Command R7B (12-2024) vs GLM 5.3 Flash
Command R7B (12-2024) costs 3.3x less per output token than GLM 5.3 Flash ($0.15 vs $0.50 per million). Updated October 21, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.15
- Coding
- 57.7
- Overall
- 66.0
- Output $/M
- $0.50
Command R7B (12-2024) or GLM 5.3 Flash: which to pick
Only GLM 5.3 Flash has a coding score, 57.7 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 3.3x cheaper ($0.15 vs $0.50 per million).
No benchmark has tested both yet. Command R7B (12-2024) has results on Berkeley Function Calling Leaderboard (BFCL); GLM 5.3 Flash on LiveBench coding, LMArena WebDev and AA Coding Index. 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 5.3 Flash has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GLM 5.3 Flash has a AA Intelligence Index result, 41.8.
Directional only
Agentic work
AA Agentic Index
No call
Only GLM 5.3 Flash has a AA Agentic Index result, 50.9.
Directional only
Reasoning
GPQA Diamond
No call
Only GLM 5.3 Flash has a GPQA Diamond result, 86.4%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 5.3 Flash has a MMLU Pro result, 86.1%.
Directional only
Web design
Design Arena: websites
No call
Only GLM 5.3 Flash has a Design Arena: websites result, 1272.
Directional only
General
Vals Index
No call
Only GLM 5.3 Flash has a Vals Index result, 50.9%.
Directional only
Long documents
Listed context window
GLM 5.3 Flash
GLM 5.3 Flash 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 5.3 Flash $0.0004 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 5.3 Flash has the number, $0.02.
Directional only
At a glance
- Coding index
- Command R7B (12-2024) not benchmarked, GLM 5.3 Flash 57.7 from 6 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 5.3 Flash $0.50 per million output tokens, 3.3x apart.
- Context window
- Command R7B (12-2024) takes 128K tokens, GLM 5.3 Flash 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 Index23 boards measured
| Benchmark | Command R7B (12-2024) | GLM 5.3 Flash | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 80.6% | Only GLM 5.3 Flash measured |
| LMArena WebDev | Not measured | 1607 | Only GLM 5.3 Flash measured |
| AA Coding Index | Not measured | 71.5 | Only GLM 5.3 Flash measured |
| LiveCodeBench (Vals AI) | Not measured | 80.5% | Only GLM 5.3 Flash measured |
| SWE-bench Verified (Vals AI) | Not measured | 92.0% | Only GLM 5.3 Flash measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 37.4% | Only GLM 5.3 Flash measured |
| Vibe Code Bench (Vals AI) | Not measured | 30.8% | Only GLM 5.3 Flash measured |
| Code migration (Vals AI) | Not measured | 20.5% | Only GLM 5.3 Flash measured |
| ProgramBench (Vals AI) | Not measured | 0.0% | Only GLM 5.3 Flash measured |
| IOI (Vals AI) | Not measured | 52.5% | Only GLM 5.3 Flash measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 62.9% | Only GLM 5.3 Flash measured |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 16.0% | Only GLM 5.3 Flash measured |
| SkillsBench (Vals AI) | Not measured | 40.2% | Only GLM 5.3 Flash measured |
| CyberBench (Vals AI) | Not measured | 67.9% | Only GLM 5.3 Flash measured |
| SciCode | Not measured | 51.6% | Only GLM 5.3 Flash measured |
| FrontierCode | Not measured | 31.8% | Only GLM 5.3 Flash measured |
| FrontierSWE | Not measured | 18.1% | Only GLM 5.3 Flash measured |
| ALE-Bench | Not measured | 303.6 | Only GLM 5.3 Flash measured |
| LMArena Text: coding | Not measured | 1507 | Only GLM 5.3 Flash measured |
| LMArena Text: software and IT | Not measured | 1500 | Only GLM 5.3 Flash measured |
| LMArena WebDev: React | Not measured | 1624 | Only GLM 5.3 Flash measured |
| LMArena WebDev: HTML | Not measured | 1577 | Only GLM 5.3 Flash measured |
| LMArena WebDev: from an image | Not measured | 1578 | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 16 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Command R7B (12-2024) | GLM 5.3 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 41.8 | Only GLM 5.3 Flash measured |
| Epoch Capabilities Index | Not measured | 153.1 | Only GLM 5.3 Flash measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index13 boards measured
