Python JSON: three libraries, three outputs

Give the same dict to three libraries and you get three different JSON documents. That is not a rhetorical flourish: I fed {"id": 1, "name": "测试"} to the standard library json, to orjson 3.13.0 and to msgspec 0.22.0 on this machine, and the byte counts, the key shapes, even the way numbers are written all came out different. Four of those differences raise no error at all.

So I built a comparison table of the nine defaults people trip over most, ran every row, and wrote a reproducible minimal case for each silent behaviour. The environment is Windows 11 with CPython 3.13.14; orjson and msgspec were installed with pip into an isolated directory so the main environment stayed untouched. Every number below came out of an actual run, not a doc page. No affiliate links in this article.

One boundary first: the 50,000-record four-format comparison answers "which wire format should I pick". This article does not repeat that size chart; it answers a narrower question — after you move a dict from the standard library to either of the two newer libraries, which defaults change, and which of those changes arrive without an error.

TL;DR: before swapping libraries, confirm three things. First, does your data contain NaN? orjson and msgspec will quietly write it as null, while the standard library writes JSON that is not valid JSON. Second, are your timestamps naive objects from datetime.now()? Neither new library adds an offset, so the reader has no way to know the timezone. Third, are all your dict keys str? Int keys go in and string keys come out, so the round trip no longer compares equal. If any of the three applies, fix the conversion before you talk about speed.

One payload, three outputs

Start with the smallest possible case, the return type:

• json.dumps returns str, and by default escapes Chinese into \u6d4b\u8bd5
• orjson.dumps returns bytes, with Chinese emitted as raw UTF-8
• msgspec.json.encode also returns bytes, byte-for-byte identical to orjson on the same payload (the comparison returned true)

That difference looks cosmetic until it hits real code. Concatenate an orjson result onto a string and you get TypeError: can't concat str to bytes. Write it to a file opened in text mode and you get TypeError: write() argument must be str, not bytes — and the file has already been created with 0 bytes, which is how my first attempt ended up feeding an empty file to a reader that happily treated it as valid JSON.

Nine defaults in one table

Four silent behaviours sit in the right column, three in the left. Here is what each one costs.

Silent change one: NaN becomes null

json.dumps copies JavaScript and writes the literal NaN. The Python documentation is explicit about this: "The RFC does not permit the representation of infinite or NaN number values. Despite that, by default, this module accepts and outputs Infinity, -Infinity, and NaN as if they were valid JSON number literal values," followed by "Neither of these calls raises an exception, but the results are not valid JSON."

orjson takes the other road and writes null for NaN, Infinity and -Infinity; the README calls the result "compliant JSON, as null". msgspec agrees, and all three values came out as null in my run.

That creates a cross-library break. JSON produced by the standard library, containing NaN, is rejected by both new libraries: orjson.JSONDecodeError: unexpected character, expected a JSON value: line 1 column 10 (char 9) and msgspec.DecodeError: JSON is malformed: invalid character (byte 9). The standard library parses its own NaN without complaint, because its decoder is permissive by design.

The consequence scales. I planted 5 NaN values in 5,000 rows: after an orjson round trip all 5 came back as null, while a json round trip kept all 5 as NaN. Downstream code reads null as "missing", not as "invalid number", so those points vanish from the metrics without a trace. If your aggregation is supposed to surface non-numeric values, both new libraries make you scan for them yourself.

One more, easier to miss: json.loads('1e400') returns inf with no error, orjson raises number is infinity when parsed as double, and msgspec raises ValidationError: Number out of range. Same dirty input, three different fates.

