orchestration. ### Scaled Tool Use (MCP Atlas) MCP Atlas tests performance when coordinating many tools simultaneously. Opus 4.5: 62.3% GPT-5.2: 60.6% Opus 4.6: 59.5% Gemini 3 Pro: 54.1% Sonnet 4.5: 43.8% This regression from Opus 4.5 suggests trade-offs in scaled tool coordination, requiring potential application-layer orchestration logic. ## Computer and Environment Interaction ### Agentic Computer Use (OSWorld) OSWorld evaluates computer control through GUI interactions, simulating desktop automation tasks. Opus 4.6: 72.7% Opus 4.5: 66.3% Sonnet 4.5: 61.4% The 6.4 percentage point improvement over its predecessor is notable for practical automation workflows. ### Agentic Search (BrowseComp) BrowseComp evaluates web browsing and multi-step research task completion. Opus 4.6: 84.0% GPT-5.2 Pro: 77.9% Opus 4.5: 67.8% Gemini 3 Pro: 59.2% Sonnet 4.5: 43.9% The 16.2 percentage point improvement over Opus 4.5 makes Opus 4.6 the clear leader for agentic web research. ## Reasoning and General Intelligence ### Multidisciplinary Reasoning (Humanity's Last Exam) Tests frontier reasoning across diverse academic disciplines. Results (without tools / with tools): GPT-5.2: 36.6% / 50.0% Opus 4.6: 40.0% / 53.1% Gemini 3 Pro: 37.5% / 45.8% Opus 4.5: 30.8% / 43.4% Sonnet 4.5: 17.7% / 33.6% The 9.2 percentage point gain without tools suggests meaningful improvements in core reasoning. ### Novel Problem-Solving (ARC AGI 2) ARC AGI 2 tests abstract reasoning and pattern recognition on novel problems. Opus 4.6: 68.8% GPT-5.2 Pro: 54.2% Gemini 3 Pro: 45.1% Opus 4.5: 37.6% The 31.2 percentage point leap represents one of the largest single-benchmark improvements, suggesting fundamental advances in novel problem-solving. ### Graduate-Level Reasoning (GPQA Diamond) GPQA Diamond evaluates PhD-level scientific questions across physics, chemistry, and biology. GPT-5.2 Pro: 93.2% Gemini 3 Pro: 91.9% Opus 4.6: 91.3% Opus 4.5: 87.0% Sonnet 4.5: 83.4% The 4.3 percentage point gain confirms continued progress in scientific reasoning. ## Long Context Capabilities ### Long-Context Retrieval (MRCR v2, Needle-in-a-Haystack) MRCR v2 measures ability to find multiple specific facts within long inputs. Results at 256K and 1M context: Opus 4.6: 93.0% at 256K / 76.0% at 1M GPT-5.2 Thinking: 98% (4-needle at 256K) / 70% (8-needle at 256K) Gemini 3 Pro: 77% (8-needle at 256K) Opus 4.6 demonstrates reliable recall at extreme context lengths, with both Opus 4.6 and GPT-5.2 showing dependable retrieval while Gemini's performance degrades more noticeably. ## Multimodal and Visual Reasoning ### Visual Reasoning (MMMU Pro) MMMU Pro tests multimodal understanding across academic disciplines. Results (without tools / with tools): GPT-5.2: 79.5% / 80.4% Gemini 3 Pro: 81.0% / (not reported) Opus 4.6: 73.9% / 77.3% Opus 4.5: 70.6% / 73.9% Sonnet 4.5: 63.4% / 68.9% Gains are steady but incremental compared to Opus 4.6's leaps in other areas. ## Knowledge Work and Domain-Specific Intelligence ### Office Tasks (GDPVal-AA Elo) GDPVal-AA measures performance on knowledge work using Elo ratings. Opus 4.6: 1606 Elo GPT-5.2: 1462 Elo Opus 4.5: 1416 Elo Sonnet 4.5: 1277 Elo Gemini 3 Pro: 1195 Elo The 190-point improvement over Opus 4.5 indicates significantly better performance on long-horizon professional tasks. ### Agentic Financial Analysis (Finance Agent) Evaluates performance on realistic financial analysis tasks. Opus 4.6: 60.7% GPT-5.2: 56.6% Opus 4.5: 55.9% Sonnet 4.5: 54.2% Gemini 3 Pro: 44.1% This best-in-class result suggests strong utility for financial services and business intelligence applications. ## Multilingual Understanding ### Multilingual Q&A (MMMLU) MMMLU evaluates multilingual understanding and reasoning. Gemini 3 Pro: 91.8% Opus 4.6: 91.1% Opus 4.5: 90.8% Sonnet 4.5: 89.5% GPT-5.2: 89.6% Near-parity across the Claude lineup suggests consistent multilingual capabilities across model sizes. ## What's New and Notable ### Agent-Focused Optimization Opus 4.6's dramatic improvements in computer use (+6.4pp), web search (+16.2pp), and terminal operations (+5.6pp) signal optimization for practical agent deployments. The 84.0% BrowseComp score positions it as the go-to model for research agents. ### Massive Leap in Abstract Reasoning The 68.8% ARC AGI 2 score, nearly doubling the previous version, represents one of the largest single-benchmark improvements in frontier model updates, suggesting genuine advances in novel problem-solving. ### MCP Atlas Regression The drop from 62.3% to 59.5% on scaled tool use represents one of the few areas where Opus 4.6 regresses. Teams building agents coordinating dozens of tools may require additional orchestration logic. ### Real Work Strong Gains The 60.7% Finance Agent score and 1606 GDPVal Elo suggest excellence in long-horizon, multi-step professional tasks crucial for enterprise deployments. ## Why This Matters for Your Agents Opus 4.6 is optimized for strong agents, excelling at core agentic tasks: computer use, terminal execution, web search, and long-horizon workflows. For research, financial analysis, or knowledge work agents, Opus 4.6 is worth testing. While MCP Atlas presents a known trade-off for large-scale tool orchestration, gains elsewhere likely outweigh this for most setups. ## Extra Resources - [GPT-5.2 Benchmarks](/blog/gpt-5-2-benchmarks) - [Google Gemini Pro Benchmarks](/blog/google-gemini-3-benchmarks) - [Claude Opus 4.5 Benchmarks](/blog/claude-opus-4-5-benchmarks) - [Beginner's Guide to Building AI Agents](/blog/ai-automation-guide) - [2026 Guide to Top 20 AI Agent Builder Platforms](/blog/top-ai-agent-builder-platforms-complete-guide)