GLM 4.6 vs GPT-4o (2024-11-20)
Updated October 21, 2026. 9 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 4.6 for coding: it scores 52.2 on the Vibe Coding Index against 18.2 for GPT-4o (2024-11-20).
Pick GPT-4o (2024-11-20) when what matters is MGSM (Vals AI) or LegalBench (Vals AI), where it leads.
- Shared results
- 9
- GLM 4.6 only
- 57
- GPT-4o (2024-11-20) only
- 15
- Like-for-like categories
- 5 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 4.6
GLM 4.6 scores 52.2 against 18.2.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
GLM 4.6
GLM 4.6 leads, 18.5 against 8.4.
Same board, both measured
Reasoning
GPQA Diamond
GLM 4.6
GLM 4.6 leads, 74.5% against 53.8%.
Same board, both measured
Math
AIME
GLM 4.6
GLM 4.6 leads, 92.7% against 11.9%.
Same board, both measured
Knowledge
MMLU Pro
GLM 4.6
GLM 4.6 leads, 82.2% against 72.6%.
Same board, both measured
Web design
Design Arena: websites
No call
Only GLM 4.6 has a Design Arena: websites result, 1174.
Directional only
Long documents
Listed context window
GLM 4.6
GLM 4.6 takes 205K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.6
GLM 4.6 $0.0016, GPT-4o (2024-11-20) $0.0075 per turn.
List prices
At a glance
- Coding index
- GLM 4.6 52.2 from 2 benchmarks, GPT-4o (2024-11-20) 18.2 from 1 benchmark.
- Widest category
- Math, 40.7 points apart on average over 2 shared benchmarks.
- Widest single gap
- AIME: GLM 4.6 92.7% against GPT-4o (2024-11-20) 11.9%, 80.8 points apart on a 0 to 100 scale.
- Output price
- GLM 4.6 $2.20 against GPT-4o (2024-11-20) $10.00 per million output tokens, 4.5x apart.
- Context window
- GLM 4.6 takes 205K 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 4.6 | Bars | GPT-4o (2024-11-20) | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 1 | 0 | ||
| Math | 1 | 1 | ||
| Knowledge | 2 | 2 | ||
| All shared boards | 6 | 3 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index10
| Benchmark | GLM 4.6 | GPT-4o (2024-11-20) | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 18.2% | Only GPT-4o (2024-11-20) measured |
| SWE-bench Verified | 68.2% | Not measured | Only GLM 4.6 measured |
| LMArena WebDev | 1341 | Not measured | Only GLM 4.6 measured |
| AA Coding Index | 45.8 | Not measured | Only GLM 4.6 measured |
| LiveCodeBench (Vals AI) | 81.0% | 43.4% | GLM 4.6 |
| Vibe Code Bench (Vals AI) | 3.1% | Not measured | Only GLM 4.6 measured |
| LiveCodeBench (Artificial Analysis run) | 69.5% | Not measured | Only GLM 4.6 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 28.8% | Not measured | Only GLM 4.6 measured |
| Terminal-Bench | 24.5% | Not measured | Only GLM 4.6 measured |
| GSO-Bench | Not measured | 0.0% | Only GPT-4o (2024-11-20) measured |
| WeirdML | Not measured | 25.1% | Only GPT-4o (2024-11-20) measured |
| Cybench | Not measured | 12.5% | Only GPT-4o (2024-11-20) measured |
| ALE-Bench | 340.8 | Not measured | Only GLM 4.6 measured |
| LMArena Text: coding | 1449 | Not measured | Only GLM 4.6 measured |
| LMArena Text: software and IT | 1455 | Not measured | Only GLM 4.6 measured |
| LMArena WebDev: HTML | 1350 | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 22 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.6 | GPT-4o (2024-11-20) | Leader |
|---|---|---|---|
| AA Intelligence Index | 18.5 | 8.4 | GLM 4.6 |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GLM 4.6 | GPT-4o (2024-11-20) | Leader |
|---|---|---|---|
