Model comparison
GLM-4.5V vs GLM-4.6V
GLM-4.6V is the stronger model overall, scoring 40.9 to 39.2 on the Noometry Index.
Last verified . 12 shared benchmarks.
Summary
- They share 12 benchmarks with published results for both. GLM-4.5V scores higher in 0 categories and GLM-4.6V in 8 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-4.6V leads 56.8 to 52.7.
- GLM-4.6V is cheaper at $0.30 / $0.90 per million input/output tokens, against $0.60 / $1.80 for GLM-4.5V.
- GLM-4.6V accepts more context: 128K tokens versus 64K.
Side by side
| GLM-4.5V | GLM-4.6V | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Z.ai (Zhipu) |
| Noometry Index | 39.2 | 40.9 |
| Released | 2025-08-11 | 2025-12-08 |
| Weights | Open | Open |
| Context window | 64K | 128K |
| Max output | 16K | 33K |
| Input $ / M tokens | $0.60 | $0.30 |
| Output $ / M tokens | $1.80 | $0.90 |
| Results tracked | 15 | 12 |
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Category by category
Coding GLM-4.6V leads
GLM-4.5V: 38.9 (#152), GLM-4.6V: 40.2 (#128)
| Benchmark | GLM-4.5V | GLM-4.6V |
|---|---|---|
| LMArena Coding | 1347 | 1390 |
Reasoning Too close to call
GLM-4.5V: 26.5 (#118), GLM-4.6V: 26.5 (#117)
| Benchmark | GLM-4.5V | GLM-4.6V |
|---|---|---|
| LMArena Hard Prompts | 1335 | 1368 |
| Kagi LLM Benchmark | 59.8% | — |
Math Not comparable
GLM-4.5V: 34.7 (#157), GLM-4.6V: —
| Benchmark | GLM-4.5V | GLM-4.6V |
|---|---|---|
| LMArena Math | 1353 | — |
Knowledge Too close to call
GLM-4.5V: 36.8 (#155), GLM-4.6V: 37.4 (#148)
| Benchmark | GLM-4.5V | GLM-4.6V |
|---|---|---|
| LMArena Expert | 1353 | 1372 |
Multimodal Too close to call
GLM-4.5V: 33.6 (#86), GLM-4.6V: 34.2 (#83)
| Benchmark | GLM-4.5V | GLM-4.6V |
|---|---|---|
| LMArena Vision | 1154 | 1164 |
Multilingual GLM-4.6V leads
GLM-4.5V: 44.9 (#172), GLM-4.6V: 48.8 (#135)
| Benchmark | GLM-4.5V | GLM-4.6V |
|---|---|---|
| LMArena Non-English | 1304 | 1359 |
| LMArena Chinese | 1338 | 1426 |
| LMArena Russian | 1299 | 1341 |
| LMArena Spanish | 1335 | — |
Instruction Following GLM-4.6V leads
GLM-4.5V: 69.4 (#170), GLM-4.6V: 71.6 (#145)
| Benchmark | GLM-4.5V | GLM-4.6V |
|---|---|---|
| LMArena Instruction Following | 1311 | 1351 |
Long Context GLM-4.6V leads
GLM-4.5V: 39.6 (#166), GLM-4.6V: 41.3 (#138)
| Benchmark | GLM-4.5V | GLM-4.6V |
|---|---|---|
| LMArena Longer Query | 1305 | 1358 |
Writing & Preference GLM-4.6V leads
GLM-4.5V: 52.7 (#162), GLM-4.6V: 56.8 (#132)
| Benchmark | GLM-4.5V | GLM-4.6V |
|---|---|---|
| LMArena Text | 1333 | 1377 |
| LMArena Creative Writing | 1295 | 1347 |
| LMArena Multi-Turn | 1333 | 1359 |
Frequently asked questions
Is GLM-4.5V better than GLM-4.6V?
GLM-4.6V is the stronger model overall, scoring 40.9 to 39.2 on the Noometry Index.
Which is cheaper, GLM-4.5V or GLM-4.6V?
GLM-4.6V is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; GLM-4.5V lists at $0.60 and $1.80.
Is GLM-4.5V or GLM-4.6V better for coding?
GLM-4.6V scores higher on coding benchmarks: 40.2 versus 38.9 in the Noometry coding category.
Which has the bigger context window?
GLM-4.6V does, with 128K tokens against 64K.
How many benchmarks do GLM-4.5V and GLM-4.6V share?
12 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and GLM-4.6V has 12.