Yes—according to a TheoPhysEval paper submitted to arXiv on October 9, 2026, GPT-6-Astra produced correct final answers for 49 of 50 advanced theoretical-physics problems, a 98% score. The result was achieved with GPT-6-Astra running in OpenAI’s Codex and using tools such as literature search plus numerical and symbolic scripting.
The result is impressive evidence of reliable performance on difficult, checkable calculations. It is not evidence that an AI system can independently choose important research questions, invent a new physical theory or make major discoveries without human direction.
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What TheoPhysEval tested
TheoPhysEval is a benchmark of 50 unpublished, research-level theoretical-physics problems with verifiable answers. The authors describe a typical problem as work that a world expert might complete in about a day or less without AI, while a physicist from a neighboring specialty could need considerably longer.
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The tasks span 11 theoretical-physics subfields, with examples involving fluid flows, biological processes, particle physics, cosmology and black-hole research. They are designed around concrete calculations: applying established methods to modified or unfamiliar versions of research questions and checking the resulting answer.
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Why the problems were not simple textbook exercises
Contributors often changed values or conditions from calculations in their own papers. That makes copying a published numerical answer more difficult, while preserving a problem that can be graded against a definite result. However, the benchmark deliberately excludes major open questions and does not ask an agent to decide which problems deserve attention.
The headline score and what it cost
In the realistic-tool condition, GPT-6-Astra was the best-performing agent and returned the final answers for 49 of the 50 tasks. The primary paper gives an average cost below USD 3 per problem. A contemporaneous report by The Decoder gives a more precise, approximate estimate of $2.40 per problem and just under $116 for the complete benchmark.
| Measure | Reported result | How to interpret it |
|---|---|---|
| Final answers | 49 of 50 | One task remained unsolved by Astra in the reported run. |
| Accuracy | 98% | The percentage calculated by the TheoPhysEval authors for the final-answer score. |
| Average cost | Under USD 3 per problem | Primary-paper estimate for the realistic-tool evaluation. |
| More precise cost estimate | Approximately $2.40 per problem; just under $116 total | The Decoder’s 2026 secondary report; the per-problem number is approximate rather than a published price list. |
The $2.40 figure therefore should not be read as a guaranteed consumer tariff. It is a rounded estimate for this benchmark run, and the primary source states the broader “under $3” figure. Costs can vary with prompt length, tool calls, scripts, retries and the model or service configuration used.
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What tool access changed
Codex gave the agent access to tools that a researcher might reasonably use: searching the literature and writing scripts for numerical or symbolic work. When the authors denied web search and background papers, average accuracy changed only slightly.
That result is suggestive of substantial literature memorization, because the system often performed similarly without the extra documents. It does not, by itself, separate memorized information from reasoning ability or prove that tool use is unimportant. Removing a tool changes the whole task environment, and the reported comparison is not a clean experiment that isolates one cause.
How dependable is the 98% measurement?
Evaluation quality matters as much as the headline percentage. One problem stumped every model in the study. The report also says that expert follow-up uncovered subtle errors in several reference answers. After those references were corrected, Astra’s responses were judged correct on the affected cases.
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This does not invalidate the benchmark, but it means the score should be read together with its grading history. A result on only 50 problems is sensitive to individual items, and a correction to a reference answer can change whether a model receives credit. The appropriate claim is that Astra achieved 49 correct final answers under the benchmark’s revised grading process, not that the system is infallible.
Does this prove that AI can do physics research?
It establishes a narrower and more useful point: a tool-using AI agent can solve many advanced, concrete calculations that resemble parts of routine theoretical-physics research, at a reported average cost of a few dollars per problem.
It does not establish that Astra can:
- select the most important unanswered question in a field;
- formulate a productive research program from an ambiguous observation;
- invent a theory that is both genuinely new and physically fruitful;
- decide which approximations are scientifically justified when no reference answer exists; or
- take responsibility for experimental design, interpretation and long-term discovery.
Those activities require judgment under uncertainty, not just a verifiable calculation. TheoPhysEval’s design intentionally measures the latter.
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How Astra compares with other agents
The Decoder’s contemporaneous report gives the following scores on the same 50-task benchmark. These figures are secondary reporting and should not be treated as definitive until each comparison is checked against the paper’s full experimental tables.
| Agent | Reported accuracy | Source and qualification |
|---|---|---|
| GPT-6-Astra | 98% (49 of 50) | TheoPhysEval authors, 2026; realistic-tool condition. |
| Opus 5.5 | 92% | The Decoder, 2026 secondary report. |
| Gemini 3.8 Flash | 68% | The Decoder, 2026 secondary report. |
| DeepSeek-V4.1-Flash | 54% | The Decoder, 2026 secondary report. |
| GLM 5.3 | 42% | The Decoder, 2026 secondary report. |
A fair comparison must hold more than the problem set constant. Readers should check whether each agent had realistic tools or an isolated prompt, what counted as a final answer, how much each run cost, which physics subfields were represented and whether the reference solutions received the same independent scrutiny.
What the result means for working physicists
The practical implication is not that researchers can hand over discovery to a chatbot. It is that agents may become useful assistants for bounded calculations: translating a paper’s method to a changed parameter set, checking algebra, exploring numerical cases and producing candidate derivations for an expert to audit.
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Human review remains essential when a result will guide a publication or experiment. The reference-answer corrections in TheoPhysEval show why: even a benchmark intended to have verifiable answers can contain subtle mistakes, and an agent’s apparent agreement may reflect a flawed target rather than sound physics.
The benchmark is therefore best viewed as a demanding capability test with an unusually high score, not as a general measure of scientific autonomy. Its strongest evidence concerns speed and accuracy on concrete, checkable research-style calculations; its evidence about independent discovery is effectively absent.
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