Command R7B (12-2024) vs GLM 5.2
Command R7B (12-2024) costs 29.3x less per output token than GLM 5.2 ($0.15 vs $4.40 per million). Updated October 21, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.15
- Coding
- 61.1
- Overall
- 62.5
- Output $/M
- $4.40
Command R7B (12-2024) or GLM 5.2: which to pick
Only GLM 5.2 has a coding score, 61.1 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 29.3x cheaper ($0.15 vs $4.40 per million).
No benchmark has tested both yet. Command R7B (12-2024) has results on Berkeley Function Calling Leaderboard (BFCL); GLM 5.2 on LMArena WebDev, AA Coding Index and LiveCodeBench (Vals AI). Compare them on price and context instead.
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 5.2 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GLM 5.2 has a AA Intelligence Index result, 33.7.
Directional only
Agentic work
AA Agentic Index
No call
Only GLM 5.2 has a AA Agentic Index result, 38.4.
Directional only
Reasoning
GPQA Diamond
No call
Only GLM 5.2 has a GPQA Diamond result, 85.6%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 5.2 has a MMLU Pro result, 86.7%.
Directional only
Web design
Design Arena: websites
No call
Only GLM 5.2 has a Design Arena: websites result, 1285.
Directional only
Long documents
Listed context window
GLM 5.2
GLM 5.2 takes 1.05M tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Command R7B (12-2024)
Command R7B (12-2024) $0.0001, GLM 5.2 $0.0036 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 5.2 has the number, $0.86.
Directional only
At a glance
- Coding index
- Command R7B (12-2024) not benchmarked, GLM 5.2 61.1 from 4 benchmarks.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- Command R7B (12-2024) $0.15 against GLM 5.2 $4.40 per million output tokens, 29.3x apart.
- Context window
- Command R7B (12-2024) takes 128K tokens, GLM 5.2 1.05M.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index18 boards measured
| Benchmark | Command R7B (12-2024) | GLM 5.2 | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1604 | Only GLM 5.2 measured |
| AA Coding Index | Not measured | 68.8 | Only GLM 5.2 measured |
| LiveCodeBench (Vals AI) | Not measured | 69.5% | Only GLM 5.2 measured |
| SWE-bench Verified (Vals AI) | Not measured | 82.8% | Only GLM 5.2 measured |
| Vibe Code Bench (Vals AI) | Not measured | 64.0% | Only GLM 5.2 measured |
| Code migration (Vals AI) | Not measured | 37.9% | Only GLM 5.2 measured |
| ProgramBench (Vals AI) | Not measured | 0.5% | Only GLM 5.2 measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 67.8% | Only GLM 5.2 measured |
| SkillsBench (Vals AI) | Not measured | 45.1% | Only GLM 5.2 measured |
| SciCode | Not measured | 51.2% | Only GLM 5.2 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 50.8% | Only GLM 5.2 measured |
| FrontierCode | Not measured | 24.5% | Only GLM 5.2 measured |
| WeirdML | Not measured | 70.1% | Only GLM 5.2 measured |
| ALE-Bench | Not measured | 1047.0 | Only GLM 5.2 measured |
| LMArena Text: coding | Not measured | 1487 | Only GLM 5.2 measured |
| LMArena Text: software and IT | Not measured | 1486 | Only GLM 5.2 measured |
| LMArena WebDev: React | Not measured | 1617 | Only GLM 5.2 measured |
| LMArena WebDev: HTML | Not measured | 1565 | Only GLM 5.2 measured |
| Neither model has a result on the other 21 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Command R7B (12-2024) | GLM 5.2 | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 33.7 | Only GLM 5.2 measured |
| Epoch Capabilities Index | Not measured | 151.9 | Only GLM 5.2 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index18 boards measured
| Benchmark | Command R7B (12-2024) | GLM 5.2 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 38.4 | Only GLM 5.2 measured |
| Design Arena: agents building web apps | Not measured | 1222 | Only GLM 5.2 measured |
| Design Arena: agents building full-stack apps | Not measured | 1241 | Only GLM 5.2 measured |
