The AI arms race just hit overdrive. Within a single week of November 2025, four game-changing models dropped: Claude Opus 4.5, GPT-5.1-Codex-Max, Gemini 3 Pro, and Claude Sonnet 4.5. But here’s what the marketing fluff won’t tell you: they’re not all built for the same job.
After spending days testing these models on real coding projects, analyzing verified benchmarks, and watching them tackle everything from arcade game engines to 24-hour autonomous refactors, I’m going to give you the unfiltered truth about which model deserves your time and money.
This isn’t another shallow comparison post. This is the definitive guide to understanding Opus 4.5 vs GPT-5.1-Codex-Max, Opus 4.5 vs Gemini 3 Pro, and Opus 4.5 vs Sonnet 4.5 — based on actual performance data, not hype.
The Current State: Who’s Actually Winning?
Let’s cut through the noise. Here’s what the verified benchmarks actually show:
SWE-Bench Verified (The Real Coding Test)
This benchmark tests models on actual GitHub issues from real-world repositories. It’s the closest thing we have to measuring “can this model actually fix bugs in production code?”
- Opus 4.5: 80.9% (High effort) — First model ever to break 80%
- GPT-5.1-Codex-Max: 77.9% (Extra-high effort)
- Sonnet 4.5: 77.2%
- Gemini 3 Pro: 76.2%
Opus 4.5 is the only model that’s crossed the 80% threshold. That’s not a small jump — it represents a fundamental leap in autonomous coding capability.
Terminal-Bench 2.0 (Command Line Mastery)
Tests models on complex terminal-based workflows that developers actually face:
- Opus 4.5: 59.3%
- GPT-5.1-Codex-Max: 58.1%
- Gemini 3 Pro: 54.2%
The Reasoning Tests (Where Things Get Interesting)
Humanity’s Last Exam (tests PhD-level reasoning across domains):
- Gemini 3 Deep Think: 41.0% (without tools)
- Gemini 3 Pro: 37.5% (without tools)
- GPT-5.1: 26.5%
- Sonnet 4.5: 13.7%
ARC-AGI-2 (abstract reasoning that can’t be memorized):
- Gemini 3 Deep Think: 45.1% (with code execution)
- Opus 4.5: 37.6%
- Gemini 3 Pro: 31.1%
- GPT-5.1: 17.6%
Here’s the insight nobody’s emphasizing: Gemini 3 dominates pure reasoning tasks, while Opus 4.5 dominates practical software engineering. They’re not competing for the same crown.
Opus 4.5 vs GPT-5.1-Codex-Max: The Battle for Coding Supremacy
Where Opus 4.5 Pulls Ahead
1. Pure Coding Accuracy Opus 4.5’s 80.9% on SWE-Bench isn’t just a number — it represents fewer failed attempts, fewer debugging loops, and more reliable autonomous work. When you need code that works on the first try, Opus has the edge.
2. Multi-File Project Generation In real-world testing, Opus 4.5 has demonstrated the ability to generate entire game engines in one shot. We’re talking:
- Multiple interconnected game modes (Breakout, Snake, Space Invaders, Tetris)
- Audio systems
- Scoring logic
- UI frameworks
- Polished animations
And here’s the kicker: it doesn’t just generate the code. It tests itself, loads a browser, captures screenshots, runs keyboard inputs, finds logic bugs, and iterates until everything works perfectly.
No other model has demonstrated this level of autonomous development at scale.
3. Tool Search Architecture Opus 4.5 introduces a revolutionary feature: it can search through thousands of available tools, load only what it needs, and avoid context window bloat. This means:
- Faster execution
- Lower token costs
- More efficient agentic workflows
- Less memory overhead
4. Thinking Efficiency Opus 4.5 achieves higher accuracy with fewer “thinking tokens.” On medium effort, it matches Sonnet 4.5’s best SWE-Bench score while using 76% fewer tokens. At high effort, it beats Sonnet while using only 50% of the tokens.
