GLM 5.3 vs gpt-oss-120b

Updated October 21, 2026. 57 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 5.3 Replace gpt-oss-120b

GLM 5.3Z.ai

Coding: 73.1
Overall: 44.8
Output $/M: $1.40

gpt-oss-120bOpenAI

Coding: 34.5
Overall: 11.6
Output $/M: $0.60

vs boards won of 57 shared

Decision reading

Pick GLM 5.3 for coding: it scores 73.1 on the Vibe Coding Index against 34.5 for gpt-oss-120b.

Pick gpt-oss-120b when output tokens cost 2.3x less, or when the benchmark that matters is LiveCodeBench (Vals AI), where it leads.

Model AGLM 5.3Z.ai73.1/1002 independent benchmarks$0.36 per solved task Model Bgpt-oss-120bOpenAI34.5/1002 independent benchmarks$0.51 per solved task

574321

Shared results: 57
GLM 5.3 only: 43
gpt-oss-120b only: 21
Like-for-like categories: 8 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

Coding
Vibe Coding Index, four public coding benchmarks
GLM 5.3
GLM 5.3 scores 73.1 against 34.5.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
GLM 5.3
GLM 5.3 leads, 44.8 against 11.6.
Same board, both measured
Agentic work
AA Agentic Index
GLM 5.3
GLM 5.3 leads, 53.1 against 3.7.
Same board, both measured
Reasoning
GPQA Diamond
GLM 5.3
GLM 5.3 leads, 88.1% against 78.5%.
Same board, both measured
Math
AIME
No call
Only gpt-oss-120b has a AIME result, 92.6%.
Directional only
Knowledge
MMLU Pro
GLM 5.3
GLM 5.3 leads, 86.8% against 79.2%.
Same board, both measured
Web design
Design Arena: websites
GLM 5.3
GLM 5.3 leads, 1295 against 968.
Same board, both measured
General
Vals Index
No call
Only GLM 5.3 has a Vals Index result, 59.7%.
Directional only
Long documents
Listed context window
GLM 5.3
GLM 5.3 takes 1.05M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
gpt-oss-120b
GLM 5.3 $0.0008, gpt-oss-120b $0.0004 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 5.3
GLM 5.3 $0.36, gpt-oss-120b $0.51.
Published attempt cost and pass rate

At a glance

Coding index: GLM 5.3 73.1 from 2 benchmarks, gpt-oss-120b 34.5 from 2 benchmarks.
Widest category: Agentic work, 50.9 points apart on average over 3 shared benchmarks.
Widest single gap: SWE-bench Verified (Vals AI): GLM 5.3 95.4% against gpt-oss-120b 33.6%, 61.8 points apart on a 0 to 100 scale.
Output price: GLM 5.3 $1.40 against gpt-oss-120b $0.60 per million output tokens, 2.3x apart.
Context window: GLM 5.3 takes 1.05M tokens, gpt-oss-120b 131K.

Benchmark by benchmark

First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.

Boards won, where both have a result
Category	GLM 5.3	gpt-oss-120b
Coding	7	1
Intelligence	2	0
Agentic work	3	0
Reasoning	8	0
Math	3	0
Knowledge	7	0
Web design	6	0
General	20	0
All shared boards	56	1