| Benchmark | Command R7B (12-2024) | GLM 5.3 Flash | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 50.9 | Only GLM 5.3 Flash measured |
| LiveBench research | Not measured | 70.1% | Only GLM 5.3 Flash measured |
| LiveBench browser | Not measured | 84.0% | Only GLM 5.3 Flash measured |
| LiveBench long agentic | Not measured | 28.4% | Only GLM 5.3 Flash measured |
| SREBench (Vals AI) | Not measured | 0.8% | Only GLM 5.3 Flash measured |
| Finance Agent (Vals AI) | Not measured | 57.9% | Only GLM 5.3 Flash measured |
| Tax Agent Bench (Vals AI) | Not measured | 31.6% | Only GLM 5.3 Flash measured |
| Legal Research Bench (Vals AI) | Not measured | 45.2% | Only GLM 5.3 Flash measured |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 10.4% | Only GLM 5.3 Flash measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 55.9% | Only GLM 5.3 Flash measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 2.9% | Only GLM 5.3 Flash measured |
| APEX-Agents | Not measured | 52.8% | Only GLM 5.3 Flash measured |
| Berkeley Function Calling Leaderboard (BFCL) | 32.1% | Not measured | Only Command R7B (12-2024) measured |
| Neither model has a result on the other 28 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index15 boards measured
| Benchmark | Command R7B (12-2024) | GLM 5.3 Flash | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 86.4% | Only GLM 5.3 Flash measured |
| LiveBench reasoning | Not measured | 77.6% | Only GLM 5.3 Flash measured |
| MMMU Pro (Vals AI) | Not measured | 86.0% | Only GLM 5.3 Flash measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 91.2% | Only GLM 5.3 Flash measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 39.9% | Only GLM 5.3 Flash measured |
| GPQA Diamond (Epoch AI run) | Not measured | 90.2% | Only GLM 5.3 Flash measured |
| ARC-AGI | Not measured | 91.0% | Only GLM 5.3 Flash measured |
| ARC-AGI-2 | Not measured | 65.8% | Only GLM 5.3 Flash measured |
| Chess puzzles | Not measured | 21.0% | Only GLM 5.3 Flash measured |
| Mystery game puzzles | Not measured | 8.0% | Only GLM 5.3 Flash measured |
| LMArena Text: hard prompts | Not measured | 1490 | Only GLM 5.3 Flash measured |
| LMArena Text: expert | Not measured | 1511 | Only GLM 5.3 Flash measured |
| LMArena Text: hard prompts in English | Not measured | 1488 | Only GLM 5.3 Flash measured |
| LMArena Vision: diagrams | Not measured | 1317 | Only GLM 5.3 Flash measured |
| LMArena Vision: homework | Not measured | 1318 | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 7 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | Command R7B (12-2024) | GLM 5.3 Flash | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 93.9% | Only GLM 5.3 Flash measured |
| FrontierMath, tiers 1 to 3 | Not measured | 55.8% | Only GLM 5.3 Flash measured |
| FrontierMath, tier 4 | Not measured | 17.1% | Only GLM 5.3 Flash measured |
| ProofBench | Not measured | 21.0% | Only GLM 5.3 Flash measured |
| LMArena Text: math | Not measured | 1503 | Only GLM 5.3 Flash measured |
| LMArena Text: mathematical work | Not measured | 1497 | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | Command R7B (12-2024) | GLM 5.3 Flash | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 86.1% | Only GLM 5.3 Flash measured |
| LegalBench (Vals AI) | Not measured | 83.9% | Only GLM 5.3 Flash measured |
| TaxEval (Vals AI) | Not measured | 75.6% | Only GLM 5.3 Flash measured |
| LMArena Text: science | Not measured | 1484 | Only GLM 5.3 Flash measured |
| LMArena Text: medicine and healthcare | Not measured | 1475 | Only GLM 5.3 Flash measured |
| LMArena Text: legal and government | Not measured | 1473 | Only GLM 5.3 Flash measured |
| LMArena Text: business and finance | Not measured | 1460 | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 11 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards measured
| Benchmark | Command R7B (12-2024) | GLM 5.3 Flash | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1272 | Only GLM 5.3 Flash measured |
| Design Arena: UI components | Not measured | 1316 | Only GLM 5.3 Flash measured |
| Design Arena: game dev | Not measured | 1278 | Only GLM 5.3 Flash measured |
| Design Arena: data viz | Not measured | 1261 | Only GLM 5.3 Flash measured |