This translates directly to:
- Lower API costs
- Faster response times
- More stable reasoning chains
Where GPT-5.1-Codex-Max Fights Back
1. Compaction Technology GPT-5.1-Codex-Max’s headline feature is “compaction” — the ability to work coherently across multiple context windows by intelligently pruning history while preserving critical information.
This enables:
- 24+ hour autonomous coding sessions
- Million-token-scale tasks
- Complex multi-day refactors without losing the thread
In internal testing, GPT-5.1-Codex-Max has worked independently for over 24 hours on single tasks, continuously refactoring, testing, and debugging.
2. Token Efficiency Gains At medium reasoning effort, Codex-Max uses 30% fewer thinking tokens than GPT-5.1-Codex while achieving better or equal accuracy. This is a big deal for high-volume API usage.
3. Extreme Instruction Following User reports consistently note that Codex-Max is “painfully persistent” in following instructions — to the point where developers joke it would “rewrite the entire V8 engine to break arithmetic” if you asked it to make 1+1=3.
This literal interpretation can be both a strength and a weakness. It gives you exactly what you asked for, which is powerful when your specifications are precise, but can lead to over-engineered solutions when they’re not.
4. Windows Environment Support GPT-5.1-Codex-Max is the first OpenAI model explicitly trained to operate in Windows environments, making it more versatile for enterprise developers working across different operating systems.
The Real-World Verdict: Opus 4.5 vs GPT-5.1-Codex-Max
Choose Opus 4.5 if you need:
- The highest raw coding accuracy available
- Autonomous testing and self-debugging capabilities
- Efficient tool integration with minimal context overhead
- Project-wide consistency across dozens of files
- Lower token costs with better results
Choose GPT-5.1-Codex-Max if you need:
- Ultra-long-horizon tasks (24+ hours)
- Compaction across millions of tokens
- Extreme literal instruction following
- Integration with the OpenAI/Codex ecosystem
- Cross-platform Windows/Linux development
The truth? For most developers, Opus 4.5 delivers better results with less babysitting. But for teams already invested in the Codex CLI and OpenAI ecosystem who need those marathon 24-hour sessions, Codex-Max is purpose-built for that workflow.
Opus 4.5 vs Gemini 3 Pro: Coding vs Universal Intelligence
This comparison reveals a fundamental philosophical split in AI development.
Where Opus 4.5 Dominates
1. Software Engineering Execution
- SWE-Bench: 80.9% vs 76.2% (Opus leads by 4.7 points)
- Terminal-Bench: 59.3% vs 54.2% (Opus leads by 5.1 points)
When you need actual working code that solves real GitHub issues, Opus 4.5 consistently outperforms.
2. Agentic Coding Workflows Opus excels at:
- Browser automation + terminal integration
- Multi-step debugging loops
- Self-testing and validation
- Autonomous error recovery
3. Developer Tool Integration Opus 4.5’s Tool Search feature gives it an architectural advantage for building complex agents that need to coordinate many different tools without overwhelming the context window.
Where Gemini 3 Pro Fights Back (And Sometimes Wins)
1. Abstract Reasoning
- Humanity’s Last Exam: 37.5% vs Opus (score not publicly reported by Anthropic)
- ARC-AGI-2: 31.1% vs 37.6% (Opus wins here)
- GPQA Diamond: 91.9% (PhD-level scientific reasoning)
Gemini 3 Pro demonstrates stronger performance on pure reasoning tasks that don’t involve code execution.
2. Long-Horizon Planning On Vending-Bench 2 (simulated year-long business decision-making):
- Gemini 3 Pro: $5,478.16 mean net worth
- GPT-5.1: $1,473.43
- Sonnet 4.5: $3,838.74
This 272% advantage over GPT-5.1 shows Gemini’s strength in sustained, coherent planning over extended time horizons.