| Design Arena: agents building full-stack apps | 1029 | Not measured | Only GLM 4.6 measured |
| Design Arena: agents building mobile apps | 1096 | Not measured | Only GLM 4.6 measured |
| Design Arena: agents building Android apps | 1090 | Not measured | Only GLM 4.6 measured |
| Design Arena: agents building Godot games | 1180 | Not measured | Only GLM 4.6 measured |
| τ²-Bench Telecom | 76.9% | Not measured | Only GLM 4.6 measured |
| TheAgentCompany | Not measured | 8.6% | Only GPT-4o (2024-11-20) measured |
| Neither model has a result on the other 33 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.6 | GPT-4o (2024-11-20) | Leader |
|---|---|---|---|
| GPQA Diamond | 74.5% | 53.8% | GLM 4.6 |
| MMMU Pro (Vals AI) | Not measured | 62.2% | Only GPT-4o (2024-11-20) measured |
| GPQA Diamond (Artificial Analysis run) | 78.0% | Not measured | Only GLM 4.6 measured |
| Humanity's Last Exam (Artificial Analysis run) | 14.5% | Not measured | Only GLM 4.6 measured |
| GPQA Diamond (Epoch AI run) | Not measured | 47.9% | Only GPT-4o (2024-11-20) measured |
| VPCT | Not measured | 40.0% | Only GPT-4o (2024-11-20) measured |
| LMArena Text: hard prompts | 1440 | Not measured | Only GLM 4.6 measured |
| LMArena Text: expert | 1432 | Not measured | Only GLM 4.6 measured |
| LMArena Text: hard prompts in English | 1445 | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 12 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GLM 4.6 | GPT-4o (2024-11-20) | Leader |
|---|---|---|---|
| AIME | 92.7% | 11.9% | GLM 4.6 |
| MATH 500 (Vals AI) | Not measured | 74.0% | Only GPT-4o (2024-11-20) measured |
| MGSM (Vals AI) | 89.8% | 90.4% | GPT-4o (2024-11-20) |
| AA Math Index | 86.0 | Not measured | Only GLM 4.6 measured |
| AIME 2025 (Artificial Analysis run) | 86.0% | Not measured | Only GLM 4.6 measured |
| OTIS Mock AIME | Not measured | 6.3% | Only GPT-4o (2024-11-20) measured |
| MATH level 5 | Not measured | 49.8% | Only GPT-4o (2024-11-20) measured |
| LMArena Text: math | 1432 | Not measured | Only GLM 4.6 measured |
| LMArena Text: mathematical work | 1447 | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 4 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index22
| Benchmark | GLM 4.6 | GPT-4o (2024-11-20) | Leader |
|---|---|---|---|
| MMLU Pro | 82.2% | 72.6% | GLM 4.6 |
| LegalBench (Vals AI) | 79.6% | 82.2% | GPT-4o (2024-11-20) |
| CaseLaw (Vals AI) | Not measured | 59.7% | Only GPT-4o (2024-11-20) measured |
| CorpFin (Vals AI) | 56.8% | 45.9% | GLM 4.6 |
| TaxEval (Vals AI) | 66.2% | 74.5% | GPT-4o (2024-11-20) |
| MortgageTax (Vals AI) | Not measured | 57.4% | Only GPT-4o (2024-11-20) measured |
| MedQA (Vals AI) | 92.2% | Not measured | Only GLM 4.6 measured |
| MMLU Pro (Artificial Analysis run) | 82.9% | Not measured | Only GLM 4.6 measured |
| GeoBench | Not measured | 71.0% | Only GPT-4o (2024-11-20) measured |
| LMArena Text: science | 1456 | Not measured | Only GLM 4.6 measured |
| LMArena Text: medicine and healthcare | 1455 | Not measured | Only GLM 4.6 measured |
| LMArena Text: legal and government | 1448 | Not measured | Only GLM 4.6 measured |
| LMArena Text: business and finance | 1416 | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 4 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GLM 4.6 | GPT-4o (2024-11-20) | Leader |
|---|---|---|---|
| Design Arena: websites | 1174 | Not measured | Only GLM 4.6 measured |
| Design Arena: UI components | 1147 | Not measured | Only GLM 4.6 measured |
