Claude Opus 4.7 vs. GPT-5.5: Benchmarks, Weak Spots and Practical Fit

The practical takeaway: treat these as different buying signals, not one head-to-head race. A 1,753 Elo score on GDPval-AA cannot be subtracted from a 59-point Intelligence Index score. They are different benchmarks measuring different things.

The short version

If your workload looks like research, long-document analysis, synthesis across sources or multi-step task execution, Claude Opus 4.7 deserves an early test. It is the new GDPval-AA leader at 1,753 Elo, around 79 Elo points ahead of the closest listed models in that source.

If your team is already built around ChatGPT, Codex or OpenAI’s product stack, GPT-5.5 may be easier to adopt. Appwrite’s summary says gpt-5.5 is the base model for ChatGPT Plus, Pro, Business and Enterprise tiers, as well as Codex.

Key comparison

Where Claude Opus 4.7 looks strongest

Agentic knowledge work is its clearest public win

Opus 4.7’s standout number is GDPval-AA: 1,753 Elo. Artificial Analysis says GDPval-AA is its primary metric for general agentic performance on knowledge-work tasks, and says Opus 4.7 is the new leader on that measure.

That matters if your AI workload is not a single prompt but a chain of work: reading documents, extracting facts, reconciling sources, planning next steps and producing a deliverable. For that kind of task, Opus 4.7 should be high on the test list.

The caveat is important: the same GDPval-AA source lists Claude Sonnet 4.6 and GPT-5.4 at 1,674 Elo as the nearest models, but it does not provide a same-table GPT-5.5 result against Opus 4.7. So the benchmark supports Opus 4.7 for this category, not a universal claim that it beats GPT-5.5 everywhere.

It improved efficiency versus its predecessor

Artificial Analysis also says Opus 4.7 used about 35% fewer output tokens than Opus 4.6 to run the Intelligence Index while scoring 4 points higher. The listed output-token counts are 102M for Opus 4.7 versus 157M for Opus 4.6.

For long-running agents, that can matter as much as raw accuracy. Fewer generated tokens may mean lower latency, less review overhead and lower total cost. But this is an Opus 4.7-versus-Opus 4.6 comparison; it does not prove Opus 4.7 is cheaper than GPT-5.5 in your workflow.

Where Claude Opus 4.7 is less certain

The main uncertainty is the lack of a full same-condition public comparison with GPT-5.5. Opus 4.7’s strongest benchmark evidence is on GDPval-AA, while GPT-5.5’s most visible numbers in this source set are on the Artificial Analysis Intelligence Index.

There is also less product-context detail in the provided sources for Opus 4.7. For GPT-5.5, the source set gives explicit ChatGPT and Codex integration details; for Opus 4.7, it does not provide an equally clear comparison of pricing, latency, enterprise controls or deployment options.

That does not make Opus 4.7 weaker. It means procurement and engineering teams should avoid deciding from one benchmark alone.

Where GPT-5.5 looks strongest

It has clearer versioned benchmark data

GPT-5.5 appears in three visible Artificial Analysis variants: high, low and non-reasoning. GPT-5.5 high scores 59 on the Intelligence Index, GPT-5.5 low scores 51, and GPT-5.5 non-reasoning scores 41.

That split can be useful in production. A team might test the high version for difficult reasoning, the low version for everyday work and the non-reasoning version for simpler flows. The benchmark numbers do not tell you exactly how to route requests, but they do make clear that the versions are not interchangeable.

ChatGPT and Codex integration are real advantages

For many teams, the “best” model is the one employees can actually use with the least friction. Appwrite’s summary says gpt-5.5 is the base model for ChatGPT Plus, Pro, Business and Enterprise tiers, and for Codex.

That is a practical advantage if your developers, analysts or support teams already work inside those tools. It may reduce training time, workflow changes and integration work.

Its coding positioning is strong, but not final proof

TechflowPost reports that OpenAI says GPT-5.5 is currently its most capable autonomous programming model. That gives GPT-5.5 a strong product narrative for coding and software automation.

Still, the provided sources do not show a complete same-condition coding benchmark between GPT-5.5 and Opus 4.7. For engineering teams, the right test is not a generic leaderboard. Use your own repositories, failing tests, code-review standards, refactoring tasks and issue backlog.

Where GPT-5.5 carries risk

The clearest risk is verbosity in the high version. Artificial Analysis says GPT-5.5 high generated 45M tokens during its Intelligence Index evaluation, compared with a comparable-model average of 23M, and describes it as somewhat verbose by comparison.

The second risk is version spread. GPT-5.5 high, low and non-reasoning score 59, 51 and 41 respectively on the Intelligence Index. If an app routes requests across versions, users may experience different capability, latency and cost profiles.

The third risk is price interpretation. Appwrite says GPT-5.5 Pro is roughly seven times the output cost of Claude Opus 4.7, while Artificial Analysis lists GPT-5.5 low at $5.00 per 1M input tokens, above that page’s median of $1.60. Those figures are enough to flag cost risk, but not enough to replace a workload-specific cost model.

How to choose in practice

Test Claude Opus 4.7 first if your work is agentic and document-heavy

Start with Opus 4.7 if the core job is multi-step research, long-document analysis, source synthesis, planning, review or deliverable generation. Its GDPval-AA result is the strongest public evidence in this source set for that kind of agentic knowledge work.

Test GPT-5.5 first if you need OpenAI workflow fit

Start with GPT-5.5 if your team already relies on ChatGPT, Codex or OpenAI-based workflows. The integration story is clearer in the provided sources, and the high/low/non-reasoning split gives teams a more obvious model-routing test plan.

Treat coding as a bake-off, not a slogan

GPT-5.5 has a strong autonomous-programming claim from OpenAI as reported by TechflowPost. But without a direct public coding comparison against Opus 4.7 in this source set, engineering teams should run a side-by-side bake-off using real tickets, real code and real acceptance tests.

Measure total cost, not just price per token

Do not choose on headline price or benchmark rank alone. GPT-5.5 high’s longer output profile, Opus 4.7’s efficiency improvement versus Opus 4.6, and GPT-5.5 low’s listed input-token price all show why total cost depends on prompt length, output length, retries, tool calls and task success rate.

A simple pre-launch checklist

1. Run both models on the same prompts, documents, tools and success criteria.
2. For GPT-5.5, test high, low and non-reasoning separately because their public Intelligence Index scores differ meaningfully.
3. Record input tokens, output tokens, retries, tool calls, latency and human correction time.
4. Score tasks by category: research agents, coding, long-document analysis, customer support, extraction and summarization.
5. Decide on total workflow cost and reliability, not a single benchmark number.

Bottom line

Claude Opus 4.7 is the more compelling first test for agentic knowledge work, based on its GDPval-AA lead. GPT-5.5 is the more straightforward first test for teams that want ChatGPT/Codex integration, versioned routing and a clearer OpenAI product path.

The evidence does not support a blanket claim that either model wins on coding, cost, latency or enterprise deployment in every case. The real question is narrower: does your workload look more like a knowledge-work agent, or more like a productized workflow that benefits from OpenAI integration and model-tier routing?