3. Multimodal Excellence
- MMMU-Pro: 81.0% (5 points ahead of GPT-5.1)
- Video-MMMU: 87.6%
- Native audio/video/image understanding in a unified architecture
If your workflows involve analyzing videos, processing images, or working across multiple modalities simultaneously, Gemini 3 Pro is built for this from the ground up.
4. Vibe Coding and UI Generation Gemini 3 Pro tops the WebDev Arena leaderboard with 1,487 Elo. When you need to generate rich, interactive web UIs from natural language descriptions, Gemini excels at zero-shot generation with less prompting.
5. Mathematics
- MathArena Apex: 23.4% (vs 1.6% for Sonnet 4.5)
- AIME 2025: 95.0% without tools, 100% with code execution
For mathematical reasoning and scientific computation, Gemini demonstrates significantly stronger capabilities.
The Real-World Verdict: Opus 4.5 vs Gemini 3 Pro
Choose Opus 4.5 if you’re:
- Building production software that needs to work
- Running autonomous coding agents
- Managing complex debugging workflows
- Working primarily in backend/systems code
- Prioritizing coding accuracy over breadth
Choose Gemini 3 Pro if you’re:
- Building multimodal applications
- Need strong mathematical/scientific reasoning
- Creating interactive web UIs and visualizations
- Working across text, image, audio, and video
- Building long-horizon planning systems
- Integrated into Google’s ecosystem (Vertex AI, etc.)
The honest take? If your job is primarily software engineering, Opus 4.5 is the sharper tool. If you need a universal intelligence that can reason across domains and modalities, Gemini 3 Pro is more versatile.
Opus 4.5 vs Sonnet 4.5: Same Family, Different League
Many developers have been happily using Sonnet 4.5 as their daily driver. Should you upgrade to Opus?
The Performance Gap
Coding Benchmarks:
- SWE-Bench Verified: Opus 80.9% vs Sonnet 77.2% (3.7 point lead)
- This gap might seem small, but it represents hundreds of additional successfully resolved issues
Token Efficiency: At medium effort, Opus matches Sonnet’s best performance while using 76% fewer tokens. Even at high effort where Opus pulls ahead in accuracy, it uses only 50% of Sonnet’s token count.
Where Opus Justifies the Upgrade
1. Project-Scale Reasoning Opus doesn’t just write code — it manages entire projects. Early testers consistently report:
- Better architectural planning
- Stronger multi-file consistency
- More reliable long-running sessions
- Fewer “off the rails” moments in extended work
2. Autonomous Capability Opus demonstrates measurably stronger performance in autonomous workflows:
- Peak performance achieved in 4 self-improvement iterations
- Other models couldn’t match that quality after 10 iterations
- Better ability to learn from experience and apply insights
3. Computer Use Opus 4.5 is Anthropic’s strongest model yet for computer automation:
- Improved OSWorld benchmark performance
- New zoom tool for inspecting screen regions
- More reliable desktop task automation
4. Advanced Tool Use On τ2-Bench (airline service agent test), Opus demonstrated creative problem-solving that went beyond simple rule following. When faced with a policy that forbade changing a basic economy ticket, it found a legitimate loophole: upgrade the cabin class first, which then unlocked the modification capability.
The benchmark technically scored this as a failure because the approach was “unanticipated.” But this kind of creative, constraint-aware reasoning is exactly what makes Opus feel like a meaningful step forward.
Where Sonnet 4.5 Still Makes Sense
1. Cost
- Opus 4.5: $5 input / $25 output (per million tokens)
- Sonnet 4.5: $3 input / $15 output (per million tokens)
For high-volume workloads where cost matters more than peak performance, Sonnet is still highly competitive.
2. Speed Sonnet 4.5 is faster and more responsive for everyday tasks. When you need quick iterations and don’t require Opus-level reasoning depth, Sonnet’s snappier responses improve the development experience.
