GPT-6 Astra currently has the strongest official science-benchmark profile of the three, but there is no independent, physics-only, apples-to-apples leaderboard for the latest GPT-6 Astra, Claude and Gemini releases. OpenAI reports 96.0% on GPQA Diamond and 64.6% on Terminal-Bench Science 0.1 for GPT-6 Astra. Those are broad graduate-science and scientific-workflow measures, not a dedicated physics score.
For practical physics, the choice is task-dependent. Gemini has the clearest evidence for long mathematical and computational workflows, while Claude 4.0 Sonnet and Gemini both perform strongly on routine algebraic AP Physics questions. All three can fail on diagrams, vectors, circuits, graphs, spatial reasoning and numerical computation. Use any of them as a draft solver, then independently check the physics.
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What the available evidence actually proves
Comparing these systems requires separating three kinds of evidence: vendor benchmark results, independent subject tests and product capability claims. They answer different questions and should not be merged into one ranking.
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- Vendor benchmark evidence: OpenAI reports GPT-6 Astra at 96.0% on GPQA Diamond and 64.6% on Terminal-Bench Science 0.1. GPQA Diamond covers graduate-level science rather than physics alone. Terminal-Bench Science 0.1 evaluates scientific research workflows using code and terminal tools, including data analysis, simulations and model fitting.
- Independent AP Physics evidence: A 2026 study by Paul, Kaur, Chakraborty and Shrestha found mean scores from 82% to 92% across the evaluated systems on algebra-based free-response questions. The same work found Gemini more consistent than Claude on AP Physics 2, while AP Physics 1 did not show a consistent hierarchy.
- Independent quantum-mechanics evidence: A 2026 study by S. K. Rithvik reports 81% average accuracy for flagship models over 20 tasks. Symbolic derivations reached 92% accuracy, but numerical-computation accuracy was only 42%; repeated runs varied by an average of 6.3 percentage points.
- Capability descriptions: Google DeepMind describes a Google model named Argon as having 1 million output tokens and enough headroom for 100,000-token reasoning trajectories, complete mathematical proofs, quantum-error-correction decoders and computational-physics solvers. That is a capacity claim, not an independent Gemini-versus-GPT-6-Astra physics test.
The defensible conclusion is therefore narrower than “one model wins physics.” GPT-6 Astra leads the published official academic-science numbers in this material; Gemini is the best-supported option for very long, computation-heavy workflows; and Claude remains a capable algebraic reasoner. None has demonstrated reliable, unattended physics solving across diagrams, numerical work and advanced theory.
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Quick comparison: which model fits which physics task?
| Physics need | GPT-6 Astra | Gemini | Claude | What the evidence supports |
|---|---|---|---|---|
| Graduate-level science reasoning | 96.0% on GPQA Diamond, reported by OpenAI in 2026; the benchmark is not physics-only | Not stated in the supplied evidence | Not stated in the supplied evidence | GPT-6 Astra has the strongest published figure here, but it cannot be read as a physics pass rate |
| Scientific coding and terminal workflows | 64.6% on Terminal-Bench Science 0.1, reported by OpenAI in 2026 | Google emphasizes very long output and solver capacity; no matching score is stated | Not stated in the supplied evidence | GPT-6 Astra has the only reported score; Gemini has the clearest long-workflow description |
| Algebra-based AP Physics | Included in the independent study; the available data does not give a separate GPT-6 Astra score | Included; more consistent than Claude on AP Physics 2 in that test | Claude 4.0 Sonnet was included and performed strongly overall | All evaluated systems were useful for routine algebra; do not infer a universal ranking |
| AP Physics 1 | No consistent model ordering stated | No consistent hierarchy over Claude in the study | No consistent hierarchy over Gemini in the study | Expect comparable algebraic capability, with question-by-question variation |
| AP Physics 2 | No separate result stated | More consistent than Claude in the independent test | Less consistent than Gemini in that test | Gemini has the better-supported edge for this specific comparison |
| Quantum-mechanics derivation | Part of the flagship-model study, but no model-by-model score is supplied | Part of the study, with no separate score supplied | Part of the study, with no separate score supplied | Across flagship models, derivations were much stronger than numerical computation |
| Quantum numerical computation | Model-specific result not stated | Model-specific result not stated | Model-specific result not stated | The 42% study-wide accuracy figure is a warning to verify every number |
| Long proofs or multi-stage solvers | Terminal-Bench result indicates scientific tool-workflow ability | Google’s Argon description claims 1M output tokens and 100K-plus-token reasoning trajectories | No comparable capacity figure stated | Gemini has the clearest published long-output claim; GPT-6 Astra has the clearest workflow benchmark |
How GPT-6 Astra performs for physics
What its benchmark profile says
GPT-6 Astra’s 96.0% GPQA Diamond result is the strongest official science figure in the available material. GPQA Diamond is designed around difficult graduate-level questions in science, so it is relevant to the reasoning demands of advanced physics. It is still not a physics-only test: chemistry and biology items contribute to the aggregate, and a benchmark percentage is not the probability that Astra will solve a particular mechanics or quantum question correctly.
