Claude Opus 4.5 vs 4.1: 3x Cheaper & Better?
After a long wait, Anthropic released its new model Claude Opus 4.5, a few days ago on November 24, 2025, this model joins the rest of the Claude 4.5 family, the new model comes with new abilities, as we always expect from Anthropic, but it also has a better price than the previous version, which is something we are not used to from the company.
This release came only one week after Google launched its powerful new model, Google Gemini 3 Pro, and after OpenAI released GPT-5.1. This increases the competition between the three leading AI companies, Opus 4.5 is priced 66–67% lower than the earlier Opus 4.1, which is a major price drop for a top model from Anthropic.
Release Timeline and Availability
Anthropic released Claude Opus 4.1 on August 5, 2025. Just a few months later, on November 24, 2025, the company launched Opus 4.5. This quick turnaround was part of a busy release schedule. Anthropic pushed out three major AI models in less than two months: Claude Sonnet 4.5 arrived on September 29, Claude Haiku 4.5 followed on October 15, and then Opus 4.5 closed out the trio in November.
Where You Can Access Opus 4.5
The new model became available across multiple platforms right away. Developers can access it through the Claude API using the model identifier claude-opus-4-5-20251101. Major cloud providers also jumped on board quickly Amazon Bedrock, Google Cloud Vertex AI, and Microsoft Azure all offer Opus 4.5 through their platforms.
Microsoft moved especially fast. The company made Opus 4.5 available in Azure Foundry and Copilot Studio on the same day as the public release. GitHub Copilot users with Enterprise or Pro+ subscriptions can now test Opus 4.5 in public preview mode.
If you’re not a developer, you can still use Opus 4.5. The model works on Claude.ai for Pro, Max, and Team users. Anthropic also built it into Claude for Chrome and Claude for Excel, though the Excel integration is still in beta.
Platform availability at launch:
- Claude API (direct access)
- Amazon Bedrock
- Google Cloud Vertex AI
- Microsoft Azure Foundry
- GitHub Copilot (Enterprise/Pro+ preview)
- Claude.ai web interface (Pro, Max, Team tiers)
- Claude for Chrome browser extension
- Claude for Excel (beta)
One thing worth noting: Anthropic removed the special usage caps that previously limited Opus models. Max and Team Premium users now get roughly the same number of Opus tokens they previously had with Sonnet. This makes the powerful new model much more accessible for everyday work
Pricing Comparison
One of the biggest surprises with Opus 4.5 is how much cheaper it got. Anthropic cut prices dramatically while actually improving the model’s performance.
Here’s what changed: Input tokens now cost $5 per million, down from $15 in Opus 4.1. Output tokens dropped even more—from $75 per million to just $25. When you do the math, that’s a 67% cost reduction across the board.
This breaks an old pattern in AI development. Usually, more powerful models cost more money. But Opus 4.5 flips that script. It’s both smarter and cheaper than its predecessor.
| Pricing Element | Opus 4.5 | Opus 4.1 | Savings |
|---|---|---|---|
| Input tokens | $5/million | $15/million | 67% |
| Output tokens | $25/million | $75/million | 67% |
How It Stacks Up Against Competitors
Even with these price cuts, Opus 4.5 isn’t the cheapest option out there. GPT-5.1 costs $1.25 for input and $10 for output per million tokens. Gemini 3 Pro runs at $2 for input and $12 for output. So if you’re running high-volume operations where every dollar counts, those models might still make more sense.
But the pricing tells only part of the story. Opus 4.5 delivers top-tier performance on complex tasks, especially coding and reasoning challenges. For work that needs that level of capability, the higher price can be worth it.
Ways to Save Even More
Anthropic built in several features to help you cut costs further:
Prompt caching saves money when you use the same content repeatedly. The system stores frequently used prompts and charges much less to reuse them. Cache writes cost around $6.25 per million tokens for a 5-minute window, or $10 for a 1-hour window. Reading from the cache? That’s only $0.50 per million tokens. At best, this can save you up to 90% on repeated content.