3. “Good Enough” for Many Tasks At 77.2% on SWE-Bench, Sonnet is still one of the strongest coding models available. For many workflows — simple bug fixes, straightforward features, documentation — Sonnet delivers excellent results at better speed and cost.
The Real-World Verdict: Opus 4.5 vs Sonnet 4.5
Upgrade to Opus 4.5 if you:
- Build complex, multi-file applications
- Run long autonomous coding sessions
- Need the absolute highest coding accuracy
- Work on architectural planning and refactoring
- Build sophisticated AI agents
- Can justify the cost premium (67% higher than Sonnet)
Stick with Sonnet 4.5 if you:
- Do rapid prototyping and iteration
- Run high-volume, cost-sensitive workloads
- Handle straightforward coding tasks
- Prioritize speed over depth
- Are happy with your current results
My honest recommendation? Start projects with Sonnet for quick iteration and cost efficiency. When you hit complex architectural decisions, long refactors, or need rock-solid autonomous execution, that’s when you call in Opus.
The Pricing Reality Check
Let’s talk money, because token costs add up fast in production:
Input / Output Costs (per million tokens):
- Opus 4.5: $5 / $25
- GPT-5.1-Codex-Max: Not yet public (API access “coming soon”)
- Gemini 3 Pro: $2 / $12 (under 200k tokens)
- Sonnet 4.5: $3 / $15
What This Means in Practice:
For a typical coding session with 50k input tokens and 20k output tokens:
- Opus 4.5: $0.75
- Gemini 3 Pro: $0.34
- Sonnet 4.5: $0.45
Gemini 3 Pro offers the best price-performance ratio if you’re counting pennies. But here’s the catch: if Opus solves your problem in one shot while another model takes three attempts, the “expensive” model becomes cheaper.
The efficiency advantage matters more than list prices. Opus 4.5’s ability to use 76% fewer tokens at medium effort means your actual costs are lower than the sticker price suggests.
Real-World Use Cases: Which Model for Which Job?
Building a Full-Stack Application from Scratch
Winner: Opus 4.5
- Best at project-scale consistency
- Autonomous testing and debugging
- Multi-file coordination
- Self-improving iterations
Runner-up: GPT-5.1-Codex-Max (if you need 24+ hour sessions)
Rapid UI Prototyping and Vibe Coding
Winner: Gemini 3 Pro
- WebDev Arena leader (1,487 Elo)
- Excellent zero-shot UI generation
- Strong visual aesthetics
- Fast iteration
Runner-up: Sonnet 4.5 (for cost efficiency)
Mathematical and Scientific Computing
Winner: Gemini 3 Pro
- 95% AIME performance without tools
- 23.4% MathArena Apex
- Strong scientific reasoning
- PhD-level problem solving
No close competition in this category.
Long-Horizon Autonomous Agents
Winner: GPT-5.1-Codex-Max
- Proven 24+ hour autonomous sessions
- Compaction across millions of tokens
- Coherent multi-day refactors
Runner-up: Gemini 3 Pro (best Vending-Bench results)
Production Bug Fixing and Maintenance
Winner: Opus 4.5
- 80.9% SWE-Bench accuracy
- Reliable autonomous debugging
- Consistent results across attempts
- Strong architectural understanding
Multimodal Applications (Image/Video/Audio)
Winner: Gemini 3 Pro
- Native multimodal architecture
- 81% MMMU-Pro score
- 87.6% Video-MMMU
- Unified cross-modal reasoning
No competition — the others don’t have comparable multimodal capabilities.
