GLM 4.6 vs o3
o3 leads GLM 4.6 on the Artificial Analysis Intelligence Index, 20.2 to 18.5, and GLM 4.6 costs 3.6x less per output token ($2.20 vs $8.00 per million). Updated October 22, 2026; 58 benchmarks have a result for both.
GLM 4.6 or o3: which to pick
Only GLM 4.6 has a coding score, 8.8 on the Vibe Coding Index; o3 has none, so the two cannot be ranked on coding.
Pick o3 for work measured by LiveCodeBench (Vals AI) or LiveCodeBench (Artificial Analysis run), where it leads.
- Shared results
- 58
- GLM 4.6 only
- 11
- o3 only
- 44
- Like-for-like categories
- 8 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 4.6 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
o3
o3 leads, 20.2 against 18.5.
Same board, both measured
Reasoning
GPQA Diamond
o3
o3 leads, 84.1% against 74.5%.
Same board, both measured
Math
AIME
GLM 4.6
GLM 4.6 leads, 92.7% against 85.3%.
Same board, both measured
Knowledge
MMLU Pro
o3
o3 leads, 85.6% against 82.2%.
Same board, both measured
Web design
Design Arena: websites
GLM 4.6
GLM 4.6 leads, 1174 against 1036.
Same board, both measured
Long documents
Listed context window
GLM 4.6
GLM 4.6 takes 205K tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.6
GLM 4.6 $0.0017, o3 $0.0060 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only o3 has the number, $0.571.
Directional only
At a glance
- Coding index
- GLM 4.6 8.8 from 2 benchmarks, o3 not benchmarked.
- Widest category
- Web design, 14.2 points apart on average over 5 shared benchmarks.
- Widest single gap
- IFBench: GLM 4.6 43.4% against o3 71.4%, 28.0 points apart on a 0 to 100 scale.
- Output price
- GLM 4.6 $2.20 against o3 $8.00 per million output tokens, 3.6x apart.
- Context window
- GLM 4.6 takes 205K tokens, o3 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 4.6 | Bars | o3 | Ties |
|---|---|---|---|---|
| Coding | 3 | 4 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 1 | 2 | ||
| Reasoning | 3 | 4 | ||
| Math | 3 | 3 | ||
| Knowledge | 4 | 6 | ||
| Web design | 4 | 1 | ||
| General | 16 | 3 | ||
| All shared boards | 34 | 24 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index34
| Benchmark | GLM 4.6 | o3 | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 81.3% | Only o3 measured |
| SWE-bench Verified | 68.2% | 58.4% | GLM 4.6 |
| SWE-bench Multimodal | Not measured | 36.0% | Only o3 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% | 83.9% | o3 |
| Vibe Code Bench (Vals AI) | 3.1% | Not measured | Only GLM 4.6 measured |
| LiveCodeBench (Artificial Analysis run) | 69.5% | 80.8% | o3 |
| Terminal-Bench Hard (Artificial Analysis run) | 28.8% | 37.1% | o3 |
| Terminal-Bench | 24.5% | Not measured | Only GLM 4.6 measured |
| GSO-Bench | Not measured | 8.8% | Only o3 measured |
| WeirdML | Not measured | 52.4% | Only o3 measured |
| ALE-Bench | 340.8 | 933.5 | o3 |
| LMArena Text: coding | 1449 | 1407 | GLM 4.6 |
| LMArena Text: software and IT | 1455 | 1415 | GLM 4.6 |
| LMArena WebDev: HTML | 1350 | Not measured | Only GLM 4.6 measured |
| SciCode | Not measured | 9.2% | Only o3 measured |
| Neither model has a result on the other 22 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.6 | o3 | Leader |
|---|---|---|---|
| AA Intelligence Index | 18.5 | 20.2 | o3 |
| Epoch Capabilities Index | Not measured | 146.7 | Only o3 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index12
| Benchmark | GLM 4.6 | o3 | 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 | 1084 | 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% | 80.7% | o3 |
| METR time horizons | Not measured | 65.4% | Only o3 measured |