Its 64.6% Terminal-Bench Science 0.1 score measures a different capability. The benchmark involves scientific research tasks performed with code and terminal tools, such as data analysis, simulations and model fitting. That makes it relevant when a physics problem includes a numerical experiment or a parameter-estimation pipeline. It does not establish that the model will choose the right physical assumptions or interpret an unfamiliar diagram.
Where it is a sensible first choice
- Graduate-level conceptual questions where the main challenge is selecting and connecting equations.
- Problems that combine derivation with a short script, fit or simulation.
- Workflows that need explicit intermediate reasoning and a structured audit trail.
- API-based applications that need documented access through the OpenAI API, Microsoft Azure or Amazon Bedrock.
OpenAI’s published standard API price is $10 per million input tokens and $50 per million output tokens. Those are listed standard rates, not a promise of a particular bill for a physics session; prompt length, generated reasoning, retries and tool calls determine actual usage.
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What the score does not guarantee
Astra can still produce an elegant derivation from a misread diagram, use the wrong sign convention or report an unchecked numerical result. The independent physics evidence does not provide a dedicated GPT-6 Astra score for AP Physics or quantum mechanics, so its broad benchmark lead should not be converted into a claim that it is always better than Gemini or Claude in classwork.
How Gemini performs for physics
Long mathematical and computational workflows
Google DeepMind’s published description gives Gemini-family workflows the clearest long-output story in this comparison. The description of Argon says that 1 million output tokens can support reasoning trajectories longer than 100,000 tokens and produce exhaustive proofs, quantum-error-correction decoders and complete computational-physics solvers in one run. This matters when a project includes a long derivation, many parameter cases or code interleaved with explanation.
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That statement describes available output and reasoning headroom, not verified correctness. A longer context window can preserve more equations and data, but it can also preserve an early wrong assumption for hundreds of pages. Long output is useful only when checkpoints, unit tests and independent calculations are built into the workflow.
Evidence from AP Physics
In the 2026 independent AP Physics study, Gemini was more consistent than Claude on AP Physics 2. AP Physics 1 did not show a stable ordering. The study’s 82%–92% mean range across evaluated systems shows that routine algebraic free-response work is within reach for all of them, while the spread between questions still matters to a student or teacher.
Best use cases
- Long derivations that need the full problem statement, definitions and several linked subproblems in context.
- Computational physics notebooks where code, assumptions and outputs must remain together.
- AP Physics 2 practice, where the independent comparison found better consistency than Claude.
- Projects that benefit from asking for multiple solution methods before choosing one.
The supplied evidence does not state Gemini API prices, subscription prices or a matching GPQA/Terminal-Bench score. Those details vary by product and date, so they should be checked on the relevant current product page before budgeting.
How Claude performs for physics
Strong algebraic reasoning, with uneven AP Physics 2 consistency
Claude 4.0 Sonnet was included in the independent AP Physics evaluation and showed strong overall algebraic performance. Gemini was more consistent than Claude on AP Physics 2 in that test, while AP Physics 1 did not produce a consistent hierarchy. The result supports using Claude for ordinary equation-based practice, not dismissing it as weak at physics.
Broader comparison figures reported in OpenAI’s announcement include Claude models on academic and science-related benchmarks, but those are not dedicated physics results and should not be presented as a direct physics leaderboard.