Batch processing gives you a 50% discount if you can wait for results. This works great for bulk jobs that don’t need instant answers. Input costs drop to $2.50 per million tokens, and output falls to $12.50. If you’re processing large amounts of data overnight or during off-hours, batch mode makes a lot of sense.
These optimization tools mean your actual costs can vary widely depending on how you use the model. A project with lots of repeated prompts might end up costing 90% less than the standard rates suggest.
Technical Specifications
Both Opus 4.5 and Opus 4.1 can handle 200,000 tokens in their context window. That’s the amount of text they can process at once. Think of it like working memory—how much information the model can keep in mind while working on a task.
But Opus 4.5 made one major improvement here. The maximum output doubled from 32,000 tokens to 64,000 tokens. This matters when you need long, detailed responses. If you’re generating comprehensive reports, writing extensive code files, or creating lengthy analyses, that extra output space gives you more room to work.
What the Models Know
The knowledge cutoff dates differ between the two versions. Opus 4.1 has reliable knowledge through January 2025. Opus 4.5 pushes that forward to March 2025. Two months might not sound like much, but it means Opus 4.5 knows about events and information from early 2025 that Opus 4.1 missed.
There’s also training data to consider. Anthropic trained Opus 4.5 on information collected through August 2025. This means the model learned from examples and patterns in data from those months, even though its reliable factual knowledge stops earlier in March.
Why the gap? Training data helps the model learn how to reason and respond, while knowledge cutoff marks where it can reliably recall facts and events. The training data cutoff comes later because the model needs time to learn patterns from that information before release.
| Specification | Opus 4.5 | Opus 4.1 |
|---|---|---|
| Context window | 200K tokens | 200K tokens |
| Maximum output | 64K tokens | 32K tokens |
| Knowledge cutoff | March 2025 | January 2025 |
| Training data through | August 2025 | Not specified |
The doubled output limit stands out as the most practical upgrade. Long-form content generation becomes much easier when you don’t hit token limits halfway through a response.
Performance Benchmarks
Opus 4.5 made history by becoming the first AI model to break 80% on SWE-bench Verified. This benchmark tests how well models can handle real-world software engineering tasks. Opus 4.5 scored 80.9%, while Opus 4.1 reached 74.5%. That 6.4 percentage point jump represents a major leap in coding ability.
Breaking Down the Numbers
The improvements show up across different types of challenges. On ARC-AGI 2, which tests abstract reasoning without relying on memorized patterns, Opus 4.5 hit 37.6%. This matters because it measures how well the model can think through new problems it’s never seen before. For context, GPT-5.1 scored 17.6% on this same test—Opus 4.5 more than doubled that score.
Computer use capabilities got a huge boost too. OSWorld measures how well AI models can actually control computers, navigate interfaces, and complete tasks. Opus 4.5 scored 66.3%, the highest result ever recorded on this benchmark.
Graduate-level reasoning saw impressive results as well. On GPQA Diamond, which throws difficult questions at the model that would challenge people with advanced degrees, Opus 4.5 achieved 87.0%. The test covers complex topics that require deep understanding, not just surface-level knowledge.
Key benchmark results for Opus 4.5:
- SWE-bench Verified: 80.9% (first to exceed 80%)
- ARC-AGI 2: 37.6% (abstract reasoning)
- OSWorld: 66.3% (computer use)
- GPQA Diamond: 87.0% (graduate-level questions)
- Aider Polyglot: 89.4% (multilingual coding)
What This Means for Coding
Aider Polyglot tests coding skills across multiple programming languages. Opus 4.5 scored 89.4% here, showing it can work effectively whether you’re writing Python, JavaScript, or other languages. The model also topped leaderboards in 7 out of 8 programming languages on SWE-bench Multilingual.
