Model comparison
GLM-4.6 vs Qwen3.7 Flash
GLM-4.6 is the stronger model overall, scoring 41.4 to 39.9 on the Noometry Index. Qwen3.7 Flash costs 18× less per token, which makes it the better buy when GLM-4.6's lead doesn't matter for your workload.
Last verified . 0 shared benchmarks.
Summary
- The widest gap is in knowledge, where Qwen3.7 Flash leads 48.9 to 40.2.
- Qwen3.7 Flash is cheaper at $0.03 / $0.13 per million input/output tokens, against $0.60 / $2.20 for GLM-4.6.
- Qwen3.7 Flash accepts more context: 1M tokens versus 205K.
- GLM-4.6 has downloadable open weights; the other is API-only.
Side by side
| GLM-4.6 | Qwen3.7 Flash | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 41.4 | 39.9 |
| Released | 2025-09-30 | 2026-07-15 |
| Weights | Open | Proprietary |
| Context window | 205K | 1M |
| Max output | 131K | 131K |
| Input $ / M tokens | $0.60 | $0.03 |
| Output $ / M tokens | $2.20 | $0.13 |
| Results tracked | 29 | 7 |
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Category by category
Coding Not comparable
GLM-4.6: 40.1 (#148), Qwen3.7 Flash: —
| Benchmark | GLM-4.6 | Qwen3.7 Flash |
|---|---|---|
| SWE-bench Verified (bash only) | 55.4% | — |
| LMArena WebDev | 1340 | — |
| SciCode | 38.4% | — |
| LMArena Coding | 1449 | — |
| ALE-Bench | 340.82 | — |
Agentic & Tool Use Not comparable
GLM-4.6: 32.3 (#66), Qwen3.7 Flash: —
| Benchmark | GLM-4.6 | Qwen3.7 Flash |
|---|---|---|
| Terminal-Bench | 24.5% | — |
| Berkeley Function Calling Leaderboard | 72.4% | — |
Reasoning Qwen3.7 Flash leads
GLM-4.6: 23.7 (#172), Qwen3.7 Flash: 28.2 (#108)
| Benchmark | GLM-4.6 | Qwen3.7 Flash |
|---|---|---|
| Kagi LLM Benchmark | 47.4% | — |
| NYT Connections (extended) | — | 43.8% |
| CritPt | 1.1% | — |
| Chess Puzzles | — | 23% |
| LMArena Hard Prompts | 1440 | — |
| Mystery Game Puzzles | — | 15% |
| Epoch Capabilities Index | — | 144.64 |
Math Too close to call
GLM-4.6: 39.1 (#111), Qwen3.7 Flash: 38.3 (#140)
| Benchmark | GLM-4.6 | Qwen3.7 Flash |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 19.3% |
| OTIS Mock AIME 2024-2025 | — | 86.7% |
| LMArena Math | 1432 | — |
| FrontierMath (Feb 2025 set) | 3.8% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge Qwen3.7 Flash leads
GLM-4.6: 40.2 (#124), Qwen3.7 Flash: 48.9 (#75)
| Benchmark | GLM-4.6 | Qwen3.7 Flash |
|---|---|---|
| GPQA Diamond | — | 82.3% |
| Vectara Hallucination Rate | 9.5% | — |
| LMArena Expert | 1431 | — |
Multilingual Not comparable
GLM-4.6: 53.5 (#66), Qwen3.7 Flash: —
| Benchmark | GLM-4.6 | Qwen3.7 Flash |
|---|---|---|
| LMArena Non-English | 1426 | — |
| LMArena Chinese | 1499 | — |
| LMArena French | 1459 | — |
| LMArena German | 1447 | — |
| LMArena Japanese | 1393 | — |
| LMArena Korean | 1400 | — |
| LMArena Russian | 1419 | — |
| LMArena Spanish | 1436 | — |
Instruction Following Not comparable
GLM-4.6: 74.3 (#98), Qwen3.7 Flash: —
| Benchmark | GLM-4.6 | Qwen3.7 Flash |
|---|---|---|
| LMArena Instruction Following | 1410 | — |
Long Context Not comparable
GLM-4.6: 43.4 (#94), Qwen3.7 Flash: —
| Benchmark | GLM-4.6 | Qwen3.7 Flash |
|---|---|---|
| LMArena Longer Query | 1422 | — |
Writing & Preference Not comparable
GLM-4.6: 61.1 (#90), Qwen3.7 Flash: —
| Benchmark | GLM-4.6 | Qwen3.7 Flash |
|---|---|---|
| LMArena Text | 1440 | — |
| LMArena Creative Writing | 1411 | — |
| EQ-Bench Creative Writing | 1411 | — |
| LMArena Multi-Turn | 1427 | — |
Frequently asked questions
Is GLM-4.6 better than Qwen3.7 Flash?
GLM-4.6 is the stronger model overall, scoring 41.4 to 39.9 on the Noometry Index. Qwen3.7 Flash costs 18× less per token, which makes it the better buy when GLM-4.6's lead doesn't matter for your workload.
Which is cheaper, GLM-4.6 or Qwen3.7 Flash?
Qwen3.7 Flash is cheaper. It lists at $0.03 per million input tokens and $0.13 per million output tokens; GLM-4.6 lists at $0.60 and $2.20.
Which has the bigger context window?
Qwen3.7 Flash does, with 1M tokens against 205K.
How many benchmarks do GLM-4.6 and Qwen3.7 Flash share?
0 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Qwen3.7 Flash has 7.