Silent change two: naive datetimes lose their timezone

This is the one I would write a lint rule for. json.dumps raises TypeError: Object of type datetime is not JSON serializable, which is ugly but safe. orjson and msgspec both handle datetimes natively and write RFC 3339:

• An object with +08:00: both libraries emit 2026-10-28T20:44:05.123456+08:00
• An object in UTC: orjson emits +00:00, msgspec emits Z (orjson needs OPT_UTC_Z for the Z form)
• A naive object from datetime.now(): both libraries write it as-is with no offset at all — 2026-10-28T20:44:05.123456

The third case is the trap. The writer in Beijing believes the string is local time; the reader on a UTC box parses it as UTC; eight hours evaporate and nothing in the pipeline complains. The msgspec migration guide says it outright: "By default, both libraries encode naive datetimes identically (without timezone info)," and notes that orjson has OPT_NAIVE_UTC to treat naive values as UTC while "msgspec has no equivalent flag" — the documented workaround is to call replace(tzinfo=timezone.utc) before encoding.

It is documented, and almost nobody reads it. My rule: if you adopt either library, encode "no naive datetimes" as a code check rather than a convention.

Silent change three: int keys become str, so round trips stop matching

Take {1: "a", 2: "b"}:

• json.dumps returns '{"1": "a", "2": "b"}' with no error
• orjson.dumps raises TypeError: Dict key must be str unless you pass option=orjson.OPT_NON_STR_KEYS
• msgspec.json.encode quietly returns b'{"1":"a","2":"b"}' — unlike orjson, it converts int and float keys on its own

Keys come back as str, so loads(dumps(x)) != x. The Python documentation has a dedicated sentence for it: "if a dictionary is converted into JSON and then back into a dictionary, the dictionary may not equal the original one. That is, loads(dumps(x)) != x if x has non-string keys."

What actually hurts is the asymmetry — one library refuses, the others stay quiet. With int keys, orjson tells you immediately when you migrate; msgspec and the standard library say nothing. The bool and None keys are worse: the standard library stringifies them to "true" and "null", orjson with OPT_NON_STR_KEYS does the same, and msgspec raises TypeError: Only dicts with str-like or number-like keys are supported. One dictionary, three libraries, two different outcomes.

Silent change four: msgspec drops unknown fields by default

This one has security implications. Decode {"id": 7, "name": "kim", "role": "admin", "quota": 999} into a msgspec.Struct that only declares id and name, and you get User(id=7, name='kim'). role and quota are gone, with no exception and no warning.

The documentation states it plainly: "By default msgspec will skip unknown fields encountered when decoding into" a Struct, and includes an example where a misspelled field name is ignored. To catch it you must declare forbid_unknown_fields=True, at which point you get msgspec.ValidationError: Object contains unknown field role``.

The flip side is real value: type checking is strict. Decoding {"id": "7"} into an int field raises ValidationError: Expected int, got str - at $.id, with the field path included — far more useful than a plain dict where types are whatever the payload happened to contain. {"id": true} is rejected too (Expected int, got bool), while in Python isinstance(True, int)` is true, so the standard-library route cannot catch that impostor at all.

Where orjson refuses instead of guessing

orjson's integer range is documented in the README: "The range supported is a signed 64-bit integer's minimum (-9223372036854775807) to an unsigned 64-bit integer's maximum (18446744073709551615)." Passing pow(2, 64) raised TypeError: Integer exceeds 64-bit range, while the standard library and msgspec both wrote a 30-digit integer without complaint.

If your IDs come from dirty data that stores long digit strings as numbers, orjson will fail at encode time. That is better than failing silently, but scan for it before migrating.

Throughput on 50,000 rows

50,000 rows with id, name, score, active; encode averaged over 5 runs, decode averaged over 5 runs:

The 11.5% size gap comes from separators and escaping: the standard library defaults to ', ' and ': ' with spaces, and ensure_ascii=True turns every Chinese character into \uXXXX. On a short Chinese snippet that is 288 B against 156 B, an 81.1% difference (pure ASCII payloads will not show anything like it).

Decoding is only 1.8x faster, which is why many teams migrate and feel nothing. The real gap is msgspec's typed decode: the same payload decoded straight into list[Row] took 20.0 ms (best of 7), while json.loads needs 57.6 ms just to produce dicts — before you write a single validation line. That 2.9x comes from validating during the parse instead of walking the tree twice.