Terminal-Bench measures command-line skills—how well the model can work in a developer’s terminal environment. Opus 4.5 reached 59.3%, significantly ahead of GPT-5.1’s 47.6% and Gemini 3 Pro’s 54.2%. This suggests the model understands not just how to write code, but how to use developer tools effectively.
These benchmark scores paint a clear picture. Opus 4.5 handles complex coding tasks better than its predecessor, reasons through abstract problems more effectively, and controls computer interfaces with greater accuracy. The improvements aren’t small tweaks—they represent substantial gains in capability.
Unique Features in Opus 4.5
Opus 4.5 introduces several capabilities that set it apart from both its predecessor and competing models. These aren’t just incremental improvements they represent entirely new ways to control and use AI.
Effort Parameter Control
Opus 4.5 is the only Claude model that lets you adjust how hard it thinks about a problem. You can set it to low, medium, or high effort depending on what you need. Low effort gives you faster responses using fewer tokens, which works great for straightforward tasks. Medium effort matches Sonnet 4.5’s quality while using 76% fewer output tokens. High effort pushes the model to its limits, exceeding Sonnet 4.5 by 4.3 percentage points while still using 48% fewer tokens.
This granular control means you can balance performance against cost and speed for each specific task. Need a quick answer? Drop the effort level. Working on something complex and critical? Crank it up.
Computer Use Gets Smarter
The new zoom action changes how Opus 4.5 interacts with computer interfaces. The model can now request zoomed-in views of specific screen regions to inspect fine-grained UI elements. This helps when reading small text, analyzing complex interfaces, or verifying precise details before taking actions. It’s particularly useful for tasks that require examining fine print or working with dense visual information.
This capability helped Opus 4.5 achieve that record-breaking 66.3% score on OSWorld.
Thinking Block Preservation
When working on complex, multi-turn projects, Opus 4.5 automatically saves all its previous thinking blocks throughout the conversation. This maintains reasoning continuity across extended interactions. The model can leverage its full reasoning history when tackling challenging problems that require multiple steps. For long-running tasks involving tool use, this feature ensures the model doesn’t lose track of its previous insights and decisions.
Multi-Agent Coordination
Opus 4.5 excels at managing teams of AI agents working together. On multi-agent search tasks, it scored 92.3% compared to Sonnet 4.5’s 85.4%. The model seamlessly coordinates hundreds of tools in complex workflows and performs particularly well with workflows requiring 10 or more tools. This makes it effective for end-to-end software engineering, cybersecurity operations, and financial analysis that need multiple specialized agents working in harmony.
Learning on the Fly
Perhaps most impressively, Opus 4.5 can improve itself as it works. Testing by Rakuten showed agents reached peak performance in just 4 iterations. Other models couldn’t match this quality even after 10 attempts. The model demonstrated the ability to autonomously refine its own capabilities, storing insights and applying them to later tasks. This self-improving behavior enables the construction of complex, well-coordinated multi-agent systems that get better with experience.
Combined with memory capabilities, these techniques boosted Opus 4.5’s performance on deep research evaluations by almost 15 percentage points.
Token Efficiency
Opus 4.5 achieves something rare in AI development: it gets smarter while using fewer tokens. This goes against the usual pattern where more powerful models generate longer responses filled with reasoning steps.
When set to medium effort, Opus 4.5 delivers the same quality as Sonnet 4.5’s best work while using 76% fewer output tokens. Push it to high effort, and it doesn’t just match Sonnet 4.5 it beats it by 4.3 percentage points, all while still using 48% fewer tokens.
The Cost-Performance Sweet Spot
These numbers have real implications. Output tokens cost money, and using fewer of them means lower bills. If you’re running hundreds or thousands of requests daily, that 76% reduction in token usage translates to substantial savings.
Speed improves too. Generating fewer tokens means faster response times. The model doesn’t need to produce lengthy chains of reasoning to arrive at good answers. It thinks efficiently.
