Command R7B (12-2024) vs GLM 4.6
Command R7B (12-2024) costs 14.7x less per output token than GLM 4.6 ($0.15 vs $2.20 per million). Updated October 21, 2026; 1 benchmarks have a result for both.
Command R7B (12-2024) or GLM 4.6: which to pick
Only GLM 4.6 has a coding score, 8.8 on the Vibe Coding Index; Command R7B (12-2024) has none, so the two cannot be ranked on coding.
Pick Command R7B (12-2024) for output tokens 14.7x cheaper ($0.15 vs $2.20 per million).
- Shared results
- 1
- Command R7B (12-2024) only
- 0
- GLM 4.6 only
- 68
- Like-for-like categories
- 1 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
No call
Only GLM 4.6 has a AA Intelligence Index result, 18.5.
Directional only
Reasoning
GPQA Diamond
No call
Only GLM 4.6 has a GPQA Diamond result, 74.5%.
Directional only
Math
AIME
No call
Only GLM 4.6 has a AIME result, 92.7%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 4.6 has a MMLU Pro result, 82.2%.
Directional only
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
Command R7B (12-2024)
Command R7B (12-2024) $0.0001, GLM 4.6 $0.0017 per turn.
List prices
At a glance
- Coding index
- Command R7B (12-2024) not benchmarked, GLM 4.6 8.8 from 2 benchmarks.
- Widest category
- Agentic work, 40.3 points apart on average over 1 shared benchmark.
- Widest single gap
- Berkeley Function Calling Leaderboard (BFCL): Command R7B (12-2024) 32.1% against GLM 4.6 72.4%, 40.3 points apart on a 0 to 100 scale.
- Output price
- Command R7B (12-2024) $0.15 against GLM 4.6 $2.20 per million output tokens, 14.7x apart.
- Context window
- Command R7B (12-2024) takes 128K 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 R7B (12-2024) | Bars | GLM 4.6 | Ties |
|---|---|---|---|---|
| Agentic work | 0 | 1 | ||
| All shared boards | 0 | 1 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index12 boards, none shared
| Benchmark | Command R7B (12-2024) | 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 | Not measured | 45.8 | Only GLM 4.6 measured |
| LiveCodeBench (Vals AI) | Not measured | 81.0% | Only GLM 4.6 measured |
| Vibe Code Bench (Vals AI) | Not measured | 3.1% | Only GLM 4.6 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 69.5% | Only GLM 4.6 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 28.8% | Only GLM 4.6 measured |
| 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 | Not measured | 1449 | Only GLM 4.6 measured |
| LMArena Text: software and IT | Not measured | 1455 | Only GLM 4.6 measured |
| LMArena WebDev: HTML | Not measured | 1350 | Only GLM 4.6 measured |
| Neither model has a result on the other 27 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Command R7B (12-2024) | GLM 4.6 | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 18.5 | Only GLM 4.6 measured |
| 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 | Command R7B (12-2024) | GLM 4.6 | Leader |
|---|---|---|---|
| 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 | 1084 | Only GLM 4.6 measured |
| Design Arena: agents building Godot games | Not measured | 1180 | Only GLM 4.6 measured |
| τ²-Bench Telecom | Not measured | 76.9% | Only GLM 4.6 measured |
| Berkeley Function Calling Leaderboard (BFCL) | 32.1% | 72.4% | GLM 4.6 |
| BrowseComp-Plus | Not measured | 48.2% | Only GLM 4.6 measured |
| Neither model has a result on the other 34 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | Command R7B (12-2024) | GLM 4.6 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 74.5% | Only GLM 4.6 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 78.0% | Only GLM 4.6 measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 14.5% | Only GLM 4.6 measured |
| LMArena Text: hard prompts | Not measured | 1440 | Only GLM 4.6 measured |
| LMArena Text: expert | Not measured | 1432 | Only GLM 4.6 measured |
| LMArena Text: hard prompts in English | Not measured | 1445 | Only GLM 4.6 measured |
| Kagi LLM Benchmark | Not measured | 45.7% | Only GLM 4.6 measured |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Command R7B (12-2024) | GLM 4.6 | Leader |
|---|---|---|---|
| AIME | Not measured | 92.7% | Only GLM 4.6 measured |
| MGSM (Vals AI) | Not measured | 89.8% | Only GLM 4.6 measured |
| AA Math Index | Not measured | 86.0 | Only GLM 4.6 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 86.0% | Only GLM 4.6 measured |
| LMArena Text: math | Not measured | 1432 | Only GLM 4.6 measured |
