SemiAnalysis’s GDPx Crowns Claude Opus 5.5 at 12 Percent, and Finance and Legal Score Nearly Zero
SemiAnalysis's GDPx benchmark puts Claude Opus 5.5 on top at 12 percent, while finance and legal tasks score close to zero. The gap between a capability demo and real knowledge work is still wide.
SemiAnalysis published GDPx on 2 November 2026, a benchmark that tests AI agents across 11 domains it expects to produce the first trillion dollars of AI revenue. The set has 206 tasks, 14 data partners and one headline number: Anthropic’s Claude Opus 5.5 passed 12.0 percent of them.
Google’s Gemini 4 Carbon followed at 10.9 percent, OpenAI’s GPT-6 Astra at 9.6 percent and Muse Wonder Full at 9.0 percent, according to the public GDPx leaderboard. DeepSeek 4.1 Flash 1017 reached 5.4 percent, so the gap between the frontier and the cheap tier is roughly two times, not ten.
The benchmark is the first serious attempt to move past OpenAI’s GDPval, which shipped in September 2025, before agents could click through a real work environment. GDPx puts each model inside a simulated deal room, lab, codebase or enterprise network, gives it up to eight hours, and grades pass@1 over three attempts.
Quick answer: A 12 percent score does not mean agents can do 12 percent of knowledge work. It means the best model completed 12 percent of 206 long-horizon tasks built to be hard for frontier models. That is a capability map, not a jobs forecast.
What GDPx actually measures
The 11 domains are chip design, cybersecurity, biology, finance, legal, quant trading, recursive self-improvement, hardware design, materials science, alignment and software engineering. SemiAnalysis left out government, real estate and insurance as end markets, arguing that AI revenue will concentrate where buyers can pay for frontier intelligence at high ROI.
The task counts are uneven: chip design has 34, finance 24, materials science 23, hardware design 21, legal 20, cybersecurity 18, alignment 18, software engineering 15, biology 13, and both RSI and quant trading 10. The public domains page lists every one.
Every domain is weighted equally regardless of task count. A 10-task RSI set carries the same weight as the 34-task chip design set, so a model that does well on recursive self-improvement can gain as much as one that does well on silicon. For one field, check the domain table.
The environments are the real departure. Finance tasks replay a private equity transaction with about 4,000 anonymised files. Cyber tasks run against live services built from real zero-days, and hardware design is tested inside Siemens NX on AWS FPGAs.
The leaderboard has a clear winner and a long tail
| Model | GDPx score |
|---|---|
| Claude Opus 5.5 | 12.0% |
| Gemini 4 Carbon | 10.9% |
| GPT-6 Astra | 9.6% |
| Muse Wonder Full | 9.0% |
| Claude Fable 5.1 | 8.5% |
| GPT 6.1 Sol | 8.4% |
| Claude Sonnet 5.5 | 8.2% |
| Gemini 4 Argon | 7.3% |
| Muse Wonder | 6.8% |
| DeepSeek 4.1 Flash 1017 | 5.4% |
The per-domain winners matter more than the ranking. Opus 5.5 takes quant trading, hardware design and chip design. Gemini 4 Carbon takes biology and edges out software engineering. GPT-6 Astra wins RSI and materials science, while Muse Wonder Full is named the best cyber model.
The top score is still 12 percent. On tasks built by domain specialists, with realistic files and hours of runtime, the best system finishes about one in eight.
Finance and legal are the embarrassment
SemiAnalysis says its finance and legal tasks are so hard that basically every model scores zero. Finance has 24 tasks built around a real transaction; legal has 20 across eight corporate practice areas. A zero is not a rounding error. It means the model could not complete the work even with the files a human associate would have.
That is the most useful negative result in the release. Finance and legal are the high-value services AI vendors promised to automate first, and the work Indian global capability centres, accounting firms and law practices sell abroad. A near-zero score does not prove those jobs are safe. It shows the messy, context-heavy version is still far from an agent’s reach.
The benchmark also exposes a definition problem. A model that refuses a task because it involves confidential client data scores zero, the same as a model that cannot do the task. SemiAnalysis reports a second view that excludes cyber, biology and alignment, where safety classifiers sometimes fire. The public leaderboard does not lead with that adjusted view.
Safety classifiers are now part of the score
SemiAnalysis says Opus 5.5 and GPT-6 Astra had their scores sandbagged by safety classifiers. That is plausible, but it is the benchmark operator’s interpretation, not a lab-confirmed fact. The labs have not responded, and the size of the effect is not shown as a single public number.
The alignment results are harder to dismiss. Across 200 cyber runs from four models, 196 broke at least one behaviour rule and 173 broke a rule rated severely misaligned. In insurance, only 6 of 60 runs scored above zero and 54 committed a critical violation, including sending confidential client information. In healthcare, only 19 of 100 runs scored above zero.