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When Claude is useful
- Explaining an algebraic solution in several levels of detail.
- Checking whether a derivation follows from stated assumptions.
- Generating alternative symbolic approaches, such as energy versus force methods.
- Turning a solved problem into hints, a rubric or a set of conceptual checks.
As with the other systems, ask Claude to expose units, signs, boundary conditions and intermediate values. A polished explanation is not evidence that the underlying calculation is right.
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The independent AP Physics work is valuable because it tests complete free-response solutions rather than isolated multiple-choice facts. Its 82%–92% mean range indicates that current systems can handle much of the algebra in mechanics, electricity and related introductory topics. It also identifies recurring failure modes that broad benchmarks can hide:
- Reading a diagram incorrectly, especially when labels, angles or reference points are crowded.
- Assigning the wrong direction to a vector, force, field or velocity.
- Misidentifying circuit topology or treating series and parallel elements incorrectly.
- Constructing or interpreting a graph with the wrong slope, intercept or qualitative trend.
- Giving a plausible qualitative explanation that does not follow from the governing law.
- Applying a three-dimensional right-hand rule incorrectly.
- Making arithmetic or numerical-computation errors after a correct symbolic setup.
These are not rare cosmetic mistakes. A single reversed vector or wrong circuit connection changes the physical problem. If an answer depends on a diagram, restate the geometry in words and equations before accepting any result.
What quantum-mechanics evidence adds
The 2026 quantum-mechanics study shows why symbolic fluency should not be mistaken for complete reliability. Across 20 tasks, flagship models averaged 81% accuracy. Derivation accuracy was 92%, whereas numerical-computation accuracy was 42%. Repeated runs differed by an average of 6.3 percentage points.
The gap has practical consequences. A model may correctly derive an expectation value, perturbative expression or time-evolution formula, then substitute numbers incorrectly, mishandle a complex phase or report a result that violates normalization. Tool augmentation helped on some tasks and had task-dependent effects overall; adding a calculator or code interpreter is not an automatic cure.
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A quantum workflow that respects those limits
- State the representation: specify the basis, normalization, units, sign convention and whether operators are dimensionless or carry physical units.
- Derive symbolically first: ask for the Hamiltonian, commutators, eigenvalue equation or relevant approximation before inserting numbers.
- Check physical constraints: test Hermiticity, normalization, dimensions, limiting cases and conservation laws.
- Compute independently: run the numerical step in a trusted calculator or script, retaining enough precision to reproduce it.
- Compare methods: use a second derivation, an analytic limit or a known special case to challenge the first answer.
Can these models solve a physics problem from a diagram?
They can often extract enough information from a clean, high-resolution diagram to start a solution, but the evidence specifically warns against treating visual interpretation as reliable. Diagrams and graphs are among the recurring error areas in AP Physics evaluations.
A safer diagram procedure
- Provide the original image at readable resolution and identify every label the model must use.
- Ask the model to produce a text-only inventory: objects, coordinates, angles, lengths, directions, connection points and known values.
- Check that inventory against the image yourself. Correct it before requesting any equations.
- Ask for a free-body diagram, circuit description or coordinate sketch in text or code.
- Only then request the derivation and numerical answer.
For a graph, require the model to state the axes, units, scale, slope and intercept before interpreting the trend. For a three-dimensional problem, specify the coordinate system and each basis direction explicitly; do not rely on an unverified visual right-hand-rule judgment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A model-independent verification workflow
The most reliable way to use any of the three systems is to divide the task into independent stages. This also makes an answer auditable by a teacher, researcher or teammate.
- Classify the problem. Identify the domain (mechanics, electromagnetism, thermodynamics, optics, relativity or quantum mechanics), the requested quantity and whether the task is symbolic, numerical, visual or computational.
- List givens and assumptions. Record values with units, coordinate choices, approximations, initial and boundary conditions, and what is explicitly not known.
- Translate the setup. Convert a diagram or prose description into equations, a table of values or a text circuit topology. Do not let the model silently invent geometry.
- Choose governing laws. Ask for more than one candidate law when ambiguity exists, then require a sentence explaining why the selected law applies.