| LMArena Text: mathematical work | Not measured | 1447 | Only GLM 4.6 measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | Command R7B (12-2024) | GLM 4.6 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 82.2% | Only GLM 4.6 measured |
| LegalBench (Vals AI) | Not measured | 79.6% | Only GLM 4.6 measured |
| CorpFin (Vals AI) | Not measured | 56.8% | Only GLM 4.6 measured |
| TaxEval (Vals AI) | Not measured | 66.2% | Only GLM 4.6 measured |
| MedQA (Vals AI) | Not measured | 92.2% | Only GLM 4.6 measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 82.9% | Only GLM 4.6 measured |
| LMArena Text: science | Not measured | 1456 | Only GLM 4.6 measured |
| LMArena Text: medicine and healthcare | Not measured | 1455 | Only GLM 4.6 measured |
| LMArena Text: legal and government | Not measured | 1448 | Only GLM 4.6 measured |
| LMArena Text: business and finance | Not measured | 1416 | Only GLM 4.6 measured |
| Neither model has a result on the other 8 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | Command R7B (12-2024) | 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 | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index19 boards, none shared
| Benchmark | Command R7B (12-2024) | GLM 4.6 | Leader |
|---|---|---|---|
| IFBench | Not measured | 43.4% | Only GLM 4.6 measured |
| Long context reasoning (AA-LCR) | Not measured | 54.0% | Only GLM 4.6 measured |
| LMArena Text | Not measured | 1440 | Only GLM 4.6 measured |
| LMArena Text: instruction following | Not measured | 1410 | Only GLM 4.6 measured |
| LMArena Text: creative writing | Not measured | 1412 | Only GLM 4.6 measured |
| LMArena Text: English | Not measured | 1449 | Only GLM 4.6 measured |
| LMArena Text: non-English | Not measured | 1426 | Only GLM 4.6 measured |
| LMArena Text: Chinese | Not measured | 1499 | Only GLM 4.6 measured |
| LMArena Text: German | Not measured | 1447 | Only GLM 4.6 measured |
| LMArena Text: Japanese | Not measured | 1393 | Only GLM 4.6 measured |
| LMArena Text: Korean | Not measured | 1400 | Only GLM 4.6 measured |
| LMArena Text: French | Not measured | 1459 | Only GLM 4.6 measured |
| LMArena Text: Spanish | Not measured | 1436 | Only GLM 4.6 measured |
| LMArena Text: Russian | Not measured | 1418 | Only GLM 4.6 measured |
| LMArena Text: Polish | Not measured | 1447 | Only GLM 4.6 measured |
| LMArena Text: multi-turn | Not measured | 1427 | Only GLM 4.6 measured |
| LMArena Text: longer queries | Not measured | 1422 | Only GLM 4.6 measured |
| LMArena Text: writing and language | Not measured | 1415 | Only GLM 4.6 measured |
| LMArena Text: entertainment and media | Not measured | 1409 | Only GLM 4.6 measured |
| Neither model has a result on the other 27 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Command R7B (12-2024) | 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
Command R7B (12-2024) costs less
Repository review
50K input + 3K output tokens
Command R7B (12-2024) costs less
Agent loop
200K input + 10K output tokens
Command R7B (12-2024) 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 R7B (12-2024) | Z.ai GLM 4.6 |
|---|---|---|
| Maker | Cohere | Z.ai |
| Weights | No public weights listed | Open weights |
| Released | December 14, 2024 | September 30, 2025 |
| Context window | 128K | 205K |
| Input $/Mtok | $0.0375 | $0.60 |
| Output $/Mtok | $0.15 | $2.20 |
| Coding index | Not benchmarked | 8.8 |
| Independent measurements behind it | 0 | 2 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Command R7B (12-2024) better than GLM 4.6 for coding?
Only GLM 4.6 has a coding score, 8.8 on the Vibe Coding Index; Command R7B (12-2024) has none, so the two cannot be ranked on coding. Pick Command R7B (12-2024) for output tokens 14.7x cheaper ($0.15 vs $2.20 per million).
Which is cheaper, Command R7B (12-2024) or GLM 4.6?
Command R7B (12-2024) costs $0.0375 per million input tokens and $0.15 per million output tokens; GLM 4.6 costs $0.60 and $2.20. Cost per solved task is known for neither of them.
Which has the bigger context window, Command R7B (12-2024) or GLM 4.6?
GLM 4.6, with 205K tokens against 128K.
How many benchmarks is the Command R7B (12-2024) vs GLM 4.6 comparison based on?
1 benchmarks have a result for both models as of October 21, 2026. Command R7B (12-2024)'s coding index rests on 0 independent benchmarks and GLM 4.6's on 2. Where only one model has a result, the page says so instead of naming a winner.