Cost-Sensitive High-Volume Workloads
Winner: Sonnet 4.5
- Strong performance at lower cost
- Faster response times
- Still near the top of coding benchmarks
- Proven reliability
Terminal Automation and System Operations
Winner: Opus 4.5
- 59.3% Terminal-Bench 2.0
- Strong command-line proficiency
- Better at sustained terminal workflows
Runner-up: GPT-5.1-Codex-Max (58.1%)
The Features Nobody’s Talking About (But Should Be)
Opus 4.5: Thinking Preservation
Previous Anthropic models discarded thinking blocks from earlier turns. Opus 4.5 preserves them in context by default, enabling:
- Better long-conversation coherence
- Learning from previous reasoning
- Consistent decision-making over time
Opus 4.5: Effort Parameter
Control how much computational effort the model uses:
- Low: Quick, lightweight tasks
- Medium: Balanced performance (76% fewer tokens than Sonnet at same quality)
- High: Maximum accuracy (still 50% fewer tokens than Sonnet)
This gives you cost/performance trade-offs other models don’t offer.
GPT-5.1-Codex-Max: Safety Improvements
- Higher refusal rates on malware tasks
- Strong prompt injection resistance
- Better destructive action avoidance
- Complete refusal on biorisk content
For enterprise deployments where security matters, these safeguards are significant.
Gemini 3 Pro: Generative UI
Gemini can generate complete user interfaces on the fly:
- Interactive web apps
- Data visualizations
- Custom tools
- Magazine-style layouts with filters
This opens entirely new workflows where the AI designs both content and user experience simultaneously.
Gemini 3 Pro: Deep Think Mode
For the hardest problems, Deep Think mode uses extended reasoning time:
- 41.0% on Humanity’s Last Exam (vs 37.5% standard)
- 45.1% on ARC-AGI-2 (vs 31.1% standard)
- Parallel reasoning chains
When you need maximum reasoning depth, Deep Think delivers measurably better results.
What The Benchmarks Don’t Tell You
Personality and Interaction Style
Opus 4.5: Professional, thorough, occasionally cautious. Excellent at explaining its reasoning. Sometimes needs encouragement to try unconventional approaches.
GPT-5.1-Codex-Max: Extremely literal and persistent. Follows instructions to the letter, even when that leads to over-engineered solutions. Some users find this frustrating; others appreciate the predictability.
Gemini 3 Pro: More conversational and creative. Willing to challenge assumptions and suggest alternatives. Excels at “thought partnership” beyond just code generation.
Sonnet 4.5: Fast, efficient, gets to the point quickly. Less likely to over-explain. Better at “vibing” what you want with minimal prompting.
Reliability and Consistency
All four models occasionally fail in ways that benchmarks don’t capture:
- Opus 4.5: Sometimes makes up replacement tools when it can’t access what it needs, rather than asking for help
- GPT-5.1-Codex-Max: Can over-optimize based on old instructions you forgot about
- Gemini 3 Pro: Occasional inconsistency in multi-turn conversations
- Sonnet 4.5: Faster means it sometimes rushes through complex reasoning
The AI Development Landscape: What This All Means
We’re at an inflection point. For the first time, we have AI models that can:
- Work autonomously for 24+ hours
- Generate and test complete applications
- Debug their own code
- Navigate complex policy constraints with creative problem-solving
- Maintain coherence across millions of tokens
But here’s what’s really happening: Each major AI lab is optimizing for different end states.
Anthropic (Opus/Sonnet) is building toward the most reliable autonomous coding agent. Their focus on safety, alignment, and consistent behavior shows a bet on AI as a dependable development partner.
OpenAI (GPT-5.1-Codex-Max) is pushing toward ultra-long-horizon tasks through compaction. They’re optimizing for scenarios where an AI agent works on complex problems for days with minimal intervention.
Google (Gemini 3 Pro) is building universal intelligence — a single model that can reason across any modality and any domain. Their bet is on breadth and versatility over specialized excellence.
The winner? Depends entirely on your workflow. There is no single “best” model anymore.
