There’s a temptation in AI to always reach for the biggest, most powerful model. The latest release. The premium tier. The one with the best benchmarks. Especially given the incredible pace of competition and innovation in AI + B2B today.
And sometimes that’s exactly right. That’s where I start.
But sometimes it isn’t.
We’ve now run 3,035+ VC pitch decks through SaaStr AI VC, and I recently ran a head-to-head test: Claude Opus 4.5 vs. Claude Sonnet 4.5 across 100+ pitch decks to see if the premium model was worth the premium price.
Here’s what the pricing difference actually looks like:
That’s 67% more expensive on input and 67% more expensive on output for Opus 4.5.
I was confident Opus would be worth it. The benchmarks are better. It’s supposed to be smarter. More nuanced. Better at complex reasoning.
I was wrong.
The results across analyzing 100+ VC pitch decks were essentially identical.
Same quality assessments. Same pattern recognition. Same actionable feedback for founders. The extra horsepower didn’t translate into better pitch deck analysis.
And Opus is also slower. Sonnet 4.5 is optimized for high-throughput scenarios where low latency is critical. When you’re processing thousands of pitch decks, that speed difference compounds. Slower responses mean worse user experience and higher costs. Double penalty.
Why This Matters for B2B Founders (and AI Teams)
We’re processing 275,000+ startup valuations monthly on SaaStr.ai. At that scale, the difference between $3 and $5 per million input tokens adds up fast. Same for $15 vs. $25 on outputs.
But more importantly, this is a lesson every SaaS company building with AI needs to internalize:
The “best” model isn’t always the best model for YOUR use case.
Why Claude Beat OpenAI for Our Use Case
Here’s something else we discovered: for pitch deck analysis specifically, both Claude models outperformed OpenAI’s offerings.
Why? Our hypothesis: Claude is simply better optimized for PDF extraction and document analysis.
Pitch decks are PDFs. They’re a mix of text, charts, tables, and visuals. Claude’s 200,000 token context window means it can process entire decks without losing information from earlier slides. The model handles the full document in one pass, maintaining context from the team slide when evaluating the market slide, and connecting the traction metrics to the financial projections.
OpenAI’s models are excellent at many things. But for our specific workflow—extracting structured insights from PDF documents at scale—Claude consistently delivered better results. The analysis was more coherent. The pattern recognition across slides was stronger. The feedback was more actionable.
This isn’t about Claude being “better” than OpenAI in some absolute sense. It’s about fit for purpose. Different models have different strengths. For document-heavy, PDF-centric workflows, Claude has been the right tool for us.
Here’s what I’ve learned running 20+ AI agents across SaaStr that generate over $1M in revenue:
When to Use the Premium Model (Opus 4.5)
- Complex, multi-step reasoning chains
- Agentic workflows that run autonomously for extended periods
- Tasks where a single mistake cascades into major problems
- Code refactoring across multiple files and systems
- Anything where you need the absolute highest accuracy and speed isn’t critical
When the Mid-Tier Model (Sonnet 4.5) is the Right Call
- Structured analysis with clear evaluation criteria (like pitch decks)
- High-volume processing where consistency matters more than brilliance
- Tasks with well-defined inputs and outputs
- Document summarization and extraction
- Most production workloads where latency matters
- User-facing applications where response time affects experience
When the Budget Model (Haiku 4.5) Actually Works Great
Here’s something else we learned: for basic enrichment tasks, even the cheapest model works fine. Maybe even better, because you can use it more often.
Haiku 4.5 costs just $1 / million input tokens and $5 / million output tokens—that’s 80% cheaper than Sonnet and 95% cheaper than Opus on outputs.
For simple tasks like:
- Data enrichment and normalization
- Basic classification and tagging
- Extracting structured fields from text
- Simple reformatting and cleanup
Haiku handles it. The results are good enough. And at that price point, you can process massive volumes without thinking twice about cost. We use Haiku for the “grunt work” in our pipeline—the preprocessing steps that don’t require sophisticated reasoning. Save the expensive models for where they actually make a difference.
When to Consider Claude Over OpenAI
- PDF-heavy workflows with complex document structures
- Large context requirements (200K tokens vs. smaller windows)
- Document analysis where you need to maintain context across the entire file
- Structured data extraction from visual documents
The pitch deck analysis was a perfect example of the second category. We have clear criteria: team strength, market size, traction, product-market fit signals, competitive positioning. The model needs to consistently apply these frameworks across thousands of decks.
Opus 4.5 didn’t give us “better” insights. It gave us the same insights at 67% higher cost.
Different Tools (Er, LLMs) for Different Jobs
This is why I’ve been saying that the real AI advantage isn’t about who has access to the fanciest models—everyone does now. It’s about:
- Knowing which model to use for which task
- Knowing which provider is best for your specific workflow
- Building the workflows that leverage AI effectively
- Optimizing the cost structure as you scale
We started with GPT-4 for everything when we built the first version of SaaStr.ai. Then we tested Claude. Then we tested different Claude models for different tasks. Now we have a hybrid setup where different models handle different workloads based on what actually performs best.
The pivot to Claude for pitch deck analysis wasn’t ideological. It was empirical. We ran the tests. Claude won on our specific use case. And within Claude, Sonnet won over Opus for our specific use case.
The founders who will win with AI aren’t the ones throwing the most expensive models at every problem. They’re the ones who understand their use cases deeply enough to match the right tool to the right job.
What This Means for Your AI Strategy
If you’re building AI into your B2B product:
1. Test your assumptions. I was sure Opus would be better. I was wrong. Run the experiment.
2. Define your success metrics clearly. “Better” is meaningless without criteria. For pitch deck analysis, we could measure quality because we knew what good analysis looked like.
3. Consider the economics at scale. A 67% cost premium might be negligible at 100 requests per day. At 275,000+ valuations per month, it’s a completely different conversation.
4. Re-test when models update. Six months from now, the calculus might change. New model releases can shift which tier makes sense for which use case.
The AI landscape is moving too fast for “set it and forget it” decisions. The best approach is continuous experimentation with clear metrics.
We’re analyzing 3,000+ pitch decks and counting on SaaStr.ai. If you’re a founder, get your deck scored. If you’re building AI into your product, learn from our mistakes—and our experiments.
Pick the right tool for the right job. It will vary.