A note on methodology: these are single-machine, single-session comparisons meant to establish magnitude. They are not a cross-machine benchmark; CPU, payload shape and field count all move the ratio.

Troubleshooting: five errors I hit

Error one: TypeError: can't concat str to bytes

Concatenating orjson.dumps(data) onto a log string. The fix is .decode(), or a bytes-based template for the log line. Note the asymmetry: json.loads(orjson.dumps(x)) works because json accepts bytes, and orjson.loads(json.dumps(x)) works because orjson accepts str. Do not rely on memory — pick one convention for the codebase.

Error two: TypeError: write() argument must be str, not bytes

Writing an orjson result to a file opened with open(path, 'w'). The file is created with 0 bytes, and if you do not clean up, the next run reads an empty file and treats it as "valid but empty". Fix: open(path, 'wb'), or assert isinstance(payload, bytes) before writing.

Error three: TypeError: Integer exceeds 64-bit range

The boundary above. Convert IDs to str before serialising, or use msgspec, which has no such limit.

Error four: msgspec.ValidationError: Expected `int`, got `str` - at `$.id`

The upstream sent a number as a string. The good news is the path $.id; the bad news is how many people write the handler:

try:
    obj = msgspec.json.decode(raw, type=User)
except msgspec.DecodeError:
    log.warning("bad payload")

ValidationError is a subclass of DecodeError (the issubclass check returned true), so catching the parent first turns "wrong field type" into "malformed JSON" and the log never shows the real problem. Fix: except msgspec.ValidationError first, except msgspec.DecodeError after.

Error five: orjson.JSONDecodeError: str is not valid UTF-8: surrogates not allowed

The upstream stuffed bytes like \xff into a string field. The standard library lets some of these through as ordinary characters; orjson rejects the input (the README states input must be valid UTF-8). Fix it at the boundary with raw.decode('utf-8', errors='replace') or reject explicitly, rather than discovering it at database write time.

Two more worth noting: a self-referencing structure gives ValueError: Circular reference detected in the standard library, and set gives TypeError: Object of type set is not JSON serializable there but TypeError: Type is not JSON serializable: decimal.Decimal in orjson when you try a Decimal — msgspec supports Decimal natively and even lets you set decimal_format. These three libraries do not agree on what "serialisable" even means.

How to choose

1. Read-only, speed-first, clean data: orjson. Remember the three rules — bytes out, NaN becomes null, 64-bit ceiling.

2. You want typed objects out of JSON: msgspec. Validation happens during parsing, errors carry field paths, and a bool impersonating an int gets caught; the price is that you must set forbid_unknown_fields=True or extra fields disappear.

3. A script that stores intermediate state and a human who wants to trust the file: the standard library. Most permissive, least defensive.

When migrating, work in this order: scan for NaN and Infinity (both new libraries rewrite them to null), then find every naive datetime.now() call, then confirm every dict key is str. Only after those three does the performance gain become the interesting part.

Scope of this test

I measured CPython 3.13.14 on Windows 11 only — no PyPy, no Linux. I did not cover HTTP framework json integration, non-Python consumers (JavaScript's JSON.parse throws on NaN, the same root cause as "the standard library emits invalid JSON", but I did not test the front end), MessagePack or YAML, or numpy array serialisation. All of that is "re-verify after you swap the library" territory.

Closing

None of the three libraries is better; they simply ship different defaults. What bites is not the throughput gap but the four changes that arrive without an error: NaN turning into null, a timezone evaporating, key types shifting, fields disappearing. They share one property — they never appear in your test fixtures and always appear in production data.

Further reading: for the encoding layer on disk, Parquet's default dictionary encoding made the file 15% bigger covers the same class of default-value trap; and for another case of "the config looked fine but the data went missing", read the logging tests.

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