GLM 5.2 vs GPT-4o (2024-11-20)
GLM 5.2 leads GPT-4o (2024-11-20) on the Artificial Analysis Intelligence Index, 33.7 to 8.4, and GLM 5.2 costs 2.3x less per output token ($4.40 vs $10.00 per million). Updated October 22, 2026; 10 benchmarks have a result for both.
GLM 5.2 or GPT-4o (2024-11-20): which to pick
Only GLM 5.2 has a coding score, 61.1 on the Vibe Coding Index; GPT-4o (2024-11-20) has none, so the two cannot be ranked on coding.
Pick GPT-4o (2024-11-20) for work measured by TaxEval (Vals AI), where it leads.
- Shared results
- 10
- GLM 5.2 only
- 94
- GPT-4o (2024-11-20) only
- 14
- Like-for-like categories
- 5 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
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, six public coding benchmarks
No call
Only GLM 5.2 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GLM 5.2
GLM 5.2 leads, 33.7 against 8.4.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GLM 5.2 has a AA Agentic Index result, 38.4.
Directional only
Reasoning
GPQA Diamond
GLM 5.2
GLM 5.2 leads, 85.6% against 53.8%.
Same board, both measured
Math
AIME
No call
Only GPT-4o (2024-11-20) has a AIME result, 11.9%.
Directional only
Knowledge
MMLU Pro
GLM 5.2
GLM 5.2 leads, 86.7% against 72.6%.
Same board, both measured
Web design
Design Arena: websites
No call
Only GLM 5.2 has a Design Arena: websites result, 1285.
Directional only
Long documents
Listed context window
GLM 5.2
GLM 5.2 takes 1.05M tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5.2
GLM 5.2 $0.0036, GPT-4o (2024-11-20) $0.0075 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 5.2 has the number, $0.86.
Directional only
At a glance
- Coding index
- GLM 5.2 61.1 from 4 benchmarks, GPT-4o (2024-11-20) not benchmarked.
- Widest category
- Math, 80.1 points apart on average over 1 shared benchmark.
- Widest single gap
- OTIS Mock AIME: GLM 5.2 86.4% against GPT-4o (2024-11-20) 6.3%, 80.1 points apart on a 0 to 100 scale.
- Output price
- GLM 5.2 $4.40 against GPT-4o (2024-11-20) $10.00 per million output tokens, 2.3x apart.
- Context window
- GLM 5.2 takes 1.05M tokens, GPT-4o (2024-11-20) 128K.
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-4o (2024-11-20) | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| Math | 1 | 0 | ||
| Knowledge | 3 | 1 | ||
| All shared boards | 9 | 1 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | GLM 5.2 | GPT-4o (2024-11-20) | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 18.2% | Only GPT-4o (2024-11-20) measured |
| LMArena WebDev | 1604 | Not measured | Only GLM 5.2 measured |
| AA Coding Index | 68.8 | Not measured | Only GLM 5.2 measured |
| LiveCodeBench (Vals AI) | 69.5% | 43.4% | GLM 5.2 |
| SWE-bench Verified (Vals AI) | 82.8% | Not measured | Only GLM 5.2 measured |
| Vibe Code Bench (Vals AI) | 64.0% | Not measured | Only GLM 5.2 measured |
| Code migration (Vals AI) | 37.9% | Not measured | Only GLM 5.2 measured |
| ProgramBench (Vals AI) | 0.5% | Not measured | Only GLM 5.2 measured |
| Terminal-Bench 2.1 (Vals AI) | 67.8% | Not measured | Only GLM 5.2 measured |
| SkillsBench (Vals AI) | 45.1% | Not measured | Only GLM 5.2 measured |
| SciCode | 51.2% | Not measured | Only GLM 5.2 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 50.8% | Not measured | Only GLM 5.2 measured |
| FrontierCode | 24.5% | Not measured | Only GLM 5.2 measured |
| GSO-Bench | Not measured | 0.0% | Only GPT-4o (2024-11-20) measured |
| WeirdML | 70.1% | 25.1% | GLM 5.2 |
| Cybench | Not measured | 12.5% | Only GPT-4o (2024-11-20) measured |
| ALE-Bench | 1047.0 | Not measured | Only GLM 5.2 measured |
| LMArena Text: coding | 1487 | Not measured | Only GLM 5.2 measured |
| LMArena Text: software and IT | 1486 | Not measured | Only GLM 5.2 measured |
| LMArena WebDev: React | 1617 | Not measured | Only GLM 5.2 measured |
| LMArena WebDev: HTML | 1565 | Not measured | Only GLM 5.2 measured |
| SWE Atlas: Codebase QnA | 48.1% | Not measured | Only GLM 5.2 measured |
| SWE Atlas: Refactoring | 42.4% | Not measured | Only GLM 5.2 measured |
| SWE Atlas: Test Writing | 41.5% | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 19 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 5.2 | GPT-4o (2024-11-20) | Leader |
|---|---|---|---|
| AA Intelligence Index | 33.7 | 8.4 | GLM 5.2 |
| Epoch Capabilities Index | 151.9 | Not measured | Only GLM 5.2 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index19 boards, none shared
