Model comparison
GLM-4.7-Flash vs Qwen3.8 27B
Qwen3.8 27B is the stronger model overall, scoring 46.0 to 38.8 on the Noometry Index. GLM-4.7-Flash costs 7.7× less per token, which makes it the better buy when Qwen3.8 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 0 categories and Qwen3.8 27B in 8 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Qwen3.8 27B leads 41.0 to 20.9.
- GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $0.99 / $1.49 for Qwen3.8 27B.
- Qwen3.8 27B accepts more context: 262K tokens versus 200K.
Side by side
| GLM-4.7-Flash | Qwen3.8 27B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 38.8 | 46.0 |
| Released | 2026-01-19 | 2026-08-14 |
| Weights | Open | Open |
| Context window | 200K | 262K |
| Max output | 131K | 33K |
| Input $ / M tokens | $0.06 | $0.99 |
| Output $ / M tokens | $0.40 | $1.49 |
| Results tracked | 21 | 31 |
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Category by category
Coding Qwen3.8 27B leads
GLM-4.7-Flash: 40.6 (#135), Qwen3.8 27B: 50.5 (#44)
| Benchmark | GLM-4.7-Flash | Qwen3.8 27B |
|---|---|---|
| LMArena Coding | 1383 | 1482 |
| LMArena WebDev | — | 1593 |
| SciCode | — | 46.6% |
Agentic & Tool Use Not comparable
GLM-4.7-Flash: —, Qwen3.8 27B: 32.9 (#57)
| Benchmark | GLM-4.7-Flash | Qwen3.8 27B |
|---|---|---|
| APEX-Agents | — | 47.5% |
Reasoning Qwen3.8 27B leads
GLM-4.7-Flash: 20.9 (#229), Qwen3.8 27B: 41.0 (#54)
| Benchmark | GLM-4.7-Flash | Qwen3.8 27B |
|---|---|---|
| LMArena Hard Prompts | 1356 | 1460 |
| ARC-AGI-2 | — | 42.4% |
| NYT Connections (extended) | — | 54.5% |
| ARC-AGI-1 | — | 87.5% |
| CritPt | — | 5.4% |
| Chess Puzzles | 0% | — |
| DTBench | — | 88% |
| LMCA | — | 41.4% |
| Surface Evolver Bench | — | 45% |
| Epoch Capabilities Index | — | 149.38 |
Math Too close to call
GLM-4.7-Flash: 36.1 (#173), Qwen3.8 27B: 37.1 (#161)
| Benchmark | GLM-4.7-Flash | Qwen3.8 27B |
|---|---|---|
| LMArena Math | 1355 | 1456 |
| OTIS Mock AIME 2024-2025 | 58.3% | — |
| ProofBench | — | 16% |
Knowledge Qwen3.8 27B leads
GLM-4.7-Flash: 35.5 (#184), Qwen3.8 27B: 41.6 (#109)
| Benchmark | GLM-4.7-Flash | Qwen3.8 27B |
|---|---|---|
| LMArena Expert | 1357 | 1482 |
| GPQA Diamond | 60.5% | — |
| Vectara Hallucination Rate | 9.3% | — |
Multimodal Not comparable
GLM-4.7-Flash: —, Qwen3.8 27B: 41.3 (#37)
| Benchmark | GLM-4.7-Flash | Qwen3.8 27B |
|---|---|---|
| LMArena Vision | — | 1271 |
Multilingual Qwen3.8 27B leads
GLM-4.7-Flash: 46.5 (#158), Qwen3.8 27B: 53.7 (#60)
| Benchmark | GLM-4.7-Flash | Qwen3.8 27B |
|---|---|---|
| LMArena Non-English | 1330 | 1430 |
| LMArena Chinese | 1403 | 1504 |
| LMArena French | 1332 | 1465 |
| LMArena German | 1337 | 1438 |
| LMArena Korean | 1283 | 1393 |
| LMArena Russian | 1332 | 1415 |
| LMArena Spanish | 1350 | 1448 |
| LMArena Japanese | — | 1384 |
Instruction Following Qwen3.8 27B leads
GLM-4.7-Flash: 70.1 (#167), Qwen3.8 27B: 75.8 (#53)
| Benchmark | GLM-4.7-Flash | Qwen3.8 27B |
|---|---|---|
| LMArena Instruction Following | 1327 | 1439 |
Long Context Qwen3.8 27B leads
GLM-4.7-Flash: 40.9 (#148), Qwen3.8 27B: 44.3 (#70)
| Benchmark | GLM-4.7-Flash | Qwen3.8 27B |
|---|---|---|
| LMArena Longer Query | 1345 | 1450 |
Writing & Preference Qwen3.8 27B leads
GLM-4.7-Flash: 47.4 (#210), Qwen3.8 27B: 65.8 (#43)
| Benchmark | GLM-4.7-Flash | Qwen3.8 27B |
|---|---|---|
| LMArena Text | 1351 | 1441 |
| LMArena Creative Writing | 1297 | 1384 |
| EQ-Bench Creative Writing | 1125 | 1671 |
| LMArena Multi-Turn | 1342 | 1441 |
Frequently asked questions
Is GLM-4.7-Flash better than Qwen3.8 27B?
Qwen3.8 27B is the stronger model overall, scoring 46.0 to 38.8 on the Noometry Index. GLM-4.7-Flash costs 7.7× less per token, which makes it the better buy when Qwen3.8 27B's lead doesn't matter for your workload.
Which is cheaper, GLM-4.7-Flash or Qwen3.8 27B?
GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; Qwen3.8 27B lists at $0.99 and $1.49.
Is GLM-4.7-Flash or Qwen3.8 27B better for coding?
Qwen3.8 27B scores higher on coding benchmarks: 50.5 versus 40.6 in the Noometry coding category.
Which has the bigger context window?
Qwen3.8 27B does, with 262K tokens against 200K.
How many benchmarks do GLM-4.7-Flash and Qwen3.8 27B share?
17 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and Qwen3.8 27B has 31.