CodingLike-for-likeFour of these boards feed the Vibe Coding Index71

Coding benchmarks for GLM 5.3 and gpt-oss-120b
Benchmark	GLM 5.3	gpt-oss-120b	Leader
Aider polyglot	Not measured	41.8%	Only gpt-oss-120b measured
SWE-bench Verified	Not measured	26.0%	Only gpt-oss-120b measured
LiveBench coding	82.8%	Not measured	Only GLM 5.3 measured
LMArena WebDev	1621	Not measured	Only GLM 5.3 measured
AA Coding Index	74.8	30.4	GLM 5.3
LiveCodeBench (Vals AI)	80.5%	83.2%	gpt-oss-120b
SWE-bench Verified (Vals AI)	95.4%	33.6%	GLM 5.3
Terminal-Bench 4 (Vals AI)	38.9%	Not measured	Only GLM 5.3 measured
Vibe Code Bench (Vals AI)	78.1%	Not measured	Only GLM 5.3 measured
Code migration (Vals AI)	44.2%	Not measured	Only GLM 5.3 measured
ProgramBench (Vals AI)	1.5%	Not measured	Only GLM 5.3 measured
IOI (Vals AI)	68.4%	Not measured	Only GLM 5.3 measured
Terminal-Bench 2.1 (Vals AI)	71.5%	Not measured	Only GLM 5.3 measured
Vibe Code Bench 1-100 (Vals AI)	20.0%	Not measured	Only GLM 5.3 measured
SkillsBench (Vals AI)	47.5%	Not measured	Only GLM 5.3 measured
CyberBench (Vals AI)	72.1%	Not measured	Only GLM 5.3 measured
LiveCodeBench (Artificial Analysis run)	Not measured	87.8%	Only gpt-oss-120b measured
SciCode	59.0%	34.0%	GLM 5.3
Terminal-Bench Hard (Artificial Analysis run)	Not measured	23.5%	Only gpt-oss-120b measured
Terminal-Bench	Not measured	18.7%	Only gpt-oss-120b measured
FrontierCode	40.1%	Not measured	Only GLM 5.3 measured
FrontierSWE	30.2%	Not measured	Only GLM 5.3 measured
WeirdML	75.4%	48.2%	GLM 5.3
ALE-Bench	1317.4	575.6	GLM 5.3
LMArena Text: coding	1497	1379	GLM 5.3
LMArena Text: software and IT	1493	1387	GLM 5.3
LMArena WebDev: React	1641	Not measured	Only GLM 5.3 measured
LMArena WebDev: HTML	1595	Not measured	Only GLM 5.3 measured
Neither model has a result on the other 10 boards here

IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20

Intelligence benchmarks for GLM 5.3 and gpt-oss-120b
Benchmark	GLM 5.3	gpt-oss-120b	Leader
AA Intelligence Index	44.8	11.6	GLM 5.3
Epoch Capabilities Index	156.0	139.8	GLM 5.3

Agentic workLike-for-likePublic boards, shown as published, kept out of the index30

Agentic work benchmarks for GLM 5.3 and gpt-oss-120b
Benchmark	GLM 5.3	gpt-oss-120b	Leader
AA Agentic Index	53.1	3.7	GLM 5.3
Design Arena: agents building mobile apps	1194	Not measured	Only GLM 5.3 measured
Design Arena: HTML slides	1189	Not measured	Only GLM 5.3 measured
Design Arena: PowerPoint slides	1255	Not measured	Only GLM 5.3 measured
LiveBench research	73.2%	Not measured	Only GLM 5.3 measured
LiveBench browser	0.0%	Not measured	Only GLM 5.3 measured
LiveBench long agentic	35.9%	Not measured	Only GLM 5.3 measured
SREBench (Vals AI)	9.2%	Not measured	Only GLM 5.3 measured
Finance Agent (Vals AI)	55.8%	Not measured	Only GLM 5.3 measured
Tax Agent Bench (Vals AI)	39.7%	Not measured	Only GLM 5.3 measured
Legal Research Bench (Vals AI)	49.0%	Not measured	Only GLM 5.3 measured
Harvey's Legal Agent Benchmark (Vals AI)	14.6%	Not measured	Only GLM 5.3 measured
Excel Modeling Benchmark (Vals AI)	56.3%	Not measured	Only GLM 5.3 measured
Terminal-Bench-Science (Vals AI)	5.7%	Not measured	Only GLM 5.3 measured
τ²-Bench Telecom	Not measured	65.8%	Only gpt-oss-120b measured
APEX-Agents	56.6%	4.4%	GLM 5.3
METR time horizons	Not measured	56.6%	Only gpt-oss-120b measured
Vending-Bench 2	$8,164	-$22	GLM 5.3
Neither model has a result on the other 21 boards here

