GLM 4.5 vs Grok 4.3
Grok 4.3 leads GLM 4.5 on the Artificial Analysis Intelligence Index, 24.9 to 12.8, at about the same output price ($2.50 vs $2.20 per million). Updated October 22, 2026; 50 benchmarks have a result for both.
GLM 4.5 or Grok 4.3: which to pick
Only Grok 4.3 has a coding score, 37.4 on the Vibe Coding Index; GLM 4.5 has none, so the two cannot be ranked on coding.
Pick GLM 4.5 for output tokens 1.1x cheaper ($2.20 vs $2.50 per million) and work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
- Shared results
- 50
- GLM 4.5 only
- 11
- Grok 4.3 only
- 55
- 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 Grok 4.3 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Grok 4.3
Grok 4.3 leads, 24.9 against 12.8.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Grok 4.3 has a AA Agentic Index result, 15.5.
Directional only
Reasoning
GPQA Diamond
Grok 4.3
Grok 4.3 leads, 91.4% against 72.2%.
Same board, both measured
Math
AIME
No call
Only GLM 4.5 has a AIME result, 86.7%.
Directional only
Knowledge
MMLU Pro
Grok 4.3
Grok 4.3 leads, 85.8% against 81.2%.
Same board, both measured
Web design
Design Arena: websites
Grok 4.3
Grok 4.3 leads, 1196 against 1169.
Same board, both measured
Long documents
Listed context window
Grok 4.3
Grok 4.3 takes 1M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.5
GLM 4.5 $0.0017, Grok 4.3 $0.0025 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Grok 4.3 has the number, $0.31.
Directional only
At a glance
- Coding index
- GLM 4.5 not benchmarked, Grok 4.3 37.4 from 3 benchmarks.
- Widest category
- Agentic work, 54.7 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: GLM 4.5 43.0% against Grok 4.3 97.7%, 54.7 points apart on a 0 to 100 scale.
- Output price
- GLM 4.5 $2.20 against Grok 4.3 $2.50 per million output tokens, 1.1x apart.
- Context window
- GLM 4.5 takes 131K tokens, Grok 4.3 1M.
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 4.5 | Bars | Grok 4.3 | Ties |
|---|---|---|---|---|
| Coding | 2 | 4 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 3 | 3 | ||
| Math | 2 | 0 | ||
| Knowledge | 5 | 3 | ||
| Web design | 2 | 5 | ||
| General | 17 | 2 | ||
| All shared boards | 31 | 19 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index24
| Benchmark | GLM 4.5 | Grok 4.3 | Leader |
|---|---|---|---|
| SWE-bench Verified | 64.2% | Not measured | Only GLM 4.5 measured |
| LMArena WebDev | Not measured | 1358 | Only Grok 4.3 measured |
| AA Coding Index | Not measured | 42.2 | Only Grok 4.3 measured |
| LiveCodeBench (Vals AI) | 67.5% | 84.5% | Grok 4.3 |
| SWE-bench Verified (Vals AI) | Not measured | 71.4% | Only Grok 4.3 measured |
| Vibe Code Bench (Vals AI) | Not measured | 19.4% | Only Grok 4.3 measured |
| Code migration (Vals AI) | Not measured | 6.8% | Only Grok 4.3 measured |
| ProgramBench (Vals AI) | Not measured | 0.0% | Only Grok 4.3 measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 42.0% | Only Grok 4.3 measured |
| SkillsBench (Vals AI) | Not measured | 40.6% | Only Grok 4.3 measured |
| LiveCodeBench (Artificial Analysis run) | 73.8% | Not measured | Only GLM 4.5 measured |
| SciCode | Not measured | 48.3% | Only Grok 4.3 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 22.0% | 37.9% | Grok 4.3 |
| WeirdML | 40.6% | 49.9% | Grok 4.3 |
| ALE-Bench | 344.8 | 944.2 | Grok 4.3 |
| LMArena Text: coding | 1434 | 1415 | GLM 4.5 |
| LMArena Text: software and IT | 1440 | 1416 | GLM 4.5 |
| LMArena WebDev: React | Not measured | 1353 | Only Grok 4.3 measured |
| LMArena WebDev: HTML | Not measured | 1367 | Only Grok 4.3 measured |
| LMArena WebDev: from an image | Not measured | 1375 | Only Grok 4.3 measured |
| Neither model has a result on the other 19 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.5 | Grok 4.3 | Leader |
|---|---|---|---|
| AA Intelligence Index | 12.8 | 24.9 | Grok 4.3 |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.5 | Grok 4.3 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 15.5 | Only Grok 4.3 measured |
| Design Arena: agents building web apps | Not measured | 1126 | Only Grok 4.3 measured |
| Design Arena: agents building full-stack apps | Not measured | 1006 | Only Grok 4.3 measured |