Efficiency gains at different settings:
- Medium effort: Same quality as Sonnet 4.5, 76% fewer tokens
- High effort: Better than Sonnet 4.5, 48% fewer tokens
Standing Out from the Crowd
Independent benchmarking placed Opus 4.5 on the Pareto frontier for Intelligence Index versus Output Tokens Used. This is a technical way of saying it achieves the best possible balance no other model gives you more intelligence per token spent.
Many competing reasoning models work by generating massive amounts of tokens during inference. They essentially “think out loud” through problems, which produces better results but at the cost of using far more tokens. Opus 4.5 took a different path. Anthropic built the intelligence directly into the model rather than relying on verbose reasoning at runtime.
For developers and businesses, this efficiency matters beyond just cost. It makes complex AI features practical to deploy at scale. Projects that would be too expensive to run with token-heavy models become financially viable with Opus 4.5.
Ideal Use Cases
Opus 4.5 shines brightest when tackling work that demands sustained intelligence and multi-step problem-solving. While you could use it for simple tasks, its real value emerges with complex projects that would challenge other models.
Professional Software Engineering
The model excels at the messy, complicated coding work that takes humans days or weeks. Complex multi-file refactoring becomes manageable Opus 4.5 can track changes across an entire codebase while maintaining architectural consistency. Code migration projects, where you’re moving from one framework or language to another, benefit from its ability to understand both the old and new systems.
GitHub reported that Opus 4.5 “delivers high quality code and excels at powering heavy duty agentic workflows” while cutting token usage in half. Developers see 50% to 75% reductions in both tool calling errors and build/lint errors. The model handles improved multilingual coding, better test coverage, and cleaner architectural choices.
Advanced AI Agents
For autonomous agents that need to work independently over extended periods, Opus 4.5 sets a new standard. The model maintains coherence through 30-hour autonomous sessions, handling long-horizon tasks that require planning, execution, and self-correction over extended timeframes. It demonstrated consistent performance through 30 minute coding sessions without getting lost or confused.
The self-improving capability makes a real difference here. Agents reached peak performance in just 4 iterations compared to 10+ attempts needed by other models.
Computer Use Automation
That record breaking 66.3% OSWorld score translates to practical benefits. Browser automation, desktop task automation, and UI testing all become more reliable. The zoom action feature helps when navigating complex interfaces or reading fine print. Opus 4.5 handles office productivity tasks end to end, creating spreadsheets, presentations, and documents with professional polish.
Complex Research and Analysis
Deep research projects that require synthesizing information from multiple sources play to Opus 4.5’s strengths. Patent analysis, technical documentation review, and financial modeling all benefit from the model’s ability to maintain context over long documents. Strategic planning and decision-making tasks that need careful reasoning work well too.
The model’s memory capabilities boost performance on research evaluations by almost 15 percentage points when combined with other techniques.
Enterprise Workflows
When workflows require coordinating 10 or more tools, Opus 4.5 really stands out. It seamlessly manages complex multi-step automation across cybersecurity operations, full stack software engineering, and financial analysis. Multi-agent orchestration scored 92.3%, showing the model can coordinate teams of specialized agents effectively.
Microsoft noted that Opus 4.5 works well for “complex, multi system development tasks with minimal supervision”. Amazon highlighted its ability to “manage full-stack architectures” and “design agentic systems that break down high-level goals into executable steps”.
Where Opus 4.5 makes the most impact:
- Software projects spanning multiple files and systems
- Autonomous agents running for hours or days
- Tasks requiring precise computer control
- Research needing deep analysis across sources
- Workflows coordinating many tools and systems
For simpler or faster tasks where maximum intelligence isn’t necessary, Sonnet 4.5 or Haiku 4.5 offer better cost-efficiency. But when the work is genuinely complex and the quality matters more than speed, Opus 4.5 delivers results other models can’t match
Safety and Security
Anthropic built Opus 4.5 with serious safety measures in mind. The model operates under ASL-3 protections, which stands for AI Safety Level 3. This represents Anthropic’s classification system for AI safety, ensuring the model meets strict standards before deployment.
