Model comparison
Command R vs GLM-5.3
GLM-5.3 is the stronger model overall, scoring 54.8 to 31.4 on the Noometry Index. Command R costs 8.2× less per token, which makes it the better buy when GLM-5.3's lead doesn't matter for your workload.
Last verified . 19 shared benchmarks.
Summary
- They share 19 benchmarks with published results for both. Command R scores higher in 0 categories and GLM-5.3 in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-5.3 leads 75.7 to 38.2.
- The biggest single-benchmark swing is LMCA: 9.2% for Command R and 55.5% for GLM-5.3.
- Command R is cheaper at $0.15 / $0.60 per million input/output tokens, against $1.40 / $4.40 for GLM-5.3.
- GLM-5.3 accepts more context: 1M tokens versus 128K.
Side by side
| Command R | GLM-5.3 | |
|---|---|---|
| Provider | Cohere | Z.ai (Zhipu) |
| Noometry Index | 31.4 | 54.8 |
| Released | 2024-08-30 | 2026-08-14 |
| Weights | Open | Open |
| Context window | 128K | 1M |
| Max output | 4K | 131K |
| Input $ / M tokens | $0.15 | $1.40 |
| Output $ / M tokens | $0.60 | $4.40 |
| Results tracked | 29 | 42 |
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Category by category
Coding GLM-5.3 leads
Command R: 29.3 (#306), GLM-5.3: 59.5 (#14)
| Benchmark | Command R | GLM-5.3 |
|---|---|---|
| LMArena Coding | 1169 | 1496 |
| DeepSWE | — | 69% |
| FrontierCode | — | 40.1% |
| CursorBench | — | 42.6% |
| LMArena WebDev | — | 1622 |
| FrontierSWE | — | 30.2% |
| SciCode | — | 59% |
| WeirdML | — | 75.4% |
| BigCodeBench Instruct | 37.1% | — |
| LiveBench Coding | 17.9% | — |
| BigCodeBench Complete | 45.2% | — |
| ALE-Bench | — | 1,317 |
Agentic & Tool Use Not comparable
Command R: —, GLM-5.3: 36.4 (#38)
| Benchmark | Command R | GLM-5.3 |
|---|---|---|
| APEX-Agents | — | 56.6% |
| Vending-Bench 2 | — | 8,164 |
Reasoning GLM-5.3 leads
Command R: 13.8 (#331), GLM-5.3: 46.1 (#46)
| Benchmark | Command R | GLM-5.3 |
|---|---|---|
| LMArena Hard Prompts | 1164 | 1489 |
| DTBench | 46.4% | 87.7% |
| LMCA | 9.2% | 55.5% |
| NYT Connections (extended) | — | 74.2% |
| CritPt | — | 19.1% |
| Chess Puzzles | — | 21% |
| LiveBench Reasoning | 21.9% | — |
| Mystery Game Puzzles | — | 33% |
| LiveBench Data Analysis | 33.3% | — |
| Bench to the Future 3 | — | 0.15 |
| Epoch Capabilities Index | — | 155.61 |
| LiveBench | 27.5% | — |
Math GLM-5.3 leads
Command R: 28.0 (#246), GLM-5.3: 62.3 (#33)
| Benchmark | Command R | GLM-5.3 |
|---|---|---|
| LMArena Math | 1155 | 1489 |
| FrontierMath (Tiers 1-3) | — | 68.8% |
| FrontierMath Tier 4 | — | 29.3% |
| OTIS Mock AIME 2024-2025 | — | 91.1% |
| ProofBench | — | 49% |
| LiveBench Math | 19.4% | — |
Knowledge GLM-5.3 leads
Command R: 31.0 (#221), GLM-5.3: 58.3 (#37)
| Benchmark | Command R | GLM-5.3 |
|---|---|---|
| LMArena Expert | 1138 | 1516 |
| GPQA Diamond | — | 90.9% |
| SimpleQA Verified | — | 41% |
| MMLU | 65.2% | — |
Multilingual GLM-5.3 leads
Command R: 35.7 (#245), GLM-5.3: 55.7 (#28)
| Benchmark | Command R | GLM-5.3 |
|---|---|---|
| LMArena Non-English | 1174 | 1457 |
| LMArena Chinese | 1182 | 1528 |
| LMArena French | 1162 | 1499 |
| LMArena German | 1176 | 1499 |
| LMArena Japanese | 1143 | 1453 |
| LMArena Korean | 1163 | 1472 |
| LMArena Russian | 1174 | 1463 |
| LMArena Spanish | 1151 | 1460 |
Instruction Following GLM-5.3 leads
Command R: 58.1 (#261), GLM-5.3: 77.5 (#23)
| Benchmark | Command R | GLM-5.3 |
|---|---|---|
| LMArena Instruction Following | 1167 | 1477 |
| LiveBench Instruction Following | 55.6% | — |
Long Context GLM-5.3 leads
Command R: 36.3 (#231), GLM-5.3: 45.4 (#41)
| Benchmark | Command R | GLM-5.3 |
|---|---|---|
| LMArena Longer Query | 1198 | 1482 |
Writing & Preference GLM-5.3 leads
Command R: 38.2 (#254), GLM-5.3: 75.7 (#6)
| Benchmark | Command R | GLM-5.3 |
|---|---|---|
| LMArena Text | 1187 | 1471 |
| LMArena Creative Writing | 1170 | 1457 |
| LMArena Multi-Turn | 1163 | 1472 |
| EQ-Bench Creative Writing | — | 2075 |
| LiveBench Language | 16.7% | — |
Frequently asked questions
Is Command R better than GLM-5.3?
GLM-5.3 is the stronger model overall, scoring 54.8 to 31.4 on the Noometry Index. Command R costs 8.2× less per token, which makes it the better buy when GLM-5.3's lead doesn't matter for your workload.
Which is cheaper, Command R or GLM-5.3?
Command R is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; GLM-5.3 lists at $1.40 and $4.40.
Is Command R or GLM-5.3 better for coding?
GLM-5.3 scores higher on coding benchmarks: 59.5 versus 29.3 in the Noometry coding category.
Which has the bigger context window?
GLM-5.3 does, with 1M tokens against 128K.
How many benchmarks do Command R and GLM-5.3 share?
19 benchmarks have published results for both models. Command R has 29 scored results on Noometry and GLM-5.3 has 42.