GLM 5.2 vs GPT-5.5
Updated October 21, 2026. 97 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick GPT-5.5 for coding: it scores 67.8 on the Vibe Coding Index against 57.7 for GLM 5.2.
Pick GLM 5.2 when output tokens cost 4.3x less, or when a solved task costs $0.86 against $1.65.
- Shared results
- 97
- GLM 5.2 only
- 0
- GPT-5.5 only
- 35
- 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
GPT-5.5
GPT-5.5 scores 67.8 against 57.7.
1 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5.5
GPT-5.5 leads, 38.4 against 33.7.
Same board, both measured
Agentic work
AA Agentic Index
GLM 5.2
GLM 5.2 leads, 38.4 against 36.4.
Same board, both measured
Reasoning
GPQA Diamond
GPT-5.5
GPT-5.5 leads, 93.2% against 85.6%.
Same board, both measured
Knowledge
MMLU Pro
GPT-5.5
GPT-5.5 leads, 88.1% against 86.7%.
Same board, both measured
Web design
Design Arena: websites
GLM 5.2
GLM 5.2 leads, 1285 against 1256.
Same board, both measured
Long documents
Listed context window
GPT-5.5
GPT-5.5 takes 1.05M tokens against 1.05M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5.2
GLM 5.2 $0.0035, GPT-5.5 $0.020 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 5.2
GLM 5.2 $0.86, GPT-5.5 $1.65.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 5.2 57.7 from 1 benchmark, GPT-5.5 67.8 from 2 benchmarks.
- Widest category
- Reasoning, 16.6 points apart on average over 13 shared benchmarks.
- Widest single gap
- ARC-AGI-2: GLM 5.2 22.8% against GPT-5.5 85.0%, 62.2 points apart on a 0 to 100 scale.
- Output price
- GLM 5.2 $7.00 against GPT-5.5 $30.00 per million output tokens, 4.3x apart.
- Context window
- GLM 5.2 takes 1.05M tokens, GPT-5.5 1.05M.
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 5.2 | Bars | GPT-5.5 | Ties |
|---|---|---|---|---|
| Coding | 4 | 13 | 1 tie | |
| Intelligence | 0 | 2 | ||
| Agentic work | 11 | 6 | ||
| Reasoning | 1 | 12 | ||
| Math | 1 | 5 | ||
| Knowledge | 2 | 8 | ||
| Web design | 5 | 3 | ||
| General | 4 | 19 | ||
| All shared boards | 28 | 68 | 1 tie |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index413
| Benchmark | GLM 5.2 | GPT-5.5 | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 85.1% | Only GPT-5.5 measured |
| LMArena WebDev | 1604 | 1513 | GLM 5.2 |
| AA Coding Index | 68.8 | 74.9 | GPT-5.5 |
| LiveCodeBench (Vals AI) | 69.5% | 85.3% | GPT-5.5 |
| SWE-bench Verified (Vals AI) | 82.8% | 82.6% | GLM 5.2 |
| Vibe Code Bench (Vals AI) | 64.0% | 69.8% | GPT-5.5 |
| Code migration (Vals AI) | 37.9% | 45.2% | GPT-5.5 |
| ProgramBench (Vals AI) | 0.5% | 0.5% | Tie |
| Terminal-Bench 2.1 (Vals AI) | 67.8% | 76.4% | GPT-5.5 |
| SkillsBench (Vals AI) | 45.1% | 62.5% | GPT-5.5 |
| SciCode | 51.2% | 56.1% | GPT-5.5 |
| Terminal-Bench Hard (Artificial Analysis run) | 50.8% | 60.6% | GPT-5.5 |
| Terminal-Bench | Not measured | 84.7% | Only GPT-5.5 measured |
| FrontierCode | 24.5% | 43.0% | GPT-5.5 |
| GSO-Bench | Not measured | 40.2% | Only GPT-5.5 measured |
| WeirdML | 70.1% | 84.9% | GPT-5.5 |
| MirrorCode | Not measured | 10.0% | Only GPT-5.5 measured |