My Final Recommendations
After extensive testing and analysis, here’s my honest advice:
For Professional Software Developers
Primary: Opus 4.5 for complex work, Sonnet 4.5 for iteration When to switch: Use Codex-Max for marathon refactoring sessions; use Gemini for UI work
For AI/ML Researchers
Primary: Gemini 3 Pro for its reasoning capabilities When to switch: Opus 4.5 when you need rock-solid code generation
For Startups Building Products
Primary: Sonnet 4.5 for cost efficiency and speed When to switch: Upgrade to Opus 4.5 for architectural decisions and complex features
For Enterprise Teams
Primary: Depends on ecosystem (AWS → Opus, Google Cloud → Gemini, OpenAI committed → Codex-Max) Critical factor: Safety, compliance, and integration matter more than benchmark scores
For Data Scientists
Primary: Gemini 3 Pro for mathematical reasoning and data visualization When to switch: Opus 4.5 for production ML pipelines
For Content Creators and Designers
Primary: Gemini 3 Pro for multimodal workflows Alternative: Use AI Studio’s “Build Mode” for instant prototyping
The Bottom Line
Opus 4.5 is the most accurate coding model available right now. If you need the highest success rate on real-world software engineering tasks, nothing beats it. The 80.9% SWE-Bench score isn’t just a number — it’s a fundamental capability threshold that other models haven’t crossed.
GPT-5.1-Codex-Max is the marathon runner. For ultra-long-horizon tasks where you need an agent to work coherently for 24+ hours across millions of tokens, it’s purpose-built for that workflow.
Gemini 3 Pro is the polymath. Strongest reasoning, best multimodal capabilities, excellent at mathematics and scientific tasks. When your workflows span multiple domains and modalities, Gemini’s versatility shines.
Sonnet 4.5 is the practical choice. For most developers, most of the time, it delivers excellent results at great speed and cost. It’s still a top-tier model that beats everything that came before it.
The Question You Should Really Be Asking
It’s not “which model is best?” — it’s “which model fits my specific workflow?”
- Are you building a production app? Opus 4.5
- Are you running 24-hour autonomous agents? GPT-5.1-Codex-Max
- Are you working with images, audio, and video? Gemini 3 Pro
- Are you prototyping rapidly on a budget? Sonnet 4.5
We’ve entered an era where the right answer is “all of them, for different tasks.” The best developers will learn which model excels at which job and orchestrate them accordingly.
That’s not a cop-out — it’s the reality of a mature AI landscape where specialization matters more than raw leaderboard positions.
What’s Next?
The pace of improvement is accelerating. We went from GPT-3 to Opus 4.5 in just three years. Within the past week alone, we’ve seen three frontier models that would each have been considered impossible a year ago.
The key insight? Stop betting on a single model. Build your workflows to be model-agnostic. The model that’s best today might not be best next month, and the ability to swap models based on the task is becoming a crucial skill.
Welcome to the multi-model future. Choose wisely.
Related Deep-Dive Guides
Want to go even deeper on specific models? Check out our comprehensive technical guides:
Opus 4.5 Benchmark & Agentic Coding: Technical Deep Dive
Get the complete technical breakdown of Opus 4.5’s benchmark performance, agentic coding capabilities, and how it stacks up in real-world development scenarios. Perfect for developers who want to understand the engineering behind the 80.9% SWE-Bench achievement.
What you’ll learn:
- Detailed benchmark analysis with methodology breakdowns
- Agentic coding workflow examples and best practices
- Performance optimization techniques for Opus 4.5
- Real-world case studies and implementation patterns
- Token efficiency strategies and cost optimization
Gemini 3 Complete Guide for Developers
Master Gemini 3 Pro’s unique capabilities with our comprehensive developer guide. Learn how to leverage its multimodal intelligence, mathematical reasoning, and long-horizon planning for your projects.
What you’ll learn:
- Complete API integration walkthrough
- Multimodal development patterns (text, image, video, audio)
- Mathematical and scientific computing workflows
- Vertex AI integration for enterprise teams
- Advanced prompt engineering for Gemini
- Cost optimization and performance tuning
This comparison is based on verified benchmarks and real-world testing as of November 2025. Model capabilities and pricing are subject to change. Always test models on your specific use cases before making production decisions.
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