| Design Arena: 3D | Not measured | 1316 | Only GLM 5.3 Flash measured |
| Design Arena: all code categories | Not measured | 1282 | Only GLM 5.3 Flash measured |
| Design Arena: SVG | Not measured | 1284 | Only GLM 5.3 Flash measured |
| Design Arena: ASCII art | Not measured | 1268 | Only GLM 5.3 Flash measured |
| LiveBench design | Not measured | 77.2% | Only GLM 5.3 Flash measured |
GeneralDirectional onlyPublic boards, shown as published, kept out of the index28 boards measured
| Benchmark | Command R7B (12-2024) | GLM 5.3 Flash | Leader |
|---|---|---|---|
| Vals Index | Not measured | 50.9% | Only GLM 5.3 Flash measured |
| LiveBench data analysis | Not measured | 76.4% | Only GLM 5.3 Flash measured |
| MedScribe (Vals AI) | Not measured | 88.9% | Only GLM 5.3 Flash measured |
| Long context reasoning (AA-LCR) | Not measured | 80.0% | Only GLM 5.3 Flash measured |
| Surface Evolver Bench | Not measured | 52.5% | Only GLM 5.3 Flash measured |
| LMArena Text | Not measured | 1471 | Only GLM 5.3 Flash measured |
| LMArena Text: instruction following | Not measured | 1478 | Only GLM 5.3 Flash measured |
| LMArena Text: creative writing | Not measured | 1441 | Only GLM 5.3 Flash measured |
| LMArena Vision | Not measured | 1294 | Only GLM 5.3 Flash measured |
| LMArena Text: English | Not measured | 1474 | Only GLM 5.3 Flash measured |
| LMArena Text: non-English | Not measured | 1462 | Only GLM 5.3 Flash measured |
| LMArena Text: Chinese | Not measured | 1531 | Only GLM 5.3 Flash measured |
| LMArena Text: German | Not measured | 1462 | Only GLM 5.3 Flash measured |
| LMArena Text: Japanese | Not measured | 1444 | Only GLM 5.3 Flash measured |
| LMArena Text: Korean | Not measured | 1436 | Only GLM 5.3 Flash measured |
| LMArena Text: French | Not measured | 1491 | Only GLM 5.3 Flash measured |
| LMArena Text: Spanish | Not measured | 1475 | Only GLM 5.3 Flash measured |
| LMArena Text: Russian | Not measured | 1465 | Only GLM 5.3 Flash measured |
| LMArena Text: Polish | Not measured | 1450 | Only GLM 5.3 Flash measured |
| LMArena Text: multi-turn | Not measured | 1469 | Only GLM 5.3 Flash measured |
| LMArena Text: longer queries | Not measured | 1481 | Only GLM 5.3 Flash measured |
| LMArena Text: writing and language | Not measured | 1455 | Only GLM 5.3 Flash measured |
| LMArena Text: entertainment and media | Not measured | 1440 | Only GLM 5.3 Flash measured |
| LMArena Vision: English | Not measured | 1295 | Only GLM 5.3 Flash measured |
| LMArena Vision: Chinese | Not measured | 1363 | Only GLM 5.3 Flash measured |
| LMArena Vision: reading text | Not measured | 1299 | Only GLM 5.3 Flash measured |
| LMArena Vision: humor | Not measured | 1279 | Only GLM 5.3 Flash measured |
| LMArena Vision: creative writing | Not measured | 1270 | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 18 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 5.3 Flash |
|---|---|---|
| Maker | Cohere | Z.ai |
| Weights | No public weights listed | Open weights |
| Released | December 14, 2024 | August 26, 2026 |
| Context window | 128K | 1.05M |
| Input $/Mtok | $0.0375 | $0.15 |
| Output $/Mtok | $0.15 | $0.50 |
| Coding index | Not benchmarked | 57.7 |
| Independent measurements behind it | 0 | 6 |
| Cost per solved task | Not computable | $0.02 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Command R7B (12-2024) better than GLM 5.3 Flash for coding?
Only GLM 5.3 Flash has a coding score, 57.7 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 3.3x cheaper ($0.15 vs $0.50 per million).
Which is cheaper, Command R7B (12-2024) or GLM 5.3 Flash?
Command R7B (12-2024) costs $0.0375 per million input tokens and $0.15 per million output tokens; GLM 5.3 Flash costs $0.15 and $0.50. Cost per solved task is known for GLM 5.3 Flash only.
Which has the bigger context window, Command R7B (12-2024) or GLM 5.3 Flash?
GLM 5.3 Flash, with 1.05M tokens against 128K.
Has any benchmark tested both Command R7B (12-2024) and GLM 5.3 Flash?
No benchmark has tested both yet. Command R7B (12-2024) has results on Berkeley Function Calling Leaderboard (BFCL); GLM 5.3 Flash on LiveBench coding, LMArena WebDev and AA Coding Index. Compare them on price and context instead. Data as of October 21, 2026.