| Design Arena: agents building mobile apps | Not measured | 1160 | Only GLM 5.2 measured |
| Design Arena: agents building Android apps | Not measured | 1178 | Only GLM 5.2 measured |
| Design Arena: agents building Godot games | Not measured | 1142 | Only GLM 5.2 measured |
| Design Arena: agents building games | Not measured | 1206 | Only GLM 5.2 measured |
| Design Arena: HTML slides | Not measured | 1175 | Only GLM 5.2 measured |
| Design Arena: PowerPoint slides | Not measured | 1195 | Only GLM 5.2 measured |
| SREBench (Vals AI) | Not measured | 0.0% | Only GLM 5.2 measured |
| Finance Agent (Vals AI) | Not measured | 49.7% | Only GLM 5.2 measured |
| Legal Research Bench (Vals AI) | Not measured | 31.3% | Only GLM 5.2 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 61.5% | Only GLM 5.2 measured |
| τ²-Bench Telecom | Not measured | 99.1% | Only GLM 5.2 measured |
| APEX-Agents | Not measured | 45.2% | Only GLM 5.2 measured |
| PostTrainBench | Not measured | 31.7% | Only GLM 5.2 measured |
| Vending-Bench 2 | Not measured | $8,314 | Only GLM 5.2 measured |
| Berkeley Function Calling Leaderboard (BFCL) | 32.1% | Not measured | Only Command R7B (12-2024) measured |
| Neither model has a result on the other 23 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index14 boards measured
| Benchmark | Command R7B (12-2024) | GLM 5.2 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 85.6% | Only GLM 5.2 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 89.5% | Only GLM 5.2 measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 41.1% | Only GLM 5.2 measured |
| GPQA Diamond (Epoch AI run) | Not measured | 91.9% | Only GLM 5.2 measured |
| ARC-AGI | Not measured | 77.0% | Only GLM 5.2 measured |
| ARC-AGI-2 | Not measured | 22.8% | Only GLM 5.2 measured |
| SimpleBench | Not measured | 58.8% | Only GLM 5.2 measured |
| Chess puzzles | Not measured | 22.0% | Only GLM 5.2 measured |
| Mystery game puzzles | Not measured | 19.0% | Only GLM 5.2 measured |
| EBR-bench | Not measured | 9.5% | Only GLM 5.2 measured |
| LMArena Text: hard prompts | Not measured | 1481 | Only GLM 5.2 measured |
| LMArena Text: expert | Not measured | 1487 | Only GLM 5.2 measured |
| LMArena Text: hard prompts in English | Not measured | 1481 | Only GLM 5.2 measured |
| Kagi LLM Benchmark | Not measured | 62.6% | Only GLM 5.2 measured |
| Neither model has a result on the other 8 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | Command R7B (12-2024) | GLM 5.2 | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 86.4% | Only GLM 5.2 measured |
| FrontierMath, tiers 1 to 3 | Not measured | 59.2% | Only GLM 5.2 measured |
| FrontierMath, tier 4 | Not measured | 29.3% | Only GLM 5.2 measured |
| ProofBench | Not measured | 35.0% | Only GLM 5.2 measured |
| LMArena Text: math | Not measured | 1483 | Only GLM 5.2 measured |
| LMArena Text: mathematical work | Not measured | 1489 | Only GLM 5.2 measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index10 boards measured
| Benchmark | Command R7B (12-2024) | GLM 5.2 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 86.7% | Only GLM 5.2 measured |
| LegalBench (Vals AI) | Not measured | 84.1% | Only GLM 5.2 measured |
| CorpFin (Vals AI) | Not measured | 66.1% | Only GLM 5.2 measured |
| TaxEval (Vals AI) | Not measured | 73.3% | Only GLM 5.2 measured |
| MedCode (Vals AI) | Not measured | 40.8% | Only GLM 5.2 measured |
| SimpleQA Verified | Not measured | 34.2% | Only GLM 5.2 measured |
| LMArena Text: science | Not measured | 1489 | Only GLM 5.2 measured |
| LMArena Text: medicine and healthcare | Not measured | 1475 | Only GLM 5.2 measured |
| LMArena Text: legal and government | Not measured | 1486 | Only GLM 5.2 measured |
| LMArena Text: business and finance | Not measured | 1451 | Only GLM 5.2 measured |
| Neither model has a result on the other 8 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards measured
