Teams often ask a simple question: “Should we pay for the most premium model, or can we get similar value for less?” To answer that, we’ll compare GPT-5 and Claude Opus 4 using the pricing data in our calculator (updated February 2026). This is not a benchmark shootout. It’s a cost and capability profile to help you decide which model makes sense for your use case.
The raw numbers
Here are the current rates and key limits from our models data:
| Model | Input / 1M tokens | Output / 1M tokens | Context window | Max output |
|---|---|---|---|---|
| GPT-5 | $1.25 | $10.00 | 1,000,000 | 131,072 |
| Claude Opus 4.6 | $5.00 | $25.00 | 200,000 | 128,000 |
The price gap is significant. On input tokens, Claude Opus 4.6 costs 4x more. On output tokens, it’s 2.5x more. For workloads heavy on long generations, the output rate is the most important line item.
What the price difference means in practice
Let’s say your typical request is 2,000 input tokens and 800 output tokens. That is a common ratio for summarization, synthesis, and extraction flows.
- GPT-5 cost per request: about $0.0105
- Claude Opus 4.6 cost per request: about $0.0280
That gap compounds quickly at scale. At 100,000 requests per month, GPT-5 costs about $1,050, while Claude Opus 4.6 costs about $2,800. If you’re running a high-volume app, the difference is material.
Context window and long-form workflows
GPT-5 provides a 1,000,000 token context window in our data, which is 5x the 200,000 context window of Claude Opus 4.6. That matters for long RAG prompts, multi-document agents, and large tool schemas. If your workload regularly uses 200k+ input tokens, GPT-5 is the only one of the two that can handle it without heavy truncation or chunking.
On max output tokens, they are effectively tied: 131,072 vs 128,000. So if you need massive outputs, both can deliver, but you should still set a strict max to control costs.
Where Claude Opus 4.6 can still be the right call
Price is not the only signal. Claude Opus has a reputation for strong instruction-following and safe responses in many teams’ anecdotal experience. If your product needs highly reliable outputs, fewer retries, or less post-processing, the premium could be worth it. That value is hard to quantify, but it can show up as lower engineering effort or higher user trust.
You can also route only the hardest prompts to Claude Opus 4.6 and keep the rest on GPT-5. This keeps quality high on edge cases while preserving budget across the broader workload.
GPT-5 vs GPT-5.2: a quick note
Our pricing data also includes GPT-5.2 at $1.75 input / $14 output. That is still meaningfully cheaper than Claude Opus 4.6 on output tokens, but it is a 40% increase over GPT-5. If you don’t need the incremental improvements, GPT-5 remains the most cost-effective option in the premium OpenAI tier.
A practical decision framework
If you’re deciding which model to ship, use this simple checklist:
- You need 200k+ context windows without chunking: start with GPT-5.
- You need the highest possible quality and can absorb cost: test Claude Opus 4.6.
- You have high volume and strict cost targets: default to GPT-5 and route only hard cases to Claude Opus 4.6.
- Your output is large and expensive: prioritize the lower output token price.
Recommendation
Most teams should start with GPT-5 for baseline workloads because the cost per request is dramatically lower and the context window is larger. Claude Opus 4.6 makes sense when you have a smaller request volume, quality-critical tasks, or a known advantage from Anthropic’s response style.
If you want to see the impact on your exact usage, plug your average tokens and request volume into the calculator. The cost delta becomes obvious immediately, and you can run “what if” comparisons in minutes.