| GDPval | Not measured | 30.8% | Only o3 measured |
| DeepResearch Bench | Not measured | 45.2% | Only o3 measured |
| OSWorld | Not measured | 23.0% | Only o3 measured |
| Berkeley Function Calling Leaderboard (BFCL) | 72.4% | 63.0% | GLM 4.6 |
| BrowseComp-Plus | 48.2% | 65.9% | o3 |
| Neither model has a result on the other 30 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index34
| Benchmark | GLM 4.6 | o3 | Leader |
|---|---|---|---|
| GPQA Diamond | 74.5% | 84.1% | o3 |
| MMMU Pro (Vals AI) | Not measured | 80.4% | Only o3 measured |
| GPQA Diamond (Artificial Analysis run) | 78.0% | 82.7% | o3 |
| Humanity's Last Exam (Artificial Analysis run) | 14.5% | 20.1% | o3 |
| GPQA Diamond (Epoch AI run) | Not measured | 81.8% | Only o3 measured |
| ARC-AGI | Not measured | 60.8% | Only o3 measured |
| ARC-AGI-2 | Not measured | 6.5% | Only o3 measured |
| SimpleBench | Not measured | 53.1% | Only o3 measured |
| Chess puzzles | Not measured | 37.0% | Only o3 measured |
| Mystery game puzzles | Not measured | 29.0% | Only o3 measured |
| VPCT | Not measured | 52.0% | Only o3 measured |
| LMArena Text: hard prompts | 1440 | 1402 | GLM 4.6 |
| LMArena Text: expert | 1432 | 1402 | GLM 4.6 |
| LMArena Text: hard prompts in English | 1445 | 1411 | GLM 4.6 |
| LMArena Vision: diagrams | Not measured | 1220 | Only o3 measured |
| LMArena Vision: homework | Not measured | 1234 | Only o3 measured |
| Kagi LLM Benchmark | 45.7% | 67.6% | o3 |
| Neither model has a result on the other 5 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index33
| Benchmark | GLM 4.6 | o3 | Leader |
|---|---|---|---|
| AIME | 92.7% | 85.3% | GLM 4.6 |
| MATH 500 (Vals AI) | Not measured | 94.6% | Only o3 measured |
| MGSM (Vals AI) | 89.8% | 91.8% | o3 |
| AA Math Index | 86.0 | 88.3 | o3 |
| AIME 2025 (Artificial Analysis run) | 86.0% | 88.3% | o3 |
| OTIS Mock AIME | Not measured | 84.4% | Only o3 measured |
| FrontierMath, tiers 1 to 3 | Not measured | 33.3% | Only o3 measured |
| MATH level 5 | Not measured | 97.8% | Only o3 measured |
| LMArena Text: math | 1432 | 1426 | GLM 4.6 |
| LMArena Text: mathematical work | 1447 | 1423 | GLM 4.6 |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index46
| Benchmark | GLM 4.6 | o3 | Leader |
|---|---|---|---|
| MMLU Pro | 82.2% | 85.6% | o3 |
| LegalBench (Vals AI) | 79.6% | 83.8% | o3 |
| CorpFin (Vals AI) | 56.8% | 59.7% | o3 |
| TaxEval (Vals AI) | 66.2% | 74.6% | o3 |
| MortgageTax (Vals AI) | Not measured | 65.7% | Only o3 measured |
| MedQA (Vals AI) | 92.2% | 96.1% | o3 |
| MedCode (Vals AI) | Not measured | 47.3% | Only o3 measured |
| MMLU Pro (Artificial Analysis run) | 82.9% | 85.3% | o3 |
| SimpleQA Verified | Not measured | 49.4% | Only o3 measured |
| GeoBench | Not measured | 74.0% | Only o3 measured |
| LMArena Text: science | 1456 | 1429 | GLM 4.6 |
| LMArena Text: medicine and healthcare | 1455 | 1448 | GLM 4.6 |
| LMArena Text: legal and government | 1448 | 1428 | GLM 4.6 |
| LMArena Text: business and finance | 1416 | 1393 | GLM 4.6 |
| FACTS Benchmark Suite | Not measured | 44.4% | Only o3 measured |
| Neither model has a result on the other 3 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index41
| Benchmark | GLM 4.6 | o3 | Leader |
|---|---|---|---|
| Design Arena: websites | 1174 | 1036 | GLM 4.6 |
| Design Arena: UI components | 1145 | 1017 | GLM 4.6 |
| Design Arena: game dev | 1150 | 1034 | GLM 4.6 |
| Design Arena: data viz | 1161 | 1200 | o3 |
| Design Arena: 3D | 1128 | Not measured | Only GLM 4.6 measured |