Industry Leading Protection Against Attacks
Prompt injection attacks pose a major threat to AI systems. These attacks try to trick the model into ignoring its instructions and following hidden malicious commands instead. Hackers and cybercriminals use these techniques to hijack AI systems for harmful purposes.
Opus 4.5 demonstrates the strongest defense against these attacks in the industry. Testing with Gray Swan’s evaluation tool showed the model has an attack success rate of just 4.7%. Compare that to Gemini 3 Pro at 12.5% and GPT-5.1 at 21.9%. Even when attackers try repeatedly, Opus 4.5 proves significantly harder to fool than competing models.
For computer use tasks specifically, the results look even better. With extended thinking enabled, Opus 4.5 fully saturated the benchmark even after 200 attack attempts, most attackers failed to find a working exploit.
Resistance to Misuse
Beyond defending against external attacks, Opus 4.5 shows strong internal alignment. The model achieved the lowest “concerning behavior” score among frontier AI models for resistance to misuse. It exhibits roughly 10% less concerning behavior than GPT-5.1 and Gemini 3 Pro.
What does this mean practically? The model is less likely to cooperate with harmful requests or take problematic actions on its own initiative. It shows high resistance to knowingly following harmful system prompts while maintaining useful behavior for legitimate tasks. Anthropic describes it as “the most robustly aligned model we have released to date”.
Safety improvements in Opus 4.5:
- 4.7% prompt injection success rate (lowest in industry)
- ~10% less concerning behavior than competitors
- ASL-3 safety level protections
- Enhanced alignment and refusal of harmful requests
The model also improved its ability to detect when something seems suspicious in tool calls or requests. This heightened awareness helps prevent misuse while still allowing the model to handle complex legitimate tasks effectively.
These safety features matter especially for enterprise deployments where AI systems handle sensitive data or critical operations. Companies need assurance that their AI won’t be easily compromised or manipulated by bad actors
Real World Performance Feedback
Major tech companies and developers who tested Opus 4.5 early shared positive reactions about its real-world performance.
GitHub’s Assessment
GitHub made Opus 4.5 available in Copilot on the same day Anthropic released it publicly. Their team praised the model’s coding abilities, noting it “delivers high-quality code and excels at powering heavy-duty agentic workflows”. They found it particularly useful for challenging tasks like code migration and refactoring—the kind of work that involves restructuring existing code while keeping everything functional.
Internal testing at GitHub showed the model “surpasses internal coding benchmarks while cutting token usage in half”. This combination of better performance and greater efficiency stood out as a major advantage.
Microsoft’s Quick Adoption
Microsoft moved fast to integrate Opus 4.5 into their platforms. The model became available in Copilot Studio the same day as the public release. This quick turnaround showed Microsoft’s confidence in the model’s readiness for production use.
According to Microsoft’s team, Opus 4.5 “enables continuous improvement in agent performance”. This capability matters for businesses building AI systems that need to get better over time without constant manual retraining.
Developer Reactions
Developers testing Opus 4.5 appreciated its polish and reliability. One common theme emerged: “Claude Opus 4.5 is smooth, with none of the rough edges we’ve seen from other frontier models”. This feedback suggests the model handles edge cases and unexpected situations more gracefully than competitors.
Speed improvements caught attention too. Developers noted that “speed improvements are remarkable” compared to earlier Opus versions. While still not as fast as Sonnet 4.5, the model performs noticeably better than Opus 4.1.
Key feedback themes:
- Handles complex coding with fewer errors
- Works smoothly without unexpected failures
- Manages long projects more efficiently
- Uses significantly fewer tokens for same results
For long-running projects, developers found that Opus 4.5 “handles long-horizon coding tasks more efficiently than any model we’ve tested”. The model maintains focus and quality even during extended sessions that might trip up other AI systems.