| ALE-Bench | 1047.0 | 1943.0 | GPT-5.5 |
| LMArena Text: coding | 1487 | 1494 | GPT-5.5 |
| LMArena Text: software and IT | 1486 | 1493 | GPT-5.5 |
| LMArena WebDev: React | 1617 | 1506 | GLM 5.2 |
| LMArena WebDev: HTML | 1565 | 1543 | GLM 5.2 |
| LMArena WebDev: from an image | Not measured | 1532 | Only GPT-5.5 measured |
| Neither model has a result on the other 15 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 5.2 | GPT-5.5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 33.7 | 38.4 | GPT-5.5 |
| Epoch Capabilities Index | 151.9 | 159.7 | GPT-5.5 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index116
| Benchmark | GLM 5.2 | GPT-5.5 | Leader |
|---|---|---|---|
| AA Agentic Index | 38.4 | 36.4 | GLM 5.2 |
| Design Arena: agents building web apps | 1222 | 1117 | GLM 5.2 |
| Design Arena: agents building full-stack apps | 1241 | 1089 | GLM 5.2 |
| Design Arena: agents building mobile apps | 1160 | 1143 | GLM 5.2 |
| Design Arena: agents building Android apps | 1184 | 1169 | GLM 5.2 |
| Design Arena: agents building Godot games | 1142 | 1173 | GPT-5.5 |
| Design Arena: agents building games | 1206 | 1179 | GLM 5.2 |
| Design Arena: HTML slides | 1175 | 1067 | GLM 5.2 |
| Design Arena: PowerPoint slides | 1195 | 1108 | GLM 5.2 |
| LiveBench research | Not measured | 61.6% | Only GPT-5.5 measured |
| LiveBench browser | Not measured | 48.0% | Only GPT-5.5 measured |
| LiveBench long agentic | Not measured | 44.6% | Only GPT-5.5 measured |
| SREBench (Vals AI) | 0.0% | 3.8% | GPT-5.5 |
| Finance Agent (Vals AI) | 49.7% | 51.8% | GPT-5.5 |
| Tax Agent Bench (Vals AI) | Not measured | 21.8% | Only GPT-5.5 measured |
| Legal Research Bench (Vals AI) | 31.3% | 40.4% | GPT-5.5 |
| Excel Modeling Benchmark (Vals AI) | 61.5% | 64.5% | GPT-5.5 |
| Time Horizon Index (Vals AI) | Not measured | 8.3% | Only GPT-5.5 measured |
| τ²-Bench Telecom | 99.1% | 93.9% | GLM 5.2 |
| APEX-Agents | 45.2% | 55.1% | GPT-5.5 |
| Remote Labor Index | Not measured | 6.3% | Only GPT-5.5 measured |
| OSWorld 2.0 | Not measured | 13.0% | Only GPT-5.5 measured |
| DeepResearch Bench | Not measured | 54.0% | Only GPT-5.5 measured |
| PostTrainBench | 31.7% | 27.2% | GLM 5.2 |
| Vending-Bench 2 | $8,314 | $7,524 | GLM 5.2 |
| Neither model has a result on the other 14 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index112
| Benchmark | GLM 5.2 | GPT-5.5 | Leader |
|---|---|---|---|
| GPQA Diamond | 85.6% | 93.2% | GPT-5.5 |
| LiveBench reasoning | Not measured | 89.7% | Only GPT-5.5 measured |
| MMMU Pro (Vals AI) | Not measured | 88.3% | Only GPT-5.5 measured |
| GPQA Diamond (Artificial Analysis run) | 89.5% | 93.5% | GPT-5.5 |
| Humanity's Last Exam (Artificial Analysis run) | 41.1% | 45.8% | GPT-5.5 |
| GPQA Diamond (Epoch AI run) | 91.9% | 90.7% | GLM 5.2 |
| ARC-AGI | 77.0% | 95.0% | GPT-5.5 |
| ARC-AGI-2 | 22.8% | 85.0% | GPT-5.5 |
| SimpleBench | 58.8% | 69.0% | GPT-5.5 |
| Chess puzzles | 22.0% | 63.0% | GPT-5.5 |
| Mystery game puzzles | 19.0% | 56.0% | GPT-5.5 |
| EBR-bench | 9.5% | 34.3% | GPT-5.5 |
