Command R (08-2024) vs GLM 4.5V
Command R (08-2024) costs 3.0x less per output token than GLM 4.5V ($0.60 vs $1.80 per million). Updated October 21, 2026; 22 benchmarks have a result for both.
Command R (08-2024) or GLM 4.5V: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Command R (08-2024) for output tokens 3.0x cheaper ($0.60 vs $1.80 per million) and a larger context window (128K against 66K tokens).
- Shared results
- 22
- Command R (08-2024) only
- 3
- GLM 4.5V only
- 19
- Like-for-like categories
- 5 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GLM 4.5V has a AA Intelligence Index result, 7.6.
Directional only
Long documents
Listed context window
Command R (08-2024)
Command R (08-2024) takes 128K tokens against 66K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Command R (08-2024)
Command R (08-2024) $0.0004, GLM 4.5V $0.0015 per turn.
List prices
At a glance
- Coding index
- Command R (08-2024) not benchmarked, GLM 4.5V not benchmarked.
- Widest category
- Reasoning, 27.3 points apart on average over 3 shared benchmarks.
- Widest single gap
- LMArena Text: expert: Command R (08-2024) 1173 against GLM 4.5V 1353, 29.9 points apart on a 0 to 100 scale.
- Output price
- Command R (08-2024) $0.60 against GLM 4.5V $1.80 per million output tokens, 3.0x apart.
- Context window
- Command R (08-2024) takes 128K tokens, GLM 4.5V 66K.
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 R (08-2024) | Bars | GLM 4.5V | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Reasoning | 0 | 3 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| General | 0 | 11 | ||
| All shared boards | 0 | 22 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | Command R (08-2024) | GLM 4.5V | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | Not measured | 60.4% | Only GLM 4.5V measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 6.8% | Only GLM 4.5V measured |
| LMArena Text: coding | 1187 | 1347 | GLM 4.5V |
| LMArena Text: software and IT | 1197 | 1353 | GLM 4.5V |
| Neither model has a result on the other 35 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Command R (08-2024) | GLM 4.5V | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 7.6 | Only GLM 4.5V measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Command R (08-2024) | GLM 4.5V | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 22.5% | Only GLM 4.5V measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Command R (08-2024) | GLM 4.5V | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 68.4% | Only GLM 4.5V measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 6.3% | Only GLM 4.5V measured |
| LMArena Text: hard prompts | 1186 | 1335 | GLM 4.5V |
| LMArena Text: expert | 1173 | 1353 | GLM 4.5V |
| LMArena Text: hard prompts in English | 1194 | 1357 | GLM 4.5V |
| LMArena Vision: diagrams | Not measured | 1151 | Only GLM 4.5V measured |
| LMArena Vision: homework | Not measured | 1163 | Only GLM 4.5V measured |
| Kagi LLM Benchmark | Not measured | 59.8% | Only GLM 4.5V measured |
| Neither model has a result on the other 14 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Command R (08-2024) | GLM 4.5V | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 73.0 | Only GLM 4.5V measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 73.0% | Only GLM 4.5V measured |
| LMArena Text: math | 1188 | 1354 | GLM 4.5V |
| LMArena Text: mathematical work | 1198 | 1336 | GLM 4.5V |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Command R (08-2024) | GLM 4.5V | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 78.8% | Only GLM 4.5V measured |
| LMArena Text: science | 1252 | 1330 | GLM 4.5V |
| LMArena Text: medicine and healthcare | 1238 | 1349 | GLM 4.5V |
| LMArena Text: legal and government | 1268 | 1338 | GLM 4.5V |
| LMArena Text: business and finance | 1215 | 1309 | GLM 4.5V |
| Neither model has a result on the other 13 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Command R (08-2024) | GLM 4.5V | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index011
| Benchmark | Command R (08-2024) | GLM 4.5V | Leader |
|---|---|---|---|
| IFBench | Not measured | 34.2% | Only GLM 4.5V measured |
| Long context reasoning (AA-LCR) | Not measured | 0.0% | Only GLM 4.5V measured |
| LMArena Text | 1229 | 1333 | GLM 4.5V |
| LMArena Text: instruction following | 1197 | 1311 | GLM 4.5V |
| LMArena Text: creative writing | 1234 | 1294 | GLM 4.5V |
| LMArena Vision | Not measured | 1154 | Only GLM 4.5V measured |
| LMArena Text: English | 1238 | 1362 | GLM 4.5V |
| LMArena Text: non-English | 1216 | 1303 | GLM 4.5V |
| LMArena Text: Chinese | 1227 | 1337 | GLM 4.5V |
| LMArena Text: German | 1216 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: Japanese | 1166 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: Korean | 1162 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: Spanish | Not measured | 1336 | Only GLM 4.5V measured |
| LMArena Text: Russian | 1227 | 1298 | GLM 4.5V |
| LMArena Text: Polish | Not measured | 1302 | Only GLM 4.5V measured |
| LMArena Text: multi-turn | 1213 | 1333 | GLM 4.5V |
| LMArena Text: longer queries | 1230 | 1305 | GLM 4.5V |
| LMArena Text: writing and language | 1241 | 1302 | GLM 4.5V |
| LMArena Text: entertainment and media | 1223 | 1303 | GLM 4.5V |
| LMArena Vision: English | Not measured | 1183 | Only GLM 4.5V measured |
| LMArena Vision: reading text | Not measured | 1151 | Only GLM 4.5V measured |
| Neither model has a result on the other 25 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Command R (08-2024) | GLM 4.5V | 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 R (08-2024) costs less
Repository review
50K input + 3K output tokens
Command R (08-2024) costs less
Agent loop
200K input + 10K output tokens
Command R (08-2024) 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 R (08-2024) | Z.ai GLM 4.5V |
|---|---|---|
| Maker | Cohere | Z.ai |
| Weights | No public weights listed | Open weights |
| Released | August 30, 2024 | August 11, 2025 |
| Context window | 128K | 66K |
| Input $/Mtok | $0.15 | $0.60 |
| Output $/Mtok | $0.60 | $1.80 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Command R (08-2024) better than GLM 4.5V for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Command R (08-2024) for output tokens 3.0x cheaper ($0.60 vs $1.80 per million) and a larger context window (128K against 66K tokens).
Which is cheaper, Command R (08-2024) or GLM 4.5V?
Command R (08-2024) costs $0.15 per million input tokens and $0.60 per million output tokens; GLM 4.5V costs $0.60 and $1.80. Cost per solved task is known for neither of them.
Which has the bigger context window, Command R (08-2024) or GLM 4.5V?
Command R (08-2024), with 128K tokens against 66K.
How many benchmarks is the Command R (08-2024) vs GLM 4.5V comparison based on?
22 benchmarks have a result for both models as of October 21, 2026. Command R (08-2024)'s coding index rests on 0 independent benchmarks and GLM 4.5V's on 0. Where only one model has a result, the page says so instead of naming a winner.