| Design Arena: all code categories | 1165 | 1020 | GLM 4.6 |
| Design Arena: SVG | 1108 | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index163
| Benchmark | GLM 4.6 | o3 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 41.8% | Only o3 measured |
| MedScribe (Vals AI) | Not measured | 76.7% | Only o3 measured |
| IFBench | 43.4% | 71.4% | o3 |
| Long context reasoning (AA-LCR) | 54.0% | 74.7% | o3 |
| DTBench | Not measured | 84.8% | Only o3 measured |
| LMCA | Not measured | 39.7% | Only o3 measured |
| Fiction.LiveBench | Not measured | 88.9% | Only o3 measured |
| Creative writing (Lech Mazur) | Not measured | 83.9% | Only o3 measured |
| CL-bench | Not measured | 17.8% | Only o3 measured |
| LMArena Text | 1440 | 1410 | GLM 4.6 |
| LMArena Text: instruction following | 1410 | 1368 | GLM 4.6 |
| LMArena Text: creative writing | 1412 | 1359 | GLM 4.6 |
| LMArena Vision | Not measured | 1214 | Only o3 measured |
| LMArena Text: English | 1449 | 1417 | GLM 4.6 |
| LMArena Text: non-English | 1426 | 1401 | GLM 4.6 |
| LMArena Text: Chinese | 1499 | 1437 | GLM 4.6 |
| LMArena Text: German | 1447 | 1420 | GLM 4.6 |
| LMArena Text: Japanese | 1393 | 1403 | o3 |
| LMArena Text: Korean | 1400 | 1370 | GLM 4.6 |
| LMArena Text: French | 1459 | 1430 | GLM 4.6 |
| LMArena Text: Spanish | 1436 | 1395 | GLM 4.6 |
| LMArena Text: Russian | 1418 | 1406 | GLM 4.6 |
| LMArena Text: Polish | 1447 | 1438 | GLM 4.6 |
| LMArena Text: multi-turn | 1427 | 1405 | GLM 4.6 |
| LMArena Text: longer queries | 1422 | 1372 | GLM 4.6 |
| LMArena Text: writing and language | 1415 | 1372 | GLM 4.6 |
| LMArena Text: entertainment and media | 1409 | 1373 | GLM 4.6 |
| LMArena Vision: English | Not measured | 1221 | Only o3 measured |
| LMArena Vision: Chinese | Not measured | 1237 | Only o3 measured |
| LMArena Vision: reading text | Not measured | 1218 | Only o3 measured |
| LMArena Vision: humor | Not measured | 1222 | Only o3 measured |
| LMArena Vision: captioning | Not measured | 1210 | Only o3 measured |
| LMArena Vision: recognizing things | Not measured | 1234 | Only o3 measured |
| LMArena Vision: creative writing | Not measured | 1201 | Only o3 measured |
| Neither model has a result on the other 12 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.6 | o3 | 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 o3 |
|---|---|---|
| Maker | Z.ai | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | September 30, 2025 | April 16, 2025 |
| Context window | 205K | 200K |
| Input $/Mtok | $0.60 | $2.00 |
| Output $/Mtok | $2.20 | $8.00 |
| Coding index | 8.8 | Not benchmarked |
| Independent measurements behind it | 2 | 0 |
| Cost per solved task | Not computable | $0.571 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.6 better than o3 for coding?
Only GLM 4.6 has a coding score, 8.8 on the Vibe Coding Index; o3 has none, so the two cannot be ranked on coding. Pick o3 for work measured by LiveCodeBench (Vals AI) or LiveCodeBench (Artificial Analysis run), where it leads.
Which is cheaper, GLM 4.6 or o3?
GLM 4.6 costs $0.60 per million input tokens and $2.20 per million output tokens; o3 costs $2.00 and $8.00. Cost per solved task is known for o3 only.
Which has the bigger context window, GLM 4.6 or o3?
GLM 4.6, with 205K tokens against 200K.
How many benchmarks is the GLM 4.6 vs o3 comparison based on?
58 benchmarks have a result for both models as of October 22, 2026. GLM 4.6's coding index rests on 2 independent benchmarks and o3's on 0. Where only one model has a result, the page says so instead of naming a winner.