| Benchmark | GLM 5.2 | GPT-4o (2024-11-20) | Leader |
|---|---|---|---|
| AA Agentic Index | 38.4 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building web apps | 1222 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building full-stack apps | 1241 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building mobile apps | 1159 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building Android apps | 1177 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building Godot games | 1142 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building games | 1206 | Not measured | Only GLM 5.2 measured |
| Design Arena: HTML slides | 1114 | Not measured | Only GLM 5.2 measured |
| Design Arena: PowerPoint slides | 1167 | Not measured | Only GLM 5.2 measured |
| SREBench (Vals AI) | 0.0% | Not measured | Only GLM 5.2 measured |
| Finance Agent (Vals AI) | 49.7% | Not measured | Only GLM 5.2 measured |
| Legal Research Bench (Vals AI) | 31.3% | Not measured | Only GLM 5.2 measured |
| Excel Modeling Benchmark (Vals AI) | 61.5% | Not measured | Only GLM 5.2 measured |
| τ²-Bench Telecom | 99.1% | Not measured | Only GLM 5.2 measured |
| APEX-Agents | 45.2% | Not measured | Only GLM 5.2 measured |
| PostTrainBench | 31.7% | Not measured | Only GLM 5.2 measured |
| TheAgentCompany | Not measured | 8.6% | Only GPT-4o (2024-11-20) measured |
| Vending-Bench 2 | $8,314 | Not measured | Only GLM 5.2 measured |
| MCP Atlas | 77.8% | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 23 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 5.2 | GPT-4o (2024-11-20) | Leader |
|---|---|---|---|
| GPQA Diamond | 85.6% | 53.8% | GLM 5.2 |
| MMMU Pro (Vals AI) | Not measured | 62.2% | Only GPT-4o (2024-11-20) measured |
| GPQA Diamond (Artificial Analysis run) | 89.5% | Not measured | Only GLM 5.2 measured |
| Humanity's Last Exam (Artificial Analysis run) | 41.1% | Not measured | Only GLM 5.2 measured |
| GPQA Diamond (Epoch AI run) | 91.9% | 47.9% | GLM 5.2 |
| ARC-AGI | 77.0% | Not measured | Only GLM 5.2 measured |
| ARC-AGI-2 | 22.8% | Not measured | Only GLM 5.2 measured |
| SimpleBench | 58.8% | Not measured | Only GLM 5.2 measured |
| Chess puzzles | 22.0% | Not measured | Only GLM 5.2 measured |
| Mystery game puzzles | 19.0% | Not measured | Only GLM 5.2 measured |
| EBR-bench | 9.5% | Not measured | Only GLM 5.2 measured |
| VPCT | Not measured | 40.0% | Only GPT-4o (2024-11-20) measured |
| LMArena Text: hard prompts | 1481 | Not measured | Only GLM 5.2 measured |
| LMArena Text: expert | 1487 | Not measured | Only GLM 5.2 measured |
| LMArena Text: hard prompts in English | 1481 | Not measured | Only GLM 5.2 measured |
| Kagi LLM Benchmark | 62.6% | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 7 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 5.2 | GPT-4o (2024-11-20) | Leader |
|---|---|---|---|
| AIME | Not measured | 11.9% | Only GPT-4o (2024-11-20) measured |
| MATH 500 (Vals AI) | Not measured | 74.0% | Only GPT-4o (2024-11-20) measured |
| MGSM (Vals AI) | Not measured | 90.4% | Only GPT-4o (2024-11-20) measured |
| OTIS Mock AIME | 86.4% | 6.3% | GLM 5.2 |
| FrontierMath, tiers 1 to 3 | 59.2% | Not measured | Only GLM 5.2 measured |
| FrontierMath, tier 4 | 29.3% | Not measured | Only GLM 5.2 measured |
| ProofBench | 35.0% | Not measured | Only GLM 5.2 measured |
| MATH level 5 | Not measured | 49.8% | Only GPT-4o (2024-11-20) measured |
| LMArena Text: math | 1483 | Not measured | Only GLM 5.2 measured |
| LMArena Text: mathematical work | 1489 | Not measured | Only GLM 5.2 measured |
| MathArena | 22.7% | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index31
| Benchmark | GLM 5.2 | GPT-4o (2024-11-20) | Leader |
|---|---|---|---|
| MMLU Pro | 86.7% | 72.6% | GLM 5.2 |
| LegalBench (Vals AI) | 84.1% | 82.2% | GLM 5.2 |
| CaseLaw (Vals AI) | Not measured | 59.7% | Only GPT-4o (2024-11-20) measured |
| CorpFin (Vals AI) | 66.1% | 45.9% | GLM 5.2 |
| TaxEval (Vals AI) | 73.3% | 74.5% | GPT-4o (2024-11-20) |
| MortgageTax (Vals AI) | Not measured | 57.4% | Only GPT-4o (2024-11-20) measured |
| MedCode (Vals AI) | 40.8% | Not measured | Only GLM 5.2 measured |
| SimpleQA Verified | 34.2% | Not measured | Only GLM 5.2 measured |
| GeoBench | Not measured | 71.0% | Only GPT-4o (2024-11-20) measured |
| LMArena Text: science | 1489 | Not measured | Only GLM 5.2 measured |
