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Python guarantees that equal integers have equal values—not that they are the same object. Use == to compare integers. The result of is can depend on the interpreter, how values are created, and the run; it is not a reliable numeric comparison.
is and == answer different questions
a == b asks whether a and b have equal values. a is b asks whether both names refer to the very same object. Two integer objects can therefore compare equal with == while failing an is test.
For example, if a and b each represent the integer value 1000, a == b tests their numeric equality. a is b tests object identity; its result is not a promise about their values. Do not use is to compare integers.
What Python guarantees about integer identity
The Python Language Reference, in “Expressions: Literals and object identity”, says that multiple evaluations of literals with the same value “may obtain the same object or a different object with the same value.” That rule applies even when the literal text is repeated in a program. Equal integer literals are not guaranteed to be identical objects.
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The Python Programming FAQ warns that identity tests should not be used for constants such as int and str, which are not guaranteed to be singletons. The Python 3.14.7 data model likewise describes identity as an object property and notes that identity observations for immutable values can depend on implementation.
Why some integer is checks appear to work
An interpreter may reuse an existing object when it encounters or creates an integer value. It may also handle literals or expressions in ways that cause values to share an object. Such reuse can make a is b evaluate to True in a particular example, but that observation is not a language guarantee.
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Whether identity appears to match can depend on how the values were produced, the interpreter’s optimizations, its version, and its configuration. A result from one short demonstration does not establish a universal rule for that value or for another run.
How CPython and PyPy differ
| Question | CPython | PyPy |
|---|---|---|
| Does Python guarantee equal integers are identical? | No. The Python reference describes small-integer reuse as an implementation detail. | No cross-implementation guarantee is established by PyPy’s documentation. |
| Can integer objects be reused? | CPython can reuse same-value small integers. Its documentation says the boundary has changed before and may change again; it gives no stable numeric range to rely on. | PyPy documents a configurable small-integer cache, disabled by default in the standard interpreter configuration described in its documentation. |
| Can identity behavior differ from CPython? | Yes. Literal identity and small-integer reuse are implementation-dependent. | Yes. PyPy documents value-based identity behavior for primitive values, including int, and contrasts it with CPython. |
These descriptions are tied to the cited implementation documentation, not every release or configuration. PyPy’s standard interpreter optimizations page describes its configurable cache and tagged-pointer representation. Its differences from CPython page discusses primitive-value identity. Neither source makes an integer identity result portable between interpreters.
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Why a fixed “small integer” range is not a safe rule
You may encounter examples claiming that integers inside a particular range are always cached. Do not treat a range like that as a Python language rule. CPython documents reuse for small integers as an implementation detail and explicitly notes that the small/large boundary has changed and can change again. PyPy’s cache is configurable and its documented default differs from CPython’s behavior.
Consequently, a cutoff observed on one interpreter cannot safely predict results on another implementation, another version, or a differently configured runtime. When demonstrating identity, name the interpreter and version and report the result only as an observation in that setup.
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How to compare integers correctly
Use equality for numeric comparisons:
a = 1000
b = 1000
print(a == b) # True: the integer values are equal
print(a is b) # Identity is not guaranteedThe first result follows from the values. The second can vary with how the interpreter handles those values. For identity checks, use is where identity is the intended question and the object is a known singleton—for example, value is None—rather than as a substitute for numeric equality.
What id() can and cannot tell you
id(x) returns an identity value that is unique while x is alive. It can help investigate whether two live references point to the same object in a particular process, but its numeric output is not a durable identifier across runs.
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In CPython, id() corresponds to the object’s memory address. After an object is deleted, that address may be reused. Do not store an id() value as a permanent key or use it to compare objects across separate executions.
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