Testing also revealed practical efficiency gains. Opus 4.5 “achieves higher pass rates on held-out tests while using up to 65% fewer tokens”. This means it not only produces correct results more often, but does so with substantially less computational overhead.
The consistent message across these early adopters points to a model that delivers on its benchmark promises in actual production environments. Companies integrated it quickly because it worked reliably from day one
Which Model Should You Choose?
The choice between Opus 4.5 and Opus 4.1 is straightforward for most users. Opus 4.5 outperforms its predecessor in virtually every way while costing 67% less. This makes it the clear choice for new projects and most existing work.
| Factor | Choose Opus 4.5 | Choose Opus 4.1 |
|---|---|---|
| New Projects | Always better performance at lower cost | Not recommended |
| Complex Coding | 80.9% on SWE-bench, superior multi-file handling | 74.5% on SWE-bench, adequate but outdated |
| Cost Sensitivity | $5/$25 per million tokens | $15/$75 per million tokens |
| Output Length | Up to 64K tokens | Limited to 32K tokens |
| Computer Use | 66.3% OSWorld score with zoom feature | No zoom capability, lower scores |
| AI Agents | Self-improving, multi-agent orchestration | Basic agent capabilities |
| Knowledge Cutoff | March 2025 | January 2025 |
| Legacy Systems | May require testing/adjustment | Already integrated and tested |
When Opus 4.5 Makes Sense
For almost all scenarios, Opus 4.5 represents the better investment. You get more capability, better efficiency, and pay less money. The effort parameter alone gives you control that Opus 4.1 simply can’t match. Computer use tasks benefit from the zoom action feature. Long-form content generation takes advantage of the doubled output limit.
The model’s token efficiency means your actual costs drop even further than the base price reduction suggests. Projects that would strain your budget with Opus 4.1 become financially viable with Opus 4.5.
The Only Case for Opus 4.1
Legacy compatibility represents the primary reason to continue using Opus 4.1. If you built systems specifically tuned to Opus 4.1’s behavior, switching models might require retesting and adjustments. Some organizations prefer to avoid changes to working systems, especially in production environments where stability matters more than cutting-edge performance.
However, even for legacy projects, the cost savings and performance improvements in Opus 4.5 usually justify the effort of migrating. The research data describes Opus 4.5 as “superior in every measurable dimension” compared to Opus 4.1.
The Bottom Line
Opus 4.5 delivers what AI development rarely sees a model that’s simultaneously more powerful and significantly cheaper than what came before. Unless you have specific technical reasons tied to legacy infrastructure, Opus 4.5 should be your default choice. It represents a generational leap, not just an incremental upgrade.
Final Words
Claude Opus 4.5 achieves something rare in technology: it’s both significantly smarter and dramatically cheaper than what came before. The model delivers a 21% intelligence improvement while cutting costs by 67%. This breaks the usual trade off where better performance means higher prices.
The numbers tell a clear story. Opus 4.5 outperforms Opus 4.1 across every major benchmark from the 80.9% SWE-bench Verified score that made it the first model to break 80%, to the 37.6% on ARC-AGI 2 that more than doubles competing models. It introduced capabilities Opus 4.1 never had, like the effort parameter for fine-tuned control, zoom actions for computer use, and self-improving agents that reach peak performance in just 4 iterations.
Token efficiency compounds these advantages. Opus 4.5 achieves superior results while using 48% to 76% fewer tokens than other approaches. This means the actual cost savings exceed the already-impressive 67% price reduction.
The research data describes this simply: Opus 4.5 is “superior in every measurable dimension” compared to Opus 4.1. It represents a generational leap forward in AI capabilities better at coding, stronger at reasoning, more capable with autonomous agents, and more affordable to use. For developers and businesses choosing between these models, Opus 4.5 makes the decision straightforward. It delivers everything Opus 4.1 offered and substantially more, at a fraction of the cost.
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Written By :
Mohamed Ezz
Founder & CEO – MPG ONE
