Use GPT 5.5 for long tool work and research. Use Opus 4.7 for careful coding, review, and visual reasoning.
The wrong question is “which model is better.” The better question is: what kind of work are you asking the model to do? GPT 5.5 and Claude Opus 4.7 are not fighting for the exact same lane. GPT 5.5 feels built for execution. Opus 4.7 feels built for judgment.
The short answer
If I had to simplify the choice, I would say this:
| Use case | Better first choice | Why |
|---|---|---|
| Terminal tasks and DevOps automation | GPT 5.5 | OpenAI reports 82.7 percent on Terminal Bench 2.0, compared with 69.4 percent for Opus 4.7. |
| Web research and browsing agents | GPT 5.5 | OpenAI reports 84.4 percent on BrowseComp, while Opus 4.7 is listed at 79.3 percent. |
| Math heavy reasoning | GPT 5.5 | GPT 5.5 leads Opus 4.7 on FrontierMath Tier 1 to 3 and Tier 4 in OpenAI’s published results. |
| Repository level software engineering | Opus 4.7 | Opus 4.7 leads GPT 5.5 on SWE Bench Pro, 64.3 percent versus 58.6 percent. |
| Code review and careful refactoring | Opus 4.7 | Anthropic positions it around long agentic coding, review, planning, and lower tool error rates. |
| High resolution charts and technical image reading | Opus 4.7 | Anthropic increased supported image fidelity, and DataCamp notes a strong CharXiv visual reasoning result for Opus 4.7. |
| High volume coding agent loops | Test both | MindStudio reports GPT 5.5 used 72 percent fewer output tokens on equivalent coding tasks, but Opus 4.7 may still win on harder architecture work. |
OpenAI reports GPT 5.5 scores of 82.7 percent on Terminal Bench 2.0, 84.4 percent on BrowseComp, 78.7 percent on OSWorld Verified, and 58.6 percent on SWE Bench Pro. The same OpenAI table lists Claude Opus 4.7 at 69.4 percent on Terminal Bench 2.0, 79.3 percent on BrowseComp, 78.0 percent on OSWorld Verified, and 64.3 percent on SWE Bench Pro.
In short, GPT 5.5 looks stronger when the model has to operate tools for a long time. Opus 4.7 looks stronger when the model has to reason carefully inside a codebase.
What do the benchmarks actually say?
Benchmarks are useful, but they are not commandments. They are more like weather reports. They tell you what conditions may look like. They do not guarantee your production result.
Here is the cleanest way to read the public numbers.
| Dimension | GPT 5.5 signal | Opus 4.7 signal | Practical read |
|---|---|---|---|
| Agentic coding | 58.6 percent on SWE Bench Pro | 64.3 percent on SWE Bench Pro | Opus 4.7 has the better signal for hard repo level fixes. |
| Terminal automation | 82.7 percent on Terminal Bench 2.0 | 69.4 percent on Terminal Bench 2.0 | GPT 5.5 has a clear edge for command line workflows. |
| Web research | 84.4 percent on BrowseComp | 79.3 percent on BrowseComp | GPT 5.5 is the safer first pick for browsing agents. |
| Tool orchestration | 75.3 percent on MCP Atlas | 79.1 percent on MCP Atlas | Opus 4.7 has a small but meaningful edge on multi tool planning. |
| Computer use | 78.7 percent on OSWorld Verified | 78.0 percent on OSWorld Verified | This is close enough that product integration may matter more. |
| Academic reasoning | 93.6 percent on GPQA Diamond | 94.2 percent on GPQA Diamond | The difference is tiny. Do not overread it. |
| Hard math | 51.7 percent on FrontierMath Tier 1 to 3 and 35.4 percent on Tier 4 | 43.8 percent on Tier 1 to 3 and 22.9 percent on Tier 4 | GPT 5.5 has the stronger public math signal. |
| Visual reasoning | GPT 5.5 has no directly comparable CharXiv score in DataCamp’s review | Opus 4.7 scored 82.1 percent on CharXiv without tools in DataCamp’s review | Opus 4.7 has better public evidence for technical vision tasks. |
DataCamp’s comparison reaches a similar split. It says Opus 4.7 leads on SWE Bench Pro and MCP Atlas, while GPT 5.5 leads on Terminal Bench 2.0, BrowseComp, and FrontierMath. It also notes that GPT 5.5 and Opus 4.7 are very close on GPQA Diamond and OSWorld Verified.