| LMArena Text: hard prompts | 1481 | 1489 | GPT-5.5 |
| LMArena Text: expert | 1487 | 1507 | GPT-5.5 |
| LMArena Text: hard prompts in English | 1481 | 1483 | GPT-5.5 |
| LMArena Vision: diagrams | Not measured | 1325 | Only GPT-5.5 measured |
| LMArena Vision: homework | Not measured | 1332 | Only GPT-5.5 measured |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index15
| Benchmark | GLM 5.2 | GPT-5.5 | Leader |
|---|---|---|---|
| OTIS Mock AIME | 86.4% | 84.4% | GLM 5.2 |
| FrontierMath, tiers 1 to 3 | 59.2% | 85.3% | GPT-5.5 |
| FrontierMath, tier 4 | 29.3% | 72.5% | GPT-5.5 |
| ProofBench | 35.0% | 50.0% | GPT-5.5 |
| LMArena Text: math | 1483 | 1487 | GPT-5.5 |
| LMArena Text: mathematical work | 1489 | 1496 | GPT-5.5 |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index28
| Benchmark | GLM 5.2 | GPT-5.5 | Leader |
|---|---|---|---|
| MMLU Pro | 86.7% | 88.1% | GPT-5.5 |
| LegalBench (Vals AI) | 84.1% | 86.5% | GPT-5.5 |
| CaseLaw (Vals AI) | Not measured | 66.2% | Only GPT-5.5 measured |
| CorpFin (Vals AI) | 66.1% | 68.4% | GPT-5.5 |
| TaxEval (Vals AI) | 73.3% | 75.0% | GPT-5.5 |
| MortgageTax (Vals AI) | Not measured | 68.8% | Only GPT-5.5 measured |
| MedCode (Vals AI) | 40.8% | 49.1% | GPT-5.5 |
| SimpleQA Verified | 34.2% | 63.0% | GPT-5.5 |
| LMArena Text: science | 1489 | 1485 | GLM 5.2 |
| LMArena Text: medicine and healthcare | 1475 | 1465 | GLM 5.2 |
| LMArena Text: legal and government | 1486 | 1491 | GPT-5.5 |
| LMArena Text: business and finance | 1451 | 1478 | GPT-5.5 |
| Neither model has a result on the other 5 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index53
| Benchmark | GLM 5.2 | GPT-5.5 | Leader |
|---|---|---|---|
| Design Arena: websites | 1285 | 1256 | GLM 5.2 |
| Design Arena: UI components | 1296 | 1260 | GLM 5.2 |
| Design Arena: game dev | 1268 | 1294 | GPT-5.5 |
| Design Arena: data viz | 1285 | 1265 | GLM 5.2 |
| Design Arena: 3D | 1288 | 1198 | GLM 5.2 |
| Design Arena: all code categories | 1288 | 1260 | GLM 5.2 |
| Design Arena: SVG | 1214 | 1234 | GPT-5.5 |
| Design Arena: ASCII art | 1190 | 1254 | GPT-5.5 |
| LiveBench design | Not measured | 70.3% | Only GPT-5.5 measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index419
| Benchmark | GLM 5.2 | GPT-5.5 | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 81.6% | Only GPT-5.5 measured |
| Public Benefits Bench (Vals AI) | Not measured | 60.9% | Only GPT-5.5 measured |
| SAGE (Vals AI) | Not measured | 51.5% | Only GPT-5.5 measured |
| MedScribe (Vals AI) | 83.5% | 86.9% | GPT-5.5 |
| Vals Multimodal Index | Not measured | 68.1% | Only GPT-5.5 measured |
| IFBench | 73.3% | 75.9% | GPT-5.5 |
| Long context reasoning (AA-LCR) | 78.3% | 84.3% | GPT-5.5 |
| DTBench | 93.6% | 96.0% | GPT-5.5 |
| LMCA | 45.8% | 54.3% | GPT-5.5 |
| Surface Evolver Bench | 55.6% | 88.1% | GPT-5.5 |
| Furniture assembly | Not measured | 44.2% | Only GPT-5.5 measured |
| ExploitBench | Not measured | 47.4% | Only GPT-5.5 measured |
| CL-bench Life | Not measured | 22.2% | Only GPT-5.5 measured |
| LMArena Text | 1470 | 1471 | GPT-5.5 |
