Command A vs GLM 4.6
Updated October 21, 2026. 49 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Only GLM 4.6 has a coding score, 8.8 on the Vibe Coding Index; Command A has none, so the two cannot be ranked on coding.
Command A has the edge where the context window is larger, 256K against 205K tokens, and where what matters is τ²-Bench Telecom or LegalBench (Vals AI), where it leads.
- Shared results
- 49
- Command A only
- 11
- GLM 4.6 only
- 17
- Like-for-like categories
- 7 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, 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
GLM 4.6
GLM 4.6 leads, 18.5 against 13.1.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Command A has a AA Agentic Index result, 1.0.
Directional only
Reasoning
GPQA Diamond
GLM 4.6
GLM 4.6 leads, 74.5% against 48.5%.
Same board, both measured
Math
AIME
GLM 4.6
GLM 4.6 leads, 92.7% against 13.3%.
Same board, both measured
Knowledge
MMLU Pro
GLM 4.6
GLM 4.6 leads, 82.2% against 69.2%.
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
Command A
Command A takes 256K tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.6
Command A $0.0075, GLM 4.6 $0.0017 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Command A has the number, $2.69.
Directional only
At a glance
- Coding index
- Command A not benchmarked, GLM 4.6 8.8 from 2 benchmarks.
- Widest category
- Math, 45.6 points apart on average over 6 shared benchmarks.
- Widest single gap
- AIME: Command A 13.3% against GLM 4.6 92.7%, 79.4 points apart on a 0 to 100 scale.
- Output price
- Command A $10.00 against GLM 4.6 $2.20 per million output tokens, 4.5x apart.
- Context window
- Command A takes 256K tokens, GLM 4.6 205K.
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 | Command A | Bars | GLM 4.6 | Ties |
|---|---|---|---|---|
| Coding | 0 | 6 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 0 | 6 | ||
| Math | 0 | 6 | ||
| Knowledge | 1 | 9 | ||
| General | 1 | 18 | ||
| All shared boards | 3 | 46 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index06
| Benchmark | Command A | GLM 4.6 | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 68.2% | Only GLM 4.6 measured |
| LMArena WebDev | Not measured | 1341 | Only GLM 4.6 measured |
| AA Coding Index | 27.8 | 45.8 | GLM 4.6 |
| LiveCodeBench (Vals AI) | 35.1% | 81.0% | GLM 4.6 |
| SWE-bench Verified (Vals AI) | 7.8% | Not measured | Only Command A measured |
| Vibe Code Bench (Vals AI) | Not measured | 3.1% | Only GLM 4.6 measured |
| Terminal-Bench 2.1 (Vals AI) | 17.6% | Not measured | Only Command A measured |
| LiveCodeBench (Artificial Analysis run) | 28.7% | 69.5% | GLM 4.6 |
| SciCode | 38.5% | Not measured | Only Command A measured |
| Terminal-Bench Hard (Artificial Analysis run) | 25.0% | 28.8% | GLM 4.6 |
| Terminal-Bench | Not measured | 24.5% | Only GLM 4.6 measured |
| ALE-Bench | Not measured | 340.8 | Only GLM 4.6 measured |
| LMArena Text: coding | 1330 | 1449 | GLM 4.6 |
| LMArena Text: software and IT | 1336 | 1455 | GLM 4.6 |
| LMArena WebDev: HTML | Not measured | 1350 | Only GLM 4.6 measured |
| Neither model has a result on the other 23 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Command A | GLM 4.6 | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.1 | 18.5 | GLM 4.6 |
| 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 | Command A | GLM 4.6 | Leader |
|---|---|---|---|
| AA Agentic Index | 1.0 | Not measured | Only Command A measured |
| Design Arena: agents building full-stack apps | Not measured | 1029 | Only GLM 4.6 measured |
| Design Arena: agents building mobile apps | Not measured | 1096 | Only GLM 4.6 measured |
| Design Arena: agents building Android apps | Not measured | 1091 | Only GLM 4.6 measured |
| Design Arena: agents building Godot games | Not measured | 1180 | Only GLM 4.6 measured |
| Finance Agent (Vals AI) | 9.0% | Not measured | Only Command A measured |
| τ²-Bench Telecom | 80.7% | 76.9% | Command A |
| Neither model has a result on the other 32 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | Command A | GLM 4.6 | Leader |
|---|---|---|---|
| GPQA Diamond | 48.5% | 74.5% | GLM 4.6 |
| GPQA Diamond (Artificial Analysis run) | 76.1% | 78.0% | GLM 4.6 |
| Humanity's Last Exam (Artificial Analysis run) | 12.0% | 14.5% | GLM 4.6 |
| LMArena Text: hard prompts | 1326 | 1440 | GLM 4.6 |
| LMArena Text: expert | 1296 | 1432 | GLM 4.6 |
| LMArena Text: hard prompts in English | 1335 | 1445 | GLM 4.6 |
| Neither model has a result on the other 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | Command A | GLM 4.6 | Leader |
|---|---|---|---|
| AIME | 13.3% | 92.7% | GLM 4.6 |