| Design Arena: agents building mobile apps | Not measured | 1058 | Only Grok 4.3 measured |
| Design Arena: agents building Android apps | Not measured | 962 | Only Grok 4.3 measured |
| Design Arena: agents building Godot games | Not measured | 914 | Only Grok 4.3 measured |
| Design Arena: agents building games | Not measured | 1007 | Only Grok 4.3 measured |
| Design Arena: HTML slides | Not measured | 959 | Only Grok 4.3 measured |
| Design Arena: PowerPoint slides | Not measured | 988 | Only Grok 4.3 measured |
| Design Arena: spreadsheets | Not measured | 879 | Only Grok 4.3 measured |
| Finance Agent (Vals AI) | Not measured | 37.7% | Only Grok 4.3 measured |
| Tax Agent Bench (Vals AI) | Not measured | 6.2% | Only Grok 4.3 measured |
| Legal Research Bench (Vals AI) | Not measured | 15.4% | Only Grok 4.3 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 18.4% | Only Grok 4.3 measured |
| τ²-Bench Telecom | 43.0% | 97.7% | Grok 4.3 |
| Vending-Bench 2 | Not measured | $35 | Only Grok 4.3 measured |
| LMArena Search | Not measured | 1166 | Only Grok 4.3 measured |
| Neither model has a result on the other 24 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index33
| Benchmark | GLM 4.5 | Grok 4.3 | Leader |
|---|---|---|---|
| GPQA Diamond | 72.2% | 91.4% | Grok 4.3 |
| MMMU Pro (Vals AI) | Not measured | 83.1% | Only Grok 4.3 measured |
| GPQA Diamond (Artificial Analysis run) | 78.2% | 90.1% | Grok 4.3 |
| Humanity's Last Exam (Artificial Analysis run) | 13.0% | 37.2% | Grok 4.3 |
| GPQA Diamond (Epoch AI run) | Not measured | 88.8% | Only Grok 4.3 measured |
| Chess puzzles | Not measured | 18.0% | Only Grok 4.3 measured |
| LMArena Text: hard prompts | 1429 | 1396 | GLM 4.5 |
| LMArena Text: expert | 1433 | 1384 | GLM 4.5 |
| LMArena Text: hard prompts in English | 1429 | 1402 | GLM 4.5 |
| LMArena Vision: diagrams | Not measured | 1241 | Only Grok 4.3 measured |
| LMArena Vision: homework | Not measured | 1241 | Only Grok 4.3 measured |
| Kagi LLM Benchmark | 57.9% | Not measured | Only GLM 4.5 measured |
| Neither model has a result on the other 10 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 4.5 | Grok 4.3 | Leader |
|---|---|---|---|
| AIME | 86.7% | Not measured | Only GLM 4.5 measured |
| MATH 500 (Vals AI) | 94.0% | Not measured | Only GLM 4.5 measured |
| MGSM (Vals AI) | 90.8% | Not measured | Only GLM 4.5 measured |
| AA Math Index | 73.7 | Not measured | Only GLM 4.5 measured |
| AIME 2025 (Artificial Analysis run) | 73.7% | Not measured | Only GLM 4.5 measured |
| OTIS Mock AIME | Not measured | 93.3% | Only Grok 4.3 measured |
| FrontierMath, tiers 1 to 3 | Not measured | 42.8% | Only Grok 4.3 measured |
| FrontierMath, tier 4 | Not measured | 14.6% | Only Grok 4.3 measured |
| ProofBench | Not measured | 11.0% | Only Grok 4.3 measured |
| LMArena Text: math | 1427 | 1387 | GLM 4.5 |
| LMArena Text: mathematical work | 1430 | 1396 | GLM 4.5 |
| Neither model has a result on the other 2 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index53
| Benchmark | GLM 4.5 | Grok 4.3 | Leader |
|---|---|---|---|
| MMLU Pro | 81.2% | 85.8% | Grok 4.3 |
| LegalBench (Vals AI) | 75.6% | 84.5% | Grok 4.3 |
| CaseLaw (Vals AI) | Not measured | 79.3% | Only Grok 4.3 measured |
| CorpFin (Vals AI) | 61.0% | 68.5% | Grok 4.3 |
| TaxEval (Vals AI) | 72.4% | 70.8% | GLM 4.5 |
| MortgageTax (Vals AI) | Not measured | 48.3% | Only Grok 4.3 measured |
| MedQA (Vals AI) | 90.0% | Not measured | Only GLM 4.5 measured |
| MedCode (Vals AI) | Not measured | 38.1% | Only Grok 4.3 measured |
| MMLU Pro (Artificial Analysis run) | 83.5% | Not measured | Only GLM 4.5 measured |
| SimpleQA Verified | Not measured | 33.2% | Only Grok 4.3 measured |
| LMArena Text: science | 1444 | 1399 | GLM 4.5 |
| LMArena Text: medicine and healthcare | 1455 | 1384 | GLM 4.5 |
| LMArena Text: legal and government | 1438 | 1386 | GLM 4.5 |
| LMArena Text: business and finance | 1420 | 1381 | GLM 4.5 |
| Neither model has a result on the other 4 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index25
| Benchmark | GLM 4.5 | Grok 4.3 | Leader |
|---|---|---|---|
| Design Arena: websites | 1169 | 1196 | Grok 4.3 |