| Design Arena: game dev | 1151 | Not measured | Only GLM 4.6 measured |
| Design Arena: data viz | 1161 | Not measured | Only GLM 4.6 measured |
| Design Arena: 3D | 1129 | Not measured | Only GLM 4.6 measured |
| Design Arena: all code categories | 1166 | Not measured | Only GLM 4.6 measured |
| Design Arena: SVG | 1108 | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index20 boards, none shared
| Benchmark | GLM 4.6 | GPT-4o (2024-11-20) | Leader |
|---|---|---|---|
| IFBench | 43.4% | Not measured | Only GLM 4.6 measured |
| Long context reasoning (AA-LCR) | 54.0% | Not measured | Only GLM 4.6 measured |
| Creative writing (Lech Mazur) | Not measured | 81.8% | Only GPT-4o (2024-11-20) measured |
| LMArena Text | 1440 | Not measured | Only GLM 4.6 measured |
| LMArena Text: instruction following | 1410 | Not measured | Only GLM 4.6 measured |
| LMArena Text: creative writing | 1412 | Not measured | Only GLM 4.6 measured |
| LMArena Text: English | 1449 | Not measured | Only GLM 4.6 measured |
| LMArena Text: non-English | 1426 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Chinese | 1499 | Not measured | Only GLM 4.6 measured |
| LMArena Text: German | 1447 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Japanese | 1393 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Korean | 1400 | Not measured | Only GLM 4.6 measured |
| LMArena Text: French | 1459 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Spanish | 1436 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Russian | 1418 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Polish | 1447 | Not measured | Only GLM 4.6 measured |
| LMArena Text: multi-turn | 1427 | Not measured | Only GLM 4.6 measured |
| LMArena Text: longer queries | 1422 | Not measured | Only GLM 4.6 measured |
| LMArena Text: writing and language | 1415 | Not measured | Only GLM 4.6 measured |
| LMArena Text: entertainment and media | 1409 | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 26 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.6 | 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 4.6 costs less
Repository review
50K input + 3K output tokens
GLM 4.6 costs less
Agent loop
200K input + 10K output tokens
GLM 4.6 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 4.6 | OpenAI GPT-4o (2024-11-20) |
|---|---|---|
| Maker | Z.ai | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | September 30, 2025 | November 20, 2024 |
| Context window | 205K | 128K |
| Input $/Mtok | $0.55 | $2.50 |
| Output $/Mtok | $2.20 | $10.00 |
| Coding index | 52.2 | 18.2 |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.6 better than GPT-4o (2024-11-20) for coding?
Pick GLM 4.6 for coding: it scores 52.2 on the Vibe Coding Index against 18.2 for GPT-4o (2024-11-20). Pick GPT-4o (2024-11-20) when what matters is MGSM (Vals AI) or LegalBench (Vals AI), where it leads.
Which is cheaper, GLM 4.6 or GPT-4o (2024-11-20)?
GLM 4.6 costs $0.55 per million input tokens and $2.20 per million output tokens; GPT-4o (2024-11-20) costs $2.50 and $10.00. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.6 or GPT-4o (2024-11-20)?
GLM 4.6, with 205K tokens against 128K.
How many benchmarks is the GLM 4.6 vs GPT-4o (2024-11-20) comparison based on?
9 benchmarks have a result for both models as of October 21, 2026. GLM 4.6's coding index rests on 2 independent benchmarks and GPT-4o (2024-11-20)'s on 1. Where only one model has a result, the page says so instead of naming a winner.