| MATH 500 (Vals AI) | 76.2% | Not measured | Only Command A measured |
| MGSM (Vals AI) | 85.7% | 89.8% | GLM 4.6 |
| AA Math Index | 13.0 | 86.0 | GLM 4.6 |
| AIME 2025 (Artificial Analysis run) | 13.0% | 86.0% | GLM 4.6 |
| LMArena Text: math | 1300 | 1432 | GLM 4.6 |
| LMArena Text: mathematical work | 1312 | 1447 | GLM 4.6 |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index19
| Benchmark | Command A | GLM 4.6 | Leader |
|---|---|---|---|
| MMLU Pro | 69.2% | 82.2% | GLM 4.6 |
| LegalBench (Vals AI) | 79.7% | 79.6% | Command A |
| CaseLaw (Vals AI) | 64.5% | Not measured | Only Command A measured |
| CorpFin (Vals AI) | 46.0% | 56.8% | GLM 4.6 |
| TaxEval (Vals AI) | 61.4% | 66.2% | GLM 4.6 |
| MedQA (Vals AI) | 80.5% | 92.2% | GLM 4.6 |
| MedCode (Vals AI) | 19.7% | Not measured | Only Command A measured |
| MMLU Pro (Artificial Analysis run) | 71.2% | 82.9% | GLM 4.6 |
| LMArena Text: science | 1339 | 1456 | GLM 4.6 |
| LMArena Text: medicine and healthcare | 1334 | 1455 | GLM 4.6 |
| LMArena Text: legal and government | 1348 | 1448 | GLM 4.6 |
| LMArena Text: business and finance | 1315 | 1416 | GLM 4.6 |
| Neither model has a result on the other 5 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | Command A | GLM 4.6 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1174 | Only GLM 4.6 measured |
| Design Arena: UI components | Not measured | 1148 | Only GLM 4.6 measured |
| Design Arena: game dev | Not measured | 1151 | Only GLM 4.6 measured |
| Design Arena: data viz | Not measured | 1161 | Only GLM 4.6 measured |
| Design Arena: 3D | Not measured | 1129 | Only GLM 4.6 measured |
| Design Arena: all code categories | Not measured | 1166 | Only GLM 4.6 measured |
| Design Arena: SVG | Not measured | 1109 | 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 index118
| Benchmark | Command A | GLM 4.6 | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | 55.7% | Not measured | Only Command A measured |
| IFBench | 73.9% | 43.4% | Command A |
| Long context reasoning (AA-LCR) | 52.7% | 54.0% | GLM 4.6 |
| DTBench | 61.3% | Not measured | Only Command A measured |
| LMCA | 10.3% | Not measured | Only Command A measured |
| LMArena Text | 1331 | 1440 | GLM 4.6 |
| LMArena Text: instruction following | 1309 | 1410 | GLM 4.6 |
| LMArena Text: creative writing | 1319 | 1412 | GLM 4.6 |
| LMArena Text: English | 1344 | 1449 | GLM 4.6 |
| LMArena Text: non-English | 1313 | 1426 | GLM 4.6 |
| LMArena Text: Chinese | 1327 | 1499 | GLM 4.6 |
| LMArena Text: German | 1342 | 1447 | GLM 4.6 |
| LMArena Text: Japanese | 1285 | 1393 | GLM 4.6 |
| LMArena Text: Korean | 1285 | 1400 | GLM 4.6 |
| LMArena Text: French | 1351 | 1459 | GLM 4.6 |
| LMArena Text: Spanish | 1347 | 1436 | GLM 4.6 |
| LMArena Text: Russian | 1314 | 1418 | GLM 4.6 |
| LMArena Text: Polish | 1324 | 1447 | GLM 4.6 |
| LMArena Text: multi-turn | 1338 | 1427 | GLM 4.6 |
| LMArena Text: longer queries | 1334 | 1422 | GLM 4.6 |
| LMArena Text: writing and language | 1328 | 1415 | GLM 4.6 |
| LMArena Text: entertainment and media | 1306 | 1409 | GLM 4.6 |
| Neither model has a result on the other 24 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Command A | GLM 4.6 | 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 | Cohere Command A | Z.ai GLM 4.6 |
|---|---|---|
| Maker | Cohere | Z.ai |
| Weights | Open weights | Open weights |
| Released | March 13, 2025 | September 30, 2025 |
| Context window | 256K | 205K |
| Input $/Mtok | $2.50 | $0.60 |
| Output $/Mtok | $10.00 | $2.20 |
| Coding index | Not benchmarked | 8.8 |
| Independent measurements behind it | 1 | 2 |
| Cost per solved task | $2.69 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Command A better than GLM 4.6 for coding?
Only GLM 4.6 has a coding score, 8.8 on the Vibe Coding Index; Command A has none, so the two cannot be ranked on coding. Command A has the edge where the context window is larger, 256K against 205K tokens, and where what matters is τ²-Bench Telecom or LegalBench (Vals AI), where it leads.
Which is cheaper, Command A or GLM 4.6?
Command A costs $2.50 per million input tokens and $10.00 per million output tokens; GLM 4.6 costs $0.60 and $2.20. Cost per solved task is known for Command A only.
Which has the bigger context window, Command A or GLM 4.6?
Command A, with 256K tokens against 205K.
How many benchmarks is the Command A vs GLM 4.6 comparison based on?
49 benchmarks have a result for both models as of October 21, 2026. Command A's coding index rests on 1 independent benchmark and GLM 4.6's on 2. Where only one model has a result, the page says so instead of naming a winner.