GLM 4.6V vs GPT-4o (2024-08-06)
GLM 4.6V leads GPT-4o (2024-08-06) on the Artificial Analysis Intelligence Index, 11.2 to 7.7, and GLM 4.6V costs 11.1x less per output token ($0.90 vs $10.00 per million). Updated October 22, 2026; 31 benchmarks have a result for both.
GLM 4.6V or GPT-4o (2024-08-06): which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GLM 4.6V for output tokens 11.1x cheaper ($0.90 vs $10.00 per million) and a larger context window (131K against 128K tokens).
- Shared results
- 31
- GLM 4.6V only
- 7
- GPT-4o (2024-08-06) only
- 27
- Like-for-like categories
- 6 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
GLM 4.6V
GLM 4.6V leads, 11.2 against 7.7.
Same board, both measured
Math
AIME
No call
Only GPT-4o (2024-08-06) has a AIME result, 14.0%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-4o (2024-08-06) has a MMLU Pro result, 74.1%.
Directional only
Long documents
Listed context window
GLM 4.6V
GLM 4.6V takes 131K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.6V
GLM 4.6V $0.0008, GPT-4o (2024-08-06) $0.0075 per turn.
List prices
At a glance
- Coding index
- GLM 4.6V not benchmarked, GPT-4o (2024-08-06) not benchmarked.
- Widest category
- Knowledge, 20.7 points apart on average over 4 shared benchmarks.
- Widest single gap
- LMArena Vision: Chinese: GLM 4.6V 1217 against GPT-4o (2024-08-06) 1009, 34.6 points apart on a 0 to 100 scale.
- Output price
- GLM 4.6V $0.90 against GPT-4o (2024-08-06) $10.00 per million output tokens, 11.1x apart.
- Context window
- GLM 4.6V takes 131K tokens, GPT-4o (2024-08-06) 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 4.6V | Bars | GPT-4o (2024-08-06) | Ties |
|---|---|---|---|---|
| Coding | 4 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 5 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 15 | 1 | ||
| All shared boards | 30 | 1 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
| Benchmark | GLM 4.6V | GPT-4o (2024-08-06) | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 23.1% | Only GPT-4o (2024-08-06) measured |
| SWE-bench Lite | Not measured | 21.7% | Only GPT-4o (2024-08-06) measured |
| LiveCodeBench (Artificial Analysis run) | 41.1% | 31.7% | GLM 4.6V |
| Terminal-Bench Hard (Artificial Analysis run) | 14.4% | 8.3% | GLM 4.6V |
| LMArena Text: coding | 1389 | 1283 | GLM 4.6V |
| LMArena Text: software and IT | 1391 | 1278 | GLM 4.6V |
| Neither model has a result on the other 33 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.6V | GPT-4o (2024-08-06) | Leader |
|---|---|---|---|
| AA Intelligence Index | 11.2 | 7.7 | GLM 4.6V |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.6V | GPT-4o (2024-08-06) | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 31.6% | 28.9% | GLM 4.6V |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GLM 4.6V | GPT-4o (2024-08-06) | Leader |
|---|---|---|---|
| MMMU Pro (Vals AI) | Not measured | 64.0% | Only GPT-4o (2024-08-06) measured |
| GPQA Diamond (Artificial Analysis run) | 71.9% | 52.1% | GLM 4.6V |
| Humanity's Last Exam (Artificial Analysis run) | 9.6% | 2.3% | GLM 4.6V |
| GPQA Diamond (Epoch AI run) | Not measured | 49.2% | Only GPT-4o (2024-08-06) measured |
| SimpleBench | Not measured | 17.8% | Only GPT-4o (2024-08-06) measured |
| Chess puzzles | Not measured | 6.0% | Only GPT-4o (2024-08-06) measured |
| LMArena Text: hard prompts | 1367 | 1264 | GLM 4.6V |
| LMArena Text: expert | 1371 | 1241 | GLM 4.6V |
| LMArena Text: hard prompts in English | 1391 | 1277 | GLM 4.6V |
| LMArena Vision: diagrams | 1182 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: homework | 1155 | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 11 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | GLM 4.6V | GPT-4o (2024-08-06) | Leader |
|---|---|---|---|
| AIME | Not measured | 14.0% | Only GPT-4o (2024-08-06) measured |
| MATH 500 (Vals AI) | Not measured | 75.2% | Only GPT-4o (2024-08-06) measured |
| MGSM (Vals AI) | Not measured | 90.7% | Only GPT-4o (2024-08-06) measured |
| AA Math Index | 85.3 | Not measured | Only GLM 4.6V measured |
| AIME 2025 (Artificial Analysis run) | 85.3% | Not measured | Only GLM 4.6V measured |
| OTIS Mock AIME | Not measured | 6.4% | Only GPT-4o (2024-08-06) measured |
| FrontierMath, tiers 1 to 3 | Not measured | 0.4% | Only GPT-4o (2024-08-06) measured |
| MATH level 5 | Not measured | 53.3% | Only GPT-4o (2024-08-06) measured |
