GLM 5.3 vs o1
Updated October 21, 2026. 46 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick GLM 5.3 for coding: it scores 73.1 on the Vibe Coding Index against 61.7 for o1.
Pick o1 when what matters is TaxEval (Vals AI) or SimpleQA Verified, where it leads.
- Shared results
- 46
- GLM 5.3 only
- 54
- o1 only
- 22
- Like-for-like categories
- 6 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 61.7.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
GLM 5.3
GLM 5.3 leads, 44.8 against 15.2.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GLM 5.3 has a AA Agentic Index result, 53.1.
Directional only
Reasoning
GPQA Diamond
GLM 5.3
GLM 5.3 leads, 88.1% against 73.2%.
Same board, both measured
Math
AIME
No call
Only o1 has a AIME result, 71.5%.
Directional only
Knowledge
MMLU Pro
GLM 5.3
GLM 5.3 leads, 86.8% against 83.5%.
Same board, both measured
Web design
Design Arena: websites
No call
Only GLM 5.3 has a Design Arena: websites result, 1295.
Directional only
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 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5.3
GLM 5.3 $0.0008, o1 $0.045 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 5.3 has the number, $0.36.
Directional only
At a glance
- Coding index
- GLM 5.3 73.1 from 2 benchmarks, o1 61.7 from 1 benchmark.
- Widest category
- Coding, 27.5 points apart on average over 5 shared benchmarks.
- Widest single gap
- FrontierMath, tiers 1 to 3: GLM 5.3 68.8% against o1 14.7%, 54.0 points apart on a 0 to 100 scale.
- Output price
- GLM 5.3 $1.40 against o1 $60.00 per million output tokens, 42.9x apart.
- Context window
- GLM 5.3 takes 1.05M tokens, o1 200K.
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.
| Category | GLM 5.3 | Bars | o1 | Ties |
|---|---|---|---|---|
| Coding | 5 | 0 | ||
| Intelligence | 2 | 0 | ||
| Reasoning | 8 | 0 | ||
| Math | 4 | 0 | ||
| Knowledge | 6 | 2 | ||
| General | 19 | 0 | ||
| All shared boards | 44 | 2 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index50
| Benchmark | GLM 5.3 | o1 | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 61.7% | Only o1 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 | 39.7 | GLM 5.3 |
| LiveCodeBench (Vals AI) | 80.5% | 50.3% | GLM 5.3 |
| SWE-bench Verified (Vals AI) | 95.4% | Not measured | Only GLM 5.3 measured |
| 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 | 67.9% | Only o1 measured |
| SciCode | 59.0% | Not measured | Only GLM 5.3 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 12.9% | Only o1 measured |
| FrontierCode | 40.1% | Not measured | Only GLM 5.3 measured |
| FrontierSWE | 30.2% | Not measured | Only GLM 5.3 measured |
| WeirdML | 75.4% | 46.1% | GLM 5.3 |
| ALE-Bench | 1317.4 | Not measured | Only GLM 5.3 measured |
| LMArena Text: coding | 1497 | 1367 | GLM 5.3 |
| LMArena Text: software and IT | 1493 | 1366 | 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 12 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 5.3 | o1 | Leader |
|---|---|---|---|
| AA Intelligence Index | 44.8 | 15.2 | GLM 5.3 |
| Epoch Capabilities Index | 156.0 | 141.7 | GLM 5.3 |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index18 boards, none shared
| Benchmark | GLM 5.3 | o1 | Leader |
|---|---|---|---|
| AA Agentic Index | 53.1 | Not measured | Only GLM 5.3 measured |
| 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 | 62.6% | Only o1 measured |
| APEX-Agents | 56.6% | Not measured | Only GLM 5.3 measured |
| METR time horizons | Not measured | 51.1% | Only o1 measured |
| Vending-Bench 2 | $8,164 | Not measured | Only GLM 5.3 measured |
| Neither model has a result on the other 21 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | GLM 5.3 | o1 | Leader |
|---|---|---|---|
| GPQA Diamond | 88.1% | 73.2% | 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 |
| MMMU Pro (Vals AI) | Not measured | 77.4% | Only o1 measured |
| GPQA Diamond (Artificial Analysis run) | 91.7% | 74.7% | GLM 5.3 |
| Humanity's Last Exam (Artificial Analysis run) | 42.3% | 7.0% | GLM 5.3 |
| GPQA Diamond (Epoch AI run) | 90.9% | 76.8% | GLM 5.3 |
| ARC-AGI | Not measured | 30.7% | Only o1 measured |
| SimpleBench | Not measured | 40.1% | Only o1 measured |
| Chess puzzles | 37.0% | 10.0% | GLM 5.3 |
| Mystery game puzzles | 33.0% | Not measured | Only GLM 5.3 measured |
| VPCT | Not measured | 37.0% | Only o1 measured |
| LMArena Text: hard prompts | 1490 | 1371 | GLM 5.3 |
| LMArena Text: expert | 1514 | 1361 | GLM 5.3 |
| LMArena Text: hard prompts in English | 1496 | 1372 | GLM 5.3 |
| Neither model has a result on the other 6 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GLM 5.3 | o1 | Leader |
|---|---|---|---|
| AIME | Not measured | 71.5% | Only o1 measured |
| MATH 500 (Vals AI) | Not measured | 90.4% | Only o1 measured |
| MGSM (Vals AI) | Not measured | 89.3% | Only o1 measured |