| LMArena Text: instruction following | 1466 | 1478 | GPT-5.5 |
| LMArena Text: creative writing | 1463 | 1455 | GLM 5.2 |
| LMArena Vision | Not measured | 1297 | Only GPT-5.5 measured |
| LMArena Text: English | 1475 | 1470 | GLM 5.2 |
| LMArena Text: non-English | 1459 | 1467 | GPT-5.5 |
| LMArena Text: Chinese | 1522 | 1534 | GPT-5.5 |
| LMArena Text: German | 1470 | 1480 | GPT-5.5 |
| LMArena Text: Japanese | 1450 | 1499 | GPT-5.5 |
| LMArena Text: Korean | 1440 | 1460 | GPT-5.5 |
| LMArena Text: French | 1479 | 1485 | GPT-5.5 |
| LMArena Text: Spanish | 1478 | 1468 | GLM 5.2 |
| LMArena Text: Russian | 1468 | 1473 | GPT-5.5 |
| LMArena Text: Polish | 1456 | 1465 | GPT-5.5 |
| LMArena Text: multi-turn | 1469 | 1476 | GPT-5.5 |
| LMArena Text: longer queries | 1480 | 1484 | GPT-5.5 |
| LMArena Text: writing and language | 1463 | 1466 | GPT-5.5 |
| LMArena Text: entertainment and media | 1450 | 1449 | GLM 5.2 |
| LMArena Vision: English | Not measured | 1292 | Only GPT-5.5 measured |
| LMArena Vision: Chinese | Not measured | 1360 | Only GPT-5.5 measured |
| LMArena Vision: reading text | Not measured | 1306 | Only GPT-5.5 measured |
| LMArena Vision: humor | Not measured | 1293 | Only GPT-5.5 measured |
| LMArena Vision: recognizing things | Not measured | 1231 | Only GPT-5.5 measured |
| LMArena Vision: creative writing | Not measured | 1301 | Only GPT-5.5 measured |
| LMArena Document | Not measured | 1486 | Only GPT-5.5 measured |
| Neither model has a result on the other 8 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.2 | GPT-5.5 | 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 5.2 costs less
Repository review
50K input + 3K output tokens
GLM 5.2 costs less
Agent loop
200K input + 10K output tokens
GLM 5.2 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 5.2 | OpenAI GPT-5.5 |
|---|---|---|
| Maker | Z.ai | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | June 16, 2026 | April 24, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $0.0341 | $5.00 |
| Output $/Mtok | $7.00 | $30.00 |
| Coding index | 57.7 | 67.8 |
| Independent measurements behind it | 1 | 2 |
| Cost per solved task | $0.86 | $1.65 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.2 better than GPT-5.5 for coding?
Pick GPT-5.5 for coding: it scores 67.8 on the Vibe Coding Index against 57.7 for GLM 5.2. Pick GLM 5.2 when output tokens cost 4.3x less, or when a solved task costs $0.86 against $1.65.
Which is cheaper, GLM 5.2 or GPT-5.5?
GLM 5.2 costs $0.0341 per million input tokens and $7.00 per million output tokens; GPT-5.5 costs $5.00 and $30.00. Per solved SWE-bench Verified task, GLM 5.2 costs $0.86 and GPT-5.5 $1.65.
Which has the bigger context window, GLM 5.2 or GPT-5.5?
GPT-5.5, with 1.05M tokens against 1.05M.
How many benchmarks is the GLM 5.2 vs GPT-5.5 comparison based on?
97 benchmarks have a result for both models as of October 21, 2026. GLM 5.2's coding index rests on 1 independent benchmark and GPT-5.5's on 2. Where only one model has a result, the page says so instead of naming a winner.