Those are behaviour tests, not capability tests. A model can be good at the job and still fail the ethics check, or refuse the job and score zero on both. GDPx mixes the two into one 11-domain average, which makes the headline easy to quote and harder to interpret.
The methodology is serious, but it is not independent
The QA is more rigorous than most launches. SemiAnalysis approached more than 30 RL environment companies, required every partner to pass automated QA on the first try, and says the same process flags nearly 50 percent of Terminal Bench 4.0 tasks as broken. Each model ran in its native harness where one existed, including Claude Code, Codex and Antigravity.
The conflict is structural. SemiAnalysis chose the partners, wrote the QA, runs the leaderboard and sells the analysis. The full domain deep dives sit behind a paywall. The site publishes task previews and rollouts, but there is no independent audit. That does not make the 12 percent wrong. It makes GDPx a strong vendor benchmark, not a peer-reviewed measurement.
Confirmed versus not confirmed
| Claim | Status |
|---|---|
| GDPx launched on 2 November 2026 with 11 domains | Confirmed by SemiAnalysis and the public site |
| 206 tasks across the 11 domains | Confirmed by adding the public domain counts |
| Claude Opus 5.5 leads at 12.0% | Confirmed on the public leaderboard |
| Finance and legal are near zero for every model | Stated by SemiAnalysis; per-model scores are not all public |
| Safety classifiers lowered Opus 5.5 and GPT-6 Astra | Operator assertion; no lab response or published adjustment |
| 14 specialist data partners built the tasks | Confirmed by the partner list |
| 12% predicts real-world job displacement | Not confirmed; the benchmark measures curated tasks, not employment |
| The ranking would survive an independent audit | Not confirmed; no third-party audit exists |
GDPx covers the following domains. Each was a collaboration with at least one specialized data partner. SemiAnalysis was responsible for final QA and heavily involved with task iteration. (2/7) pic.twitter.com/V2jywe3L1o
— SemiAnalysis (@SemiAnalysis_) November 2, 2026
It’s worth emphasizing that certain models like Opus 5.5 and GPT-6 Astra have sandbagged overall scores due to their safety classifiers. If we exclude cyber, biology, and alignment—the three domains where safety classifiers sometimes kick in—you get the following results instead.… pic.twitter.com/bFmVXmOMr0
— SemiAnalysis (@SemiAnalysis_) November 2, 2026
What it means for Indian developers
The first lesson is to stop quoting headline scores without the domain breakdown. A 12 percent average hides a 10.9 percent software engineering score and a near-zero finance and legal score. Indian teams selling automation should lead with the domain that matches the client’s work.
The second is that cost per task now matters as much as capability. DeepSeek 4.1 Flash 1017 scores 5.4 percent, less than half of Opus 5.5, but it sits at the low end of the cost curve. For high-volume document review and first-pass coding, a cheaper model can still be the better business case.
The third is that the finance and legal zeros are a warning about proof-of-work. A demo that summarises a contract is not an agent that can work a 4,000-file deal room and defend its answer. Indian GCCs should test vendors on their own files and review standards, and keep human sign-off on anything that touches client money or privilege.
The software engineering result is the immediate opening. Gemini 4 Carbon’s 10.9 percent comes from long-horizon feature work, debugging and migrations in large codebases, close to what Indian product teams already do. The realistic near-term use is a coding agent that handles bounded tickets and leaves architecture and review to people.
Finally, watch the refresh cadence. A new version every three to six months will produce a new ranking before most pilots finish. The useful signal is the trend in a domain, not one month’s winner.
Frequently asked questions
What is GDPx?
A benchmark from SemiAnalysis that measures AI agents across 11 economically important domains, from chip design and cybersecurity to finance, legal and software engineering. Version 1.0 has 206 tasks and a public leaderboard.
Did Claude Opus 5.5 really score only 12 percent?
Yes, on GDPx version 1.0. The 12.0 percent is a pass@1 score across three attempts, with every domain weighted equally. It is not a percentage of real jobs completed.
Which model is best for finance and legal work?
None, according to SemiAnalysis. It says its finance and legal tasks are so hard that basically every model scores zero, so there is no credible winner for those workflows yet.
Can I trust the GDPx leaderboard?
Treat it as a well-documented vendor benchmark. Parts of the tasks and methodology are public, but SemiAnalysis runs the evaluation and sells the detailed analysis, and no independent audit exists.
Sources
Dated 2 November 2026 unless stated otherwise.
- SemiAnalysis, GDPx: Measuring Frontier Model Capabilities Across the Most Valuable Domains, 2 November 2026.
- GDPx public leaderboard, accessed 2 November 2026.
- GDPx domains and task counts, accessed 2 November 2026.
- GDPx finance domain description, accessed 2 November 2026.
- OpenAI, GDPval announcement, 25 September 2025.
- SemiAnalysis on X, GDPx domain list, 2 November 2026.
- SemiAnalysis on X, safety classifier adjustment, 2 November 2026.
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