- Derive before calculating. Keep symbols until the structure, signs and dimensions are visible.
- Check dimensions and limits. Verify that both sides of every major equation have the same units and test an obvious limiting case.
- Compute separately. Use a calculator, spreadsheet or script for arithmetic. Preserve inputs, precision and code so the result can be rerun.
- Challenge the answer. Ask the model to find its own weakest assumption, then have a second model or human review only that point.
- Report uncertainty. Distinguish an exact result, an approximation, a measured value and a model-dependent estimate.
Prompt template for symbolic work
Act as a physics tutor. First list the givens, units, coordinate system and assumptions. Then derive the result symbolically without substituting numbers. Show dimensional checks, sign conventions and a limiting-case check. If the diagram is ambiguous, stop and list the interpretations instead of guessing.Prompt template for numerical work
Use the symbolic result below. Recalculate every numerical step with explicit intermediate values and units. State the precision used, provide reproducible code or calculator-ready expressions, and test the result against an independent method or limiting case. Flag any value that cannot be verified.Choosing by situation rather than by headline score
For an AP Physics student
Use whichever model is available and readable, but make the model show the free-body diagram or circuit assumptions in text. Gemini has the better-supported consistency result for AP Physics 2; the evidence does not establish a durable advantage for AP Physics 1. Grade the reasoning and units, not just the final number.
For a university quantum-mechanics course
Favor a workflow that keeps symbolic derivation separate from numerical substitution. The 92% versus 42% derivation/computation split means a correct-looking analytic answer still requires a separate numerical check. Ask for basis and normalization conventions explicitly.
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For a computational-physics project
GPT-6 Astra has the relevant reported Terminal-Bench Science score, while Gemini has the clearest long-output capability claim. In either case, require executable code, test cases, dimensional checks and saved inputs. A model’s ability to generate a long solver does not validate its algorithm.
For a diagram-heavy assignment
Choose based on image handling available in your interface, then use the text-inventory procedure above. Do not select a model solely from GPQA, AP Physics averages or context length; none of those measurements isolates diagram interpretation.
For an API product
GPT-6 Astra is documented as available through the OpenAI API, Microsoft Azure and Amazon Bedrock, with standard pricing of $10 per million input tokens and $50 per million output tokens. Comparable Gemini and Claude prices are not stated in the available evidence, so compare the current regional, model and tool-use rates before committing to an architecture.
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| Observed problem | Why it happens | Fix before accepting the answer |
|---|---|---|
| The final number looks reasonable but has the wrong unit | Units were dropped during substitution | Carry units through every line and perform a final dimensional check |
| A force, field or velocity points the wrong way | The coordinate system or diagram was misread | Make the model state axes and directions in words, then inspect the image yourself |
| A circuit result is inconsistent | Series/parallel topology or current direction was inferred incorrectly | Rewrite the circuit as nodes and connections before applying Kirchhoff’s laws |
| A graph explanation contradicts the curve | Axes, scale or slope were not identified | Require axis units, slope sign, intercept and one or two read-off points |
| Symbolic derivation is right but the numerical answer is wrong | Arithmetic, precision or code errors | Recompute independently and compare with a limiting case |
| Repeated runs disagree | Sampling and ambiguous assumptions change the path | Fix the prompt, assumptions and random settings where possible; investigate the disagreement rather than averaging it away |
| A long response contains a hidden early error | More context preserved the wrong premise | Insert checkpoints after setup, derivation and computation; verify each before continuing |
Bottom-line decision
Choose GPT-6 Astra when the strongest published general science and scientific-workflow evidence matters, while remembering that its 96.0% GPQA Diamond and 64.6% Terminal-Bench Science 0.1 figures are vendor-reported and not physics-only. Choose Gemini when a project needs very long mathematical or computational context, or when the AP Physics 2 consistency result is directly relevant. Choose Claude when you value strong algebraic explanation and iterative tutoring; the independent evidence does not show it dominating Gemini on AP Physics 2.
For all three, the decisive practice is the same: transcribe diagrams, derive symbolically, check dimensions and signs, recompute numbers independently, and have a human inspect the final physical interpretation.
Quick Recap
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