| Benchmark | Command R7B (12-2024) | GLM 5.2 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1285 | Only GLM 5.2 measured |
| Design Arena: UI components | Not measured | 1296 | Only GLM 5.2 measured |
| Design Arena: game dev | Not measured | 1268 | Only GLM 5.2 measured |
| Design Arena: data viz | Not measured | 1285 | Only GLM 5.2 measured |
| Design Arena: 3D | Not measured | 1288 | Only GLM 5.2 measured |
| Design Arena: all code categories | Not measured | 1288 | Only GLM 5.2 measured |
| Design Arena: SVG | Not measured | 1214 | Only GLM 5.2 measured |
| Design Arena: ASCII art | Not measured | 1190 | Only GLM 5.2 measured |
| Neither model has a result on the other 1 board here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index23 boards measured
| Benchmark | Command R7B (12-2024) | GLM 5.2 | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | Not measured | 83.5% | Only GLM 5.2 measured |
| IFBench | Not measured | 73.3% | Only GLM 5.2 measured |
| Long context reasoning (AA-LCR) | Not measured | 78.3% | Only GLM 5.2 measured |
| DTBench | Not measured | 93.6% | Only GLM 5.2 measured |
| LMCA | Not measured | 45.8% | Only GLM 5.2 measured |
| Surface Evolver Bench | Not measured | 55.6% | Only GLM 5.2 measured |
| LMArena Text | Not measured | 1470 | Only GLM 5.2 measured |
| LMArena Text: instruction following | Not measured | 1466 | Only GLM 5.2 measured |
| LMArena Text: creative writing | Not measured | 1463 | Only GLM 5.2 measured |
| LMArena Text: English | Not measured | 1475 | Only GLM 5.2 measured |
| LMArena Text: non-English | Not measured | 1459 | Only GLM 5.2 measured |
| LMArena Text: Chinese | Not measured | 1522 | Only GLM 5.2 measured |
| LMArena Text: German | Not measured | 1470 | Only GLM 5.2 measured |
| LMArena Text: Japanese | Not measured | 1450 | Only GLM 5.2 measured |
| LMArena Text: Korean | Not measured | 1440 | Only GLM 5.2 measured |
| LMArena Text: French | Not measured | 1479 | Only GLM 5.2 measured |
| LMArena Text: Spanish | Not measured | 1478 | Only GLM 5.2 measured |
| LMArena Text: Russian | Not measured | 1468 | Only GLM 5.2 measured |
| LMArena Text: Polish | Not measured | 1456 | Only GLM 5.2 measured |
| LMArena Text: multi-turn | Not measured | 1469 | Only GLM 5.2 measured |
| LMArena Text: longer queries | Not measured | 1480 | Only GLM 5.2 measured |
| LMArena Text: writing and language | Not measured | 1463 | Only GLM 5.2 measured |
| LMArena Text: entertainment and media | Not measured | 1450 | Only GLM 5.2 measured |
| Neither model has a result on the other 23 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 5.2 |
|---|---|---|
| Maker | Cohere | Z.ai |
| Weights | No public weights listed | Open weights |
| Released | December 14, 2024 | June 16, 2026 |
| Context window | 128K | 1.05M |
| Input $/Mtok | $0.0375 | $1.40 |
| Output $/Mtok | $0.15 | $4.40 |
| Coding index | Not benchmarked | 61.1 |
| Independent measurements behind it | 0 | 4 |
| Cost per solved task | Not computable | $0.86 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Command R7B (12-2024) better than GLM 5.2 for coding?
Only GLM 5.2 has a coding score, 61.1 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 29.3x cheaper ($0.15 vs $4.40 per million).
Which is cheaper, Command R7B (12-2024) or GLM 5.2?
Command R7B (12-2024) costs $0.0375 per million input tokens and $0.15 per million output tokens; GLM 5.2 costs $1.40 and $4.40. Cost per solved task is known for GLM 5.2 only.
Which has the bigger context window, Command R7B (12-2024) or GLM 5.2?
GLM 5.2, with 1.05M tokens against 128K.
Has any benchmark tested both Command R7B (12-2024) and GLM 5.2?
No benchmark has tested both yet. Command R7B (12-2024) has results on Berkeley Function Calling Leaderboard (BFCL); GLM 5.2 on LMArena WebDev, AA Coding Index and LiveCodeBench (Vals AI). Compare them on price and context instead. Data as of October 21, 2026.