My read is simple. GPT 5.5 is a stronger operator. Opus 4.7 is a stronger reviewer. GPT 5.5 is the model I would trust more for “go do this across tools.” Opus 4.7 is the model I would trust more for “slow down and find the mistake.”
Coding is not one category
A lot of articles say “which model is better for coding,” but that question is too vague.
Coding has at least four different jobs.
| Coding job | Better first choice | Why |
|---|---|---|
| Fixing a deep bug across a large repo | Opus 4.7 | It has stronger public evidence on SWE Bench Pro and tends to be more careful with architectural context. |
| Running terminal commands and checking outputs | GPT 5.5 | Terminal Bench 2.0 strongly favors GPT 5.5. |
| Code review before merge | Opus 4.7 | Anthropic highlights review workflows, task budgets, and stronger long run control. |
| Fast coding agent loops at scale | GPT 5.5 | MindStudio reports much lower output token use on equivalent coding tasks. |
OpenAI says GPT 5.5 is more persistent than GPT 5.4 and better at tool use, and it describes internal usage where teams used GPT 5.5 in Codex for operational research, spreadsheet modeling, document generation, and multi step work. Anthropic says Opus 4.7 adds more effort control, task budgets, and better control over token spend in long runs. It also says Opus 4.7 uses a new tokenizer and that users should measure token impact on real traffic.
This is why I would not say “Opus is better for coding” or “GPT is better for coding.” That is lazy analysis.
Use Opus 4.7 when wrong code is expensive. Use GPT 5.5 when slow execution is expensive.
Price is not just the sticker price
At standard API pricing, GPT 5.5 and Opus 4.7 start close on input price. The difference appears on output price, Pro pricing, and task level efficiency.
| Model | Input price per 1M tokens | Output price per 1M tokens | Cost note |
|---|---|---|---|
| GPT 5.5 standard | $5 | $30 | OpenAI says Batch and Flex are available at half the standard API rate. |
| GPT 5.5 Pro standard | $30 | $180 | This is for higher accuracy use cases, not default traffic. |
| Claude Opus 4.7 | $5 | $25 | Anthropic says pricing remains the same as Opus 4.6. |
| Claude Opus 4.7 cache hit | $0.50 | Not applicable | Anthropic lists cache hits and refreshes at $0.50 per 1M tokens. |
OpenAI’s pricing docs list GPT 5.5 standard at $5 per 1M input tokens and $30 per 1M output tokens, while GPT 5.5 Pro standard is $30 input and $180 output. The same pricing page lists Flex pricing at $2.50 input and $15 output for GPT 5.5, where available. Anthropic lists Claude Opus 4.7 at $5 per 1M base input tokens and $25 per 1M output tokens, with cache hits and refreshes at $0.50 per 1M tokens.
But the sticker price can mislead you. A model with a higher output price can still be cheaper per finished task if it produces fewer tokens, uses fewer retries, or avoids failed loops.
MindStudio’s coding comparison claims GPT 5.5 used roughly 72 percent fewer output tokens than Opus 4.7 on equivalent coding tasks. I would not treat one benchmark as universal truth, but the point is important: cost per completed task matters more than cost per token.
Is GPT 5.5 Pro worth it?
For most teams, I would not start with GPT 5.5 Pro.
That sounds harsh, but this is how real API budgets work. A Pro model can be impressive and still be the wrong default. GPT 5.5 Pro costs six times more than standard GPT 5.5 under OpenAI’s standard pricing. It may make sense for high value math, legal review, scientific research, financial modeling, or tasks where a one point quality gain is worth real money. It does not make sense for every agent step.
My rule is simple: use Pro only when the cost of being wrong is higher than the cost of the model.
For everything else, route. Use a cheaper model for simple extraction. Use GPT 5.5 for tool heavy execution. Use Opus 4.7 for careful reasoning and review. Save Pro for the moments where the extra quality can actually change the outcome.
The real difference: one works longer, one thinks harder
Here is the more human way to say it.