ReasoningLike-for-likePublic boards, shown as published, kept out of the index80

Reasoning benchmarks for GLM 5.3 and gpt-oss-120b
Benchmark	GLM 5.3	gpt-oss-120b	Leader
GPQA Diamond	88.1%	78.5%	GLM 5.3
LiveBench reasoning	85.8%	Not measured	Only GLM 5.3 measured
MysteryMechanism (Vals AI)	23.0%	Not measured	Only GLM 5.3 measured
GPQA Diamond (Artificial Analysis run)	91.7%	78.2%	GLM 5.3
Humanity's Last Exam (Artificial Analysis run)	42.3%	19.6%	GLM 5.3
GPQA Diamond (Epoch AI run)	90.9%	75.8%	GLM 5.3
SimpleBench	Not measured	22.1%	Only gpt-oss-120b measured
Chess puzzles	37.0%	Not measured	Only GLM 5.3 measured
Mystery game puzzles	33.0%	2.0%	GLM 5.3
LMArena Text: hard prompts	1490	1364	GLM 5.3
LMArena Text: expert	1514	1356	GLM 5.3
LMArena Text: hard prompts in English	1496	1368	GLM 5.3
Neither model has a result on the other 9 boards here

MathLike-for-likePublic boards, shown as published, kept out of the index30

Math benchmarks for GLM 5.3 and gpt-oss-120b
Benchmark	GLM 5.3	gpt-oss-120b	Leader
AIME	Not measured	92.6%	Only gpt-oss-120b measured
MATH 500 (Vals AI)	Not measured	94.8%	Only gpt-oss-120b measured
MGSM (Vals AI)	Not measured	92.0%	Only gpt-oss-120b measured
AA Math Index	Not measured	93.4	Only gpt-oss-120b measured
AIME 2025 (Artificial Analysis run)	Not measured	93.4%	Only gpt-oss-120b measured
OTIS Mock AIME	91.1%	88.9%	GLM 5.3
FrontierMath, tiers 1 to 3	68.8%	Not measured	Only GLM 5.3 measured
FrontierMath, tier 4	29.3%	Not measured	Only GLM 5.3 measured
ProofBench	49.0%	Not measured	Only GLM 5.3 measured
LMArena Text: math	1495	1389	GLM 5.3
LMArena Text: mathematical work	1497	1386	GLM 5.3
Neither model has a result on the other 2 boards here

KnowledgeLike-for-likePublic boards, shown as published, kept out of the index70

Knowledge benchmarks for GLM 5.3 and gpt-oss-120b
Benchmark	GLM 5.3	gpt-oss-120b	Leader
MMLU Pro	86.8%	79.2%	GLM 5.3
LegalBench (Vals AI)	84.8%	75.9%	GLM 5.3
CaseLaw (Vals AI)	Not measured	48.8%	Only gpt-oss-120b measured
CorpFin (Vals AI)	Not measured	58.2%	Only gpt-oss-120b measured
TaxEval (Vals AI)	72.4%	71.6%	GLM 5.3
MedQA (Vals AI)	Not measured	91.4%	Only gpt-oss-120b measured
MedCode (Vals AI)	42.9%	Not measured	Only GLM 5.3 measured
MMLU Pro (Artificial Analysis run)	Not measured	80.8%	Only gpt-oss-120b measured
SimpleQA Verified	41.0%	Not measured	Only GLM 5.3 measured
LMArena Text: science	1498	1382	GLM 5.3
LMArena Text: medicine and healthcare	1481	1388	GLM 5.3
LMArena Text: legal and government	1473	1355	GLM 5.3
LMArena Text: business and finance	1459	1359	GLM 5.3
Neither model has a result on the other 4 boards here

Web designLike-for-likePublic boards, shown as published, kept out of the index60

Web design benchmarks for GLM 5.3 and gpt-oss-120b
Benchmark	GLM 5.3	gpt-oss-120b	Leader
Design Arena: websites	1295	968	GLM 5.3
Design Arena: UI components	1314	927	GLM 5.3
Design Arena: game dev	1311	992	GLM 5.3
Design Arena: data viz	1260	987	GLM 5.3
Design Arena: 3D	1332	901	GLM 5.3
Design Arena: all code categories	1304	963	GLM 5.3
Design Arena: SVG	1292	Not measured	Only GLM 5.3 measured
Design Arena: ASCII art	1213	Not measured	Only GLM 5.3 measured
LiveBench design	77.3%	Not measured	Only GLM 5.3 measured

