Model comparison
GLM-4.7-Flash vs Qwen3.5 27B
Qwen3.5 27B is the stronger model overall, scoring 41.4 to 38.4 on the Noometry Index. GLM-4.7-Flash costs 5.7× less per token, which makes it the better buy when Qwen3.5 27B's lead doesn't matter for your workload.
Last verified . 17 shared benchmarks.
Alibaba (Qwen)Summary
- They share 17 benchmarks with published results for both. GLM-4.7-Flash scores higher in 1 category and Qwen3.5 27B in 7 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Qwen3.5 27B leads 59.5 to 47.7.
- GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $0.30 / $2.40 for Qwen3.5 27B.
- Qwen3.5 27B accepts more context: 262K tokens versus 200K.
Side by side
| GLM-4.7-Flash | Qwen3.5 27B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 38.4 | 41.4 |
| Released | 2026-01-19 | 2026-02-23 |
| Weights | Open | Open |
| Context window | 200K | 262K |
| Max output | 131K | 66K |
| Input $ / M tokens | $0.06 | $0.30 |
| Output $ / M tokens | $0.40 | $2.40 |
| Results tracked | 21 | 28 |
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Category by category
Coding GLM-4.7-Flash leads
GLM-4.7-Flash: 40.0 (#133), Qwen3.5 27B: 38.6 (#159)
| Benchmark | GLM-4.7-Flash | Qwen3.5 27B |
|---|---|---|
| LMArena Coding | 1383 | 1427 |
| LMArena WebDev | — | 1357 |
| WeirdML | — | 39.5% |
| ALE-Bench | — | 349.45 |
Agentic & Tool Use Not comparable
GLM-4.7-Flash: —, Qwen3.5 27B: —
| Benchmark | GLM-4.7-Flash | Qwen3.5 27B |
|---|---|---|
| Vending-Bench 2 | — | 201.98 |
Reasoning Qwen3.5 27B leads
GLM-4.7-Flash: 20.2 (#224), Qwen3.5 27B: 26.6 (#116)
| Benchmark | GLM-4.7-Flash | Qwen3.5 27B |
|---|---|---|
| LMArena Hard Prompts | 1356 | 1414 |
| NYT Connections (extended) | — | 47.9% |
| Chess Puzzles | 0% | — |
| Thematic Generalization | — | 45.5% |
| DTBench | — | 82.4% |
| LMCA | — | 34% |
Math Qwen3.5 27B leads
GLM-4.7-Flash: 34.4 (#159), Qwen3.5 27B: 36.8 (#103)
| Benchmark | GLM-4.7-Flash | Qwen3.5 27B |
|---|---|---|
| LMArena Math | 1354 | 1428 |
| MathArena Final-Answer Competitions | — | 56.7% |
| OTIS Mock AIME 2024-2025 | 58.3% | — |
Knowledge Qwen3.5 27B leads
GLM-4.7-Flash: 35.4 (#177), Qwen3.5 27B: 37.4 (#149)
| Benchmark | GLM-4.7-Flash | Qwen3.5 27B |
|---|---|---|
| Vectara Hallucination Rate | 9.3% | 12.1% |
| LMArena Expert | 1357 | 1428 |
| GPQA Diamond | 60.5% | — |
Multimodal Not comparable
GLM-4.7-Flash: —, Qwen3.5 27B: 38.7 (#56)
| Benchmark | GLM-4.7-Flash | Qwen3.5 27B |
|---|---|---|
| LMArena Vision | — | 1240 |
Multilingual Qwen3.5 27B leads
GLM-4.7-Flash: 46.8 (#152), Qwen3.5 27B: 51.0 (#112)
| Benchmark | GLM-4.7-Flash | Qwen3.5 27B |
|---|---|---|
| LMArena Non-English | 1331 | 1390 |
| LMArena Chinese | 1403 | 1478 |
| LMArena French | 1332 | 1409 |
| LMArena German | 1336 | 1393 |
| LMArena Korean | 1284 | 1359 |
| LMArena Russian | 1333 | 1389 |
| LMArena Spanish | 1351 | 1408 |
| LMArena Japanese | — | 1345 |
Instruction Following Qwen3.5 27B leads
GLM-4.7-Flash: 70.3 (#162), Qwen3.5 27B: 73.7 (#114)
| Benchmark | GLM-4.7-Flash | Qwen3.5 27B |
|---|---|---|
| LMArena Instruction Following | 1327 | 1393 |
Long Context Qwen3.5 27B leads
GLM-4.7-Flash: 40.9 (#143), Qwen3.5 27B: 43.1 (#103)
| Benchmark | GLM-4.7-Flash | Qwen3.5 27B |
|---|---|---|
| LMArena Longer Query | 1345 | 1413 |
Writing & Preference Qwen3.5 27B leads
GLM-4.7-Flash: 47.7 (#206), Qwen3.5 27B: 59.5 (#106)
| Benchmark | GLM-4.7-Flash | Qwen3.5 27B |
|---|---|---|
| LMArena Text | 1351 | 1409 |
| LMArena Creative Writing | 1297 | 1362 |
| LMArena Multi-Turn | 1342 | 1410 |
| EQ-Bench Creative Writing | 1125 | — |
Frequently asked questions
Is GLM-4.7-Flash better than Qwen3.5 27B?
Qwen3.5 27B is the stronger model overall, scoring 41.4 to 38.4 on the Noometry Index. GLM-4.7-Flash costs 5.7× less per token, which makes it the better buy when Qwen3.5 27B's lead doesn't matter for your workload.
Which is cheaper, GLM-4.7-Flash or Qwen3.5 27B?
GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; Qwen3.5 27B lists at $0.30 and $2.40.
Is GLM-4.7-Flash or Qwen3.5 27B better for coding?
GLM-4.7-Flash scores higher on coding benchmarks: 40.0 versus 38.6 in the Noometry coding category.
Which has the bigger context window?
Qwen3.5 27B does, with 262K tokens against 200K.
How many benchmarks do GLM-4.7-Flash and Qwen3.5 27B share?
17 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and Qwen3.5 27B has 28.