| LMArena Text: medicine and healthcare | 1475 | Not measured | Only GLM 5.2 measured |
| LMArena Text: legal and government | 1486 | Not measured | Only GLM 5.2 measured |
| LMArena Text: business and finance | 1451 | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 7 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GLM 5.2 | GPT-4o (2024-11-20) | Leader |
|---|---|---|---|
| Design Arena: websites | 1285 | Not measured | Only GLM 5.2 measured |
| Design Arena: UI components | 1294 | Not measured | Only GLM 5.2 measured |
| Design Arena: game dev | 1267 | Not measured | Only GLM 5.2 measured |
| Design Arena: data viz | 1286 | Not measured | Only GLM 5.2 measured |
| Design Arena: 3D | 1287 | Not measured | Only GLM 5.2 measured |
| Design Arena: all code categories | 1287 | Not measured | Only GLM 5.2 measured |
| Design Arena: SVG | 1213 | Not measured | Only GLM 5.2 measured |
| Design Arena: ASCII art | 1191 | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 1 board here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index25 boards, none shared
| Benchmark | GLM 5.2 | GPT-4o (2024-11-20) | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | 83.5% | Not measured | Only GLM 5.2 measured |
| IFBench | 73.3% | Not measured | Only GLM 5.2 measured |
| Long context reasoning (AA-LCR) | 78.3% | Not measured | Only GLM 5.2 measured |
| DTBench | 93.6% | Not measured | Only GLM 5.2 measured |
| LMCA | 45.8% | Not measured | Only GLM 5.2 measured |
| Creative writing (Lech Mazur) | Not measured | 81.8% | Only GPT-4o (2024-11-20) measured |
| Surface Evolver Bench | 55.6% | Not measured | Only GLM 5.2 measured |
| LMArena Text | 1470 | Not measured | Only GLM 5.2 measured |
| LMArena Text: instruction following | 1466 | Not measured | Only GLM 5.2 measured |
| LMArena Text: creative writing | 1463 | Not measured | Only GLM 5.2 measured |
| LMArena Text: English | 1475 | Not measured | Only GLM 5.2 measured |
| LMArena Text: non-English | 1459 | Not measured | Only GLM 5.2 measured |
| LMArena Text: Chinese | 1522 | Not measured | Only GLM 5.2 measured |
| LMArena Text: German | 1470 | Not measured | Only GLM 5.2 measured |
| LMArena Text: Japanese | 1450 | Not measured | Only GLM 5.2 measured |
| LMArena Text: Korean | 1440 | Not measured | Only GLM 5.2 measured |
| LMArena Text: French | 1479 | Not measured | Only GLM 5.2 measured |
| LMArena Text: Spanish | 1478 | Not measured | Only GLM 5.2 measured |
| LMArena Text: Russian | 1468 | Not measured | Only GLM 5.2 measured |
| LMArena Text: Polish | 1456 | Not measured | Only GLM 5.2 measured |
| LMArena Text: multi-turn | 1469 | Not measured | Only GLM 5.2 measured |
| LMArena Text: longer queries | 1480 | Not measured | Only GLM 5.2 measured |
| LMArena Text: writing and language | 1463 | Not measured | Only GLM 5.2 measured |
| LMArena Text: entertainment and media | 1450 | Not measured | Only GLM 5.2 measured |
| EQ-Bench 4 | 1222 | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 23 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.2 | GPT-4o (2024-11-20) | 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-4o (2024-11-20) |
|---|---|---|
| Maker | Z.ai | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | June 16, 2026 | November 20, 2024 |
| Context window | 1.05M | 128K |
| Input $/Mtok | $1.40 | $2.50 |
| Output $/Mtok | $4.40 | $10.00 |
| Coding index | 61.1 | Not benchmarked |
| Independent measurements behind it | 4 | 0 |
| Cost per solved task | $0.86 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.2 better than GPT-4o (2024-11-20) for coding?
Only GLM 5.2 has a coding score, 61.1 on the Vibe Coding Index; GPT-4o (2024-11-20) has none, so the two cannot be ranked on coding. Pick GPT-4o (2024-11-20) for work measured by TaxEval (Vals AI), where it leads.
Which is cheaper, GLM 5.2 or GPT-4o (2024-11-20)?
GLM 5.2 costs $1.40 per million input tokens and $4.40 per million output tokens; GPT-4o (2024-11-20) costs $2.50 and $10.00. Cost per solved task is known for GLM 5.2 only.
Which has the bigger context window, GLM 5.2 or GPT-4o (2024-11-20)?
GLM 5.2, with 1.05M tokens against 128K.
How many benchmarks is the GLM 5.2 vs GPT-4o (2024-11-20) comparison based on?
10 benchmarks have a result for both models as of October 22, 2026. GLM 5.2's coding index rests on 4 independent benchmarks and GPT-4o (2024-11-20)'s on 0. Where only one model has a result, the page says so instead of naming a winner.