GeneralLike-for-likePublic boards, shown as published, kept out of the index200

General benchmarks for GLM 5.3 and gpt-oss-120b
Benchmark	GLM 5.3	gpt-oss-120b	Leader
Vals Index	59.7%	Not measured	Only GLM 5.3 measured
LiveBench data analysis	70.2%	Not measured	Only GLM 5.3 measured
Public Benefits Bench (Vals AI)	68.5%	Not measured	Only GLM 5.3 measured
MedScribe (Vals AI)	88.8%	Not measured	Only GLM 5.3 measured
IFBench	Not measured	69.0%	Only gpt-oss-120b measured
Long context reasoning (AA-LCR)	79.7%	52.0%	GLM 5.3
DTBench	87.7%	76.3%	GLM 5.3
LMCA	55.5%	22.1%	GLM 5.3
Fiction.LiveBench	Not measured	44.4%	Only gpt-oss-120b measured
Creative writing (Lech Mazur)	Not measured	77.1%	Only gpt-oss-120b measured
Surface Evolver Bench	Not measured	25.0%	Only gpt-oss-120b measured
LMArena Text	1474	1366	GLM 5.3
LMArena Text: instruction following	1479	1318	GLM 5.3
LMArena Text: creative writing	1458	1274	GLM 5.3
LMArena Text: English	1482	1375	GLM 5.3
LMArena Text: non-English	1459	1351	GLM 5.3
LMArena Text: Chinese	1532	1385	GLM 5.3
LMArena Text: German	1488	1353	GLM 5.3
LMArena Text: Japanese	1460	1331	GLM 5.3
LMArena Text: Korean	1460	1282	GLM 5.3
LMArena Text: French	1503	1370	GLM 5.3
LMArena Text: Spanish	1462	1389	GLM 5.3
LMArena Text: Russian	1469	1343	GLM 5.3
LMArena Text: Polish	1466	1353	GLM 5.3
LMArena Text: multi-turn	1472	1340	GLM 5.3
LMArena Text: longer queries	1483	1319	GLM 5.3
LMArena Text: writing and language	1469	1306	GLM 5.3
LMArena Text: entertainment and media	1447	1292	GLM 5.3
Neither model has a result on the other 18 boards here

Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared

Image generation benchmarks for GLM 5.3 and gpt-oss-120b
Benchmark	GLM 5.3	gpt-oss-120b	Leader
Neither model has a result on 5 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

GLM 5.3$0.0008Fits in one request

gpt-oss-120b$0.0004Fits in one request

gpt-oss-120b costs less

Repository review

50K input + 3K output tokens

GLM 5.3$0.0081Fits in one request

gpt-oss-120b$0.0093Fits in one request

GLM 5.3 costs less

Agent loop

200K input + 10K output tokens

GLM 5.3$0.030Fits in one request

gpt-oss-120b$0.036Exceeds the context window

GLM 5.3 costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of GLM 5.3 and gpt-oss-120b
Specification	Z.ai GLM 5.3	OpenAI gpt-oss-120b
Maker	Z.ai	OpenAI
Weights	Open weights	Open weights
Released	August 18, 2026	August 5, 2025
Context window	1.05M	131K
Input $/Mtok	$0.079	$0.15
Output $/Mtok	$1.40	$0.60
Coding index	73.1	34.5
Independent measurements behind it	2	2
Cost per solved task	$0.36	$0.51
Source	OpenRouter listing	OpenRouter listing

Questions

Is GLM 5.3 better than gpt-oss-120b for coding?

Pick GLM 5.3 for coding: it scores 73.1 on the Vibe Coding Index against 34.5 for gpt-oss-120b. Pick gpt-oss-120b when output tokens cost 2.3x less, or when the benchmark that matters is LiveCodeBench (Vals AI), where it leads.

Which is cheaper, GLM 5.3 or gpt-oss-120b?

GLM 5.3 costs $0.079 per million input tokens and $1.40 per million output tokens; gpt-oss-120b costs $0.15 and $0.60. Per solved SWE-bench Verified task, GLM 5.3 costs $0.36 and gpt-oss-120b $0.51.

Which has the bigger context window, GLM 5.3 or gpt-oss-120b?

GLM 5.3, with 1.05M tokens against 131K.

How many benchmarks is the GLM 5.3 vs gpt-oss-120b comparison based on?

57 benchmarks have a result for both models as of October 21, 2026. GLM 5.3's coding index rests on 2 independent benchmarks and gpt-oss-120b's on 2. Where only one model has a result, the page says so instead of naming a winner.

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Decision reading

Which one for your work

Coding

Intelligence

Agentic work

Reasoning

Math

Knowledge

Web design

General

Long documents

Everyday cost

Cost per solved task