| Design Arena: UI components | 1141 | 1190 | Grok 4.3 |
| Design Arena: game dev | 1144 | 1173 | Grok 4.3 |
| Design Arena: data viz | 1159 | 1179 | Grok 4.3 |
| Design Arena: 3D | 1170 | 1124 | GLM 4.5 |
| Design Arena: all code categories | 1165 | 1186 | Grok 4.3 |
| Design Arena: SVG | 1096 | 1080 | GLM 4.5 |
| Design Arena: ASCII art | Not measured | 1125 | Only Grok 4.3 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index172
| Benchmark | GLM 4.5 | Grok 4.3 | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | Not measured | 51.7% | Only Grok 4.3 measured |
| SAGE (Vals AI) | Not measured | 19.7% | Only Grok 4.3 measured |
| MedScribe (Vals AI) | Not measured | 74.4% | Only Grok 4.3 measured |
| Vals Multimodal Index | Not measured | 43.3% | Only Grok 4.3 measured |
| IFBench | 44.1% | 83.3% | Grok 4.3 |
| Long context reasoning (AA-LCR) | 52.7% | 75.0% | Grok 4.3 |
| DTBench | Not measured | 90.7% | Only Grok 4.3 measured |
| LMCA | Not measured | 38.3% | Only Grok 4.3 measured |
| Creative writing (Lech Mazur) | 73.4% | Not measured | Only GLM 4.5 measured |
| LMArena Text | 1430 | 1397 | GLM 4.5 |
| LMArena Text: instruction following | 1404 | 1366 | GLM 4.5 |
| LMArena Text: creative writing | 1395 | 1380 | GLM 4.5 |
| LMArena Vision | Not measured | 1229 | Only Grok 4.3 measured |
| LMArena Text: English | 1437 | 1404 | GLM 4.5 |
| LMArena Text: non-English | 1417 | 1385 | GLM 4.5 |
| LMArena Text: Chinese | 1465 | 1422 | GLM 4.5 |
| LMArena Text: German | 1407 | 1395 | GLM 4.5 |
| LMArena Text: Japanese | 1415 | 1379 | GLM 4.5 |
| LMArena Text: Korean | 1380 | 1354 | GLM 4.5 |
| LMArena Text: French | 1418 | 1411 | GLM 4.5 |
| LMArena Text: Spanish | 1454 | 1399 | GLM 4.5 |
| LMArena Text: Russian | 1413 | 1399 | GLM 4.5 |
| LMArena Text: Polish | 1422 | 1398 | GLM 4.5 |
| LMArena Text: multi-turn | 1415 | 1406 | GLM 4.5 |
| LMArena Text: longer queries | 1412 | 1393 | GLM 4.5 |
| LMArena Text: writing and language | 1403 | 1385 | GLM 4.5 |
| LMArena Text: entertainment and media | 1404 | 1370 | GLM 4.5 |
| LMArena Vision: English | Not measured | 1235 | Only Grok 4.3 measured |
| LMArena Vision: Chinese | Not measured | 1257 | Only Grok 4.3 measured |
| LMArena Vision: reading text | Not measured | 1235 | Only Grok 4.3 measured |
| LMArena Vision: humor | Not measured | 1222 | Only Grok 4.3 measured |
| LMArena Vision: recognizing things | Not measured | 1217 | Only Grok 4.3 measured |
| LMArena Vision: creative writing | Not measured | 1237 | Only Grok 4.3 measured |
| Neither model has a result on the other 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.5 | Grok 4.3 | 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 4.5 costs less
Repository review
50K input + 3K output tokens
GLM 4.5 costs less
Agent loop
200K input + 10K output tokens
GLM 4.5 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 4.5 | SpaceXAI Grok 4.3 |
|---|---|---|
| Maker | Z.ai | xAI |
| Weights | Open weights | No public weights listed |
| Released | July 25, 2025 | April 30, 2026 |
| Context window | 131K | 1M |
| Input $/Mtok | $0.60 | $1.25 |
| Output $/Mtok | $2.20 | $2.50 |
| Coding index | Not benchmarked | 37.4 |
| Independent measurements behind it | 0 | 3 |
| Cost per solved task | Not computable | $0.31 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.5 better than Grok 4.3 for coding?
Only Grok 4.3 has a coding score, 37.4 on the Vibe Coding Index; GLM 4.5 has none, so the two cannot be ranked on coding. Pick GLM 4.5 for output tokens 1.1x cheaper ($2.20 vs $2.50 per million) and work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
Which is cheaper, GLM 4.5 or Grok 4.3?
GLM 4.5 costs $0.60 per million input tokens and $2.20 per million output tokens; Grok 4.3 costs $1.25 and $2.50. Cost per solved task is known for Grok 4.3 only.
Which has the bigger context window, GLM 4.5 or Grok 4.3?
Grok 4.3, with 1M tokens against 131K.
How many benchmarks is the GLM 4.5 vs Grok 4.3 comparison based on?
50 benchmarks have a result for both models as of October 22, 2026. GLM 4.5's coding index rests on 0 independent benchmarks and Grok 4.3's on 3. Where only one model has a result, the page says so instead of naming a winner.