| LMArena Text: math | Not measured | 1285 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: mathematical work | Not measured | 1283 | Only GPT-4o (2024-08-06) measured |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GLM 4.6V | GPT-4o (2024-08-06) | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 74.1% | Only GPT-4o (2024-08-06) measured |
| LegalBench (Vals AI) | Not measured | 80.1% | Only GPT-4o (2024-08-06) measured |
| CorpFin (Vals AI) | Not measured | 39.4% | Only GPT-4o (2024-08-06) measured |
| TaxEval (Vals AI) | Not measured | 71.1% | Only GPT-4o (2024-08-06) measured |
| MortgageTax (Vals AI) | Not measured | 61.0% | Only GPT-4o (2024-08-06) measured |
| MedQA (Vals AI) | Not measured | 88.2% | Only GPT-4o (2024-08-06) measured |
| MMLU Pro (Artificial Analysis run) | 79.9% | Not measured | Only GLM 4.6V measured |
| SimpleQA Verified | Not measured | 26.0% | Only GPT-4o (2024-08-06) measured |
| MMLU | Not measured | 84.3% | Only GPT-4o (2024-08-06) measured |
| LMArena Text: science | 1389 | 1274 | GLM 4.6V |
| LMArena Text: medicine and healthcare | 1389 | 1252 | GLM 4.6V |
| LMArena Text: legal and government | 1393 | 1275 | GLM 4.6V |
| LMArena Text: business and finance | 1359 | 1232 | GLM 4.6V |
| Neither model has a result on the other 5 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.6V | GPT-4o (2024-08-06) | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index151
| Benchmark | GLM 4.6V | GPT-4o (2024-08-06) | Leader |
|---|---|---|---|
| IFBench | 30.1% | 36.0% | GPT-4o (2024-08-06) |
| Long context reasoning (AA-LCR) | 48.7% | 41.0% | GLM 4.6V |
| LMArena Text | 1376 | 1283 | GLM 4.6V |
| LMArena Text: instruction following | 1350 | 1267 | GLM 4.6V |
| LMArena Text: creative writing | 1347 | 1274 | GLM 4.6V |
| LMArena Vision | 1164 | 1065 | GLM 4.6V |
| LMArena Text: English | 1395 | 1297 | GLM 4.6V |
| LMArena Text: non-English | 1358 | 1262 | GLM 4.6V |
| LMArena Text: Chinese | 1425 | 1254 | GLM 4.6V |
| LMArena Text: German | Not measured | 1257 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: Japanese | Not measured | 1234 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: Korean | Not measured | 1218 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: French | Not measured | 1263 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: Spanish | Not measured | 1269 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: Russian | 1339 | 1272 | GLM 4.6V |
| LMArena Text: multi-turn | 1359 | 1279 | GLM 4.6V |
| LMArena Text: longer queries | 1357 | 1283 | GLM 4.6V |
| LMArena Text: writing and language | 1347 | 1290 | GLM 4.6V |
| LMArena Text: entertainment and media | 1343 | 1267 | GLM 4.6V |
| LMArena Vision: English | 1179 | 1072 | GLM 4.6V |
| LMArena Vision: Chinese | 1217 | 1009 | GLM 4.6V |
| LMArena Vision: reading text | 1171 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: creative writing | 1113 | Not measured | Only GLM 4.6V 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 4.6V | GPT-4o (2024-08-06) | 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 4.6V costs less
Repository review
50K input + 3K output tokens
GLM 4.6V costs less
Agent loop
200K input + 10K output tokens
GLM 4.6V 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 4.6V | OpenAI GPT-4o (2024-08-06) |
|---|---|---|
| Maker | Z.ai | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | December 8, 2025 | August 6, 2024 |
| Context window | 131K | 128K |
| Input $/Mtok | $0.30 | $2.50 |
| Output $/Mtok | $0.90 | $10.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.6V better than GPT-4o (2024-08-06) for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 4.6V for output tokens 11.1x cheaper ($0.90 vs $10.00 per million) and a larger context window (131K against 128K tokens).
Which is cheaper, GLM 4.6V or GPT-4o (2024-08-06)?
GLM 4.6V costs $0.30 per million input tokens and $0.90 per million output tokens; GPT-4o (2024-08-06) costs $2.50 and $10.00. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.6V or GPT-4o (2024-08-06)?
GLM 4.6V, with 131K tokens against 128K.
How many benchmarks is the GLM 4.6V vs GPT-4o (2024-08-06) comparison based on?
31 benchmarks have a result for both models as of October 22, 2026. GLM 4.6V's coding index rests on 0 independent benchmarks and GPT-4o (2024-08-06)'s on 0. Where only one model has a result, the page says so instead of naming a winner.