GPT 5.5 feels like the person who can stay up all night, open ten tools, run the checklist, fill the spreadsheet, search the web, and keep going. It may not be perfect, but it has stamina.
Opus 4.7 feels like the senior engineer who reads the pull request slowly and says, “This part is probably where the bug hides.” It can be verbose. It can cost more in long outputs. But that patience is useful when the task is ambiguous.
The boring truth is that serious teams should not marry one model. They should build a routing layer.
How should teams route them in production?
Here is the production setup I would use.
| Traffic type | Route to | Reason |
|---|---|---|
| Simple rewrite, summary, classification | Cheaper small model | Frontier models are wasteful here. |
| Web research and browsing agent | GPT 5.5 | Stronger BrowseComp signal and better fit for search driven workflows. |
| Terminal and DevOps agent | GPT 5.5 | Strong Terminal Bench 2.0 result. |
| Repo level bug fix | Opus 4.7 | Stronger SWE Bench Pro signal. |
| Code review and security review | Opus 4.7 first, GPT 5.5 second pass if needed | Careful review matters more than speed. |
| Math heavy analysis | GPT 5.5 or GPT 5.5 Pro | Stronger FrontierMath signal. |
| Technical chart or high resolution image analysis | Opus 4.7 | Better documented visual reasoning evidence. |
| Business workflow with documents and spreadsheets | GPT 5.5 | OpenAI reports strong results on GDPval, OfficeQA Pro, and internal finance tasks. |
| Final high stakes review | GPT 5.5 Pro or Opus 4.7 at higher effort | Use premium compute only where it changes risk. |
This is the part a lot of model comparison articles miss. The best model is not a model. The best model is a routing policy.
One model choice is a preference. A routing policy is an operating system for cost, quality, and reliability.
Where PP API fits
This is exactly where a unified API layer becomes useful.
PP API is built as a unified large language model API platform. It lets teams access models from OpenAI, Anthropic, Google, DeepSeek, Alibaba, and other providers through one interface. It uses a compatible format, supports smart routing and multi provider failover, provides pay as you go billing with no subscription fee, and shows model prices for comparison.
The practical value is not just convenience. It is control.
You do not want your engineering team to rewrite integration code every time GPT 5.5 wins one task and Opus 4.7 wins another. PP API’s quick start guide says developers can keep an OpenAI compatible Chat Completions format, point the base URL to PP API, and switch models by changing the model parameter.
For teams choosing between GPT 5.5 and Opus 4.7, this matters. You can test both, route tasks by type, compare cost, and avoid locking your workflow into one vendor. PP API’s Dashboard shows model usage distribution, usage trends, request distribution, and usage by API Key. It supports hourly, daily, and weekly aggregation, and the dashboard usually updates within one minute.
In short, PP API turns the question from “which model should we bet on” into “which model should this task use right now.”
FAQs
Which is better for coding, GPT 5.5 or Opus 4.7?
Opus 4.7 is the stronger first choice for deep repo level fixes and careful code review. GPT 5.5 is stronger for terminal work, tool loops, and high volume coding agents. OpenAI reports GPT 5.5 at 82.7 percent on Terminal Bench 2.0, while Opus 4.7 leads on SWE Bench Pro at 64.3 percent versus GPT 5.5 at 58.6 percent.
Which model is cheaper?
At standard list pricing, Opus 4.7 is cheaper on output tokens, $25 per 1M output tokens versus GPT 5.5 at $30. GPT 5.5 can still be cheaper per task if it uses fewer tokens or fewer retries. That is why teams should measure cost per completed task, not only token price.
Should I use GPT 5.5 Pro?
Use GPT 5.5 Pro only for high value tasks where the extra quality can justify the much higher price. I would not use it as the default model for every agent step.
Which model is better for agents?
GPT 5.5 is stronger for long execution, browsing, terminal work, and computer use. Opus 4.7 is stronger for careful multi step coding, tool orchestration, task budgets, and review style work. Serious teams should route between both.
Can a team use both models together?
Yes. That is the most practical setup. Use GPT 5.5 for execution heavy tasks, use Opus 4.7 for careful reasoning and review, and use cheaper models for simple work. A unified API layer like PP API makes that routing easier to operate.