| OTIS Mock AIME | 91.1% | 73.3% | GLM 5.3 |
| FrontierMath, tiers 1 to 3 | 68.8% | 14.7% | GLM 5.3 |
| FrontierMath, tier 4 | 29.3% | Not measured | Only GLM 5.3 measured |
| ProofBench | 49.0% | Not measured | Only GLM 5.3 measured |
| MATH level 5 | Not measured | 94.7% | Only o1 measured |
| LMArena Text: math | 1495 | 1388 | GLM 5.3 |
| LMArena Text: mathematical work | 1497 | 1390 | GLM 5.3 |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index62
| Benchmark | GLM 5.3 | o1 | Leader |
|---|---|---|---|
| MMLU Pro | 86.8% | 83.5% | GLM 5.3 |
| LegalBench (Vals AI) | 84.8% | 80.4% | GLM 5.3 |
| TaxEval (Vals AI) | 72.4% | 74.3% | o1 |
| MedQA (Vals AI) | Not measured | 96.5% | Only o1 measured |
| MedCode (Vals AI) | 42.9% | Not measured | Only GLM 5.3 measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 84.1% | Only o1 measured |
| SimpleQA Verified | 41.0% | 41.1% | o1 |
| GeoBench | Not measured | 80.0% | Only o1 measured |
| LMArena Text: science | 1498 | 1369 | GLM 5.3 |
| LMArena Text: medicine and healthcare | 1481 | 1355 | GLM 5.3 |
| LMArena Text: legal and government | 1473 | 1368 | GLM 5.3 |
| LMArena Text: business and finance | 1459 | 1328 | GLM 5.3 |
| Neither model has a result on the other 5 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GLM 5.3 | o1 | Leader |
|---|---|---|---|
| Design Arena: websites | 1295 | Not measured | Only GLM 5.3 measured |
| Design Arena: UI components | 1314 | Not measured | Only GLM 5.3 measured |
| Design Arena: game dev | 1311 | Not measured | Only GLM 5.3 measured |
| Design Arena: data viz | 1260 | Not measured | Only GLM 5.3 measured |
| Design Arena: 3D | 1332 | Not measured | Only GLM 5.3 measured |
| Design Arena: all code categories | 1304 | Not measured | Only GLM 5.3 measured |
| 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 index190
| Benchmark | GLM 5.3 | o1 | 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 | 70.3% | Only o1 measured |
| Long context reasoning (AA-LCR) | 79.7% | 65.0% | GLM 5.3 |
| DTBench | 87.7% | 74.7% | GLM 5.3 |
| LMCA | 55.5% | 22.3% | GLM 5.3 |
| Fiction.LiveBench | Not measured | 83.3% | Only o1 measured |
| Creative writing (Lech Mazur) | Not measured | 70.2% | Only o1 measured |
| LMArena Text | 1474 | 1366 | GLM 5.3 |
| LMArena Text: instruction following | 1479 | 1367 | GLM 5.3 |
| LMArena Text: creative writing | 1458 | 1347 | GLM 5.3 |
| LMArena Vision | Not measured | 1168 | Only o1 measured |
| LMArena Text: English | 1482 | 1372 | GLM 5.3 |
| LMArena Text: non-English | 1459 | 1358 | GLM 5.3 |
| LMArena Text: Chinese | 1532 | 1394 | GLM 5.3 |
| LMArena Text: German | 1488 | 1337 | GLM 5.3 |
| LMArena Text: Japanese | 1460 | 1346 | GLM 5.3 |
| LMArena Text: Korean | 1460 | 1396 | GLM 5.3 |
| LMArena Text: French | 1503 | 1344 | GLM 5.3 |
| LMArena Text: Spanish | 1462 | 1345 | GLM 5.3 |
| LMArena Text: Russian | 1469 | 1356 | GLM 5.3 |
| LMArena Text: Polish | 1466 | Not measured | Only GLM 5.3 measured |
| LMArena Text: multi-turn | 1472 | 1355 | GLM 5.3 |
| LMArena Text: longer queries | 1483 | 1378 | GLM 5.3 |
| LMArena Text: writing and language | 1469 | 1360 | GLM 5.3 |
| LMArena Text: entertainment and media | 1447 | 1358 | GLM 5.3 |
| LMArena Vision: English | Not measured | 1162 | Only o1 measured |
| LMArena Vision: Chinese | Not measured | 1235 | Only o1 measured |
| Neither model has a result on the other 16 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.3 | o1 | 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 costs less
Repository review
50K input + 3K output tokens
GLM 5.3 costs less
Agent loop
200K input + 10K output tokens
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.
| Specification | Z.ai GLM 5.3 | OpenAI o1 |
|---|---|---|
| Maker | Z.ai | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | August 18, 2026 | December 17, 2024 |
| Context window | 1.05M | 200K |
| Input $/Mtok | $0.079 | $15.00 |
| Output $/Mtok | $1.40 | $60.00 |
| Coding index | 73.1 | 61.7 |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | $0.36 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.3 better than o1 for coding?
Pick GLM 5.3 for coding: it scores 73.1 on the Vibe Coding Index against 61.7 for o1. Pick o1 when what matters is TaxEval (Vals AI) or SimpleQA Verified, where it leads.
Which is cheaper, GLM 5.3 or o1?
GLM 5.3 costs $0.079 per million input tokens and $1.40 per million output tokens; o1 costs $15.00 and $60.00. Cost per solved task is known for GLM 5.3 only.
Which has the bigger context window, GLM 5.3 or o1?
GLM 5.3, with 1.05M tokens against 200K.
How many benchmarks is the GLM 5.3 vs o1 comparison based on?
46 benchmarks have a result for both models as of October 21, 2026. GLM 5.3's coding index rests on 2 independent benchmarks and o1's on 1. Where only one model has a result, the page says so instead of naming a winner.