Model comparison
GLM-4.6V vs Qwen3 235B-A22B
Qwen3 235B-A22B is the stronger model overall, scoring 43.2 to 40.9 on the Noometry Index. GLM-4.6V costs 2.7× less per token, which makes it the better buy when Qwen3 235B-A22B's lead doesn't matter for your workload.
Last verified . 11 shared benchmarks.
Alibaba (Qwen)Summary
- They share 11 benchmarks with published results for both. GLM-4.6V scores higher in 1 category and Qwen3 235B-A22B in 6 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Qwen3 235B-A22B leads 49.5 to 37.4.
- GLM-4.6V is cheaper at $0.30 / $0.90 per million input/output tokens, against $0.70 / $2.80 for Qwen3 235B-A22B.
- Qwen3 235B-A22B accepts more context: 131K tokens versus 128K.
Side by side
| GLM-4.6V | Qwen3 235B-A22B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 40.9 | 43.2 |
| Released | 2025-12-08 | 2025-04 |
| Weights | Open | Open |
| Context window | 128K | 131K |
| Max output | 33K | 16K |
| Input $ / M tokens | $0.30 | $0.70 |
| Output $ / M tokens | $0.90 | $2.80 |
| Results tracked | 12 | 49 |
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Category by category
Coding Qwen3 235B-A22B leads
GLM-4.6V: 40.2 (#128), Qwen3 235B-A22B: 43.8 (#72)
| Benchmark | GLM-4.6V | Qwen3 235B-A22B |
|---|---|---|
| LMArena Coding | 1390 | 1445 |
| Aider Polyglot | — | 59.6% |
| SciCode | — | 42.4% |
| WeirdML | — | 41% |
Agentic & Tool Use Not comparable
GLM-4.6V: —, Qwen3 235B-A22B: 33.7 (#48)
| Benchmark | GLM-4.6V | Qwen3 235B-A22B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 52.1% |
| Vending-Bench 2 | — | -11.34 |
Reasoning GLM-4.6V leads
GLM-4.6V: 26.5 (#117), Qwen3 235B-A22B: 15.8 (#302)
| Benchmark | GLM-4.6V | Qwen3 235B-A22B |
|---|---|---|
| LMArena Hard Prompts | 1368 | 1433 |
| ARC-AGI-2 | — | 1.3% |
| SimpleBench | — | 31% |
| Kagi LLM Benchmark | — | 69.4% |
| ARC-AGI-1 | — | 11% |
| CritPt | — | 0% |
| Chess Puzzles | — | 12% |
| Mystery Game Puzzles | — | 9% |
| DTBench | — | 80.3% |
| LMCA | — | 29.3% |
| Epoch Capabilities Index | — | 143.87 |
| ForecastBench | — | 59.7 |
Math Not comparable
GLM-4.6V: —, Qwen3 235B-A22B: 48.5 (#51)
| Benchmark | GLM-4.6V | Qwen3 235B-A22B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 86.7% |
| Omni-MATH | — | 71.8% |
| LMArena Math | — | 1431 |
| MATH Level 5 | — | 68.9% |
| FrontierMath (Feb 2025 set) | — | 8.5% |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge Qwen3 235B-A22B leads
GLM-4.6V: 37.4 (#148), Qwen3 235B-A22B: 49.5 (#67)
| Benchmark | GLM-4.6V | Qwen3 235B-A22B |
|---|---|---|
| LMArena Expert | 1372 | 1460 |
| GPQA Diamond | — | 80.1% |
| SimpleQA Verified | — | 40.4% |
| MMLU-Pro | — | 84.4% |
| Confabulations | — | 15.6% |
| Vectara Hallucination Rate | — | 9.3% |
| GPQA (HELM) | — | 72.7% |
Multimodal Not comparable
GLM-4.6V: 34.2 (#83), Qwen3 235B-A22B: —
| Benchmark | GLM-4.6V | Qwen3 235B-A22B |
|---|---|---|
| LMArena Vision | 1164 | — |
Multilingual Qwen3 235B-A22B leads
GLM-4.6V: 48.8 (#135), Qwen3 235B-A22B: 52.5 (#84)
| Benchmark | GLM-4.6V | Qwen3 235B-A22B |
|---|---|---|
| LMArena Non-English | 1359 | 1410 |
| LMArena Chinese | 1426 | 1481 |
| LMArena Russian | 1341 | 1411 |
| LMArena French | — | 1445 |
| LMArena German | — | 1432 |
| LMArena Japanese | — | 1400 |
| LMArena Korean | — | 1391 |
| LMArena Spanish | — | 1429 |
Instruction Following Qwen3 235B-A22B leads
GLM-4.6V: 71.6 (#145), Qwen3 235B-A22B: 72.8 (#132)
| Benchmark | GLM-4.6V | Qwen3 235B-A22B |
|---|---|---|
| LMArena Instruction Following | 1351 | 1408 |
| IFEval | — | 83.5% |
Long Context Qwen3 235B-A22B leads
GLM-4.6V: 41.3 (#138), Qwen3 235B-A22B: 46.1 (#25)
| Benchmark | GLM-4.6V | Qwen3 235B-A22B |
|---|---|---|
| LMArena Longer Query | 1358 | 1426 |
| Fiction.LiveBench | — | 75% |
Writing & Preference Qwen3 235B-A22B leads
GLM-4.6V: 56.8 (#132), Qwen3 235B-A22B: 59.8 (#103)
| Benchmark | GLM-4.6V | Qwen3 235B-A22B |
|---|---|---|
| LMArena Text | 1377 | 1419 |
| LMArena Creative Writing | 1347 | 1384 |
| LMArena Multi-Turn | 1359 | 1432 |
| Short-Story Creative Writing | — | 83% |
| EQ-Bench Creative Writing | — | 1366 |
| WildBench | — | 86.6% |
Frequently asked questions
Is GLM-4.6V better than Qwen3 235B-A22B?
Qwen3 235B-A22B is the stronger model overall, scoring 43.2 to 40.9 on the Noometry Index. GLM-4.6V costs 2.7× less per token, which makes it the better buy when Qwen3 235B-A22B's lead doesn't matter for your workload.
Which is cheaper, GLM-4.6V or Qwen3 235B-A22B?
GLM-4.6V is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; Qwen3 235B-A22B lists at $0.70 and $2.80.
Is GLM-4.6V or Qwen3 235B-A22B better for coding?
Qwen3 235B-A22B scores higher on coding benchmarks: 43.8 versus 40.2 in the Noometry coding category.
Which has the bigger context window?
Qwen3 235B-A22B does, with 131K tokens against 128K.
How many benchmarks do GLM-4.6V and Qwen3 235B-A22B share?
11 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and Qwen3 235B-A22B has 49.