Model comparison
DeepSeek V4 Flash vs GLM-5V-Turbo
DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 43.8 on the Noometry Index.
Last verified . 17 shared benchmarks.
DeepSeekSummary
- They share 17 benchmarks with published results for both. DeepSeek V4 Flash scores higher in 5 categories and GLM-5V-Turbo in 3 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek V4 Flash leads 53.7 to 29.7.
- DeepSeek V4 Flash is cheaper at $0.15 / $0.60 per million input/output tokens, against $1.20 / $4 for GLM-5V-Turbo.
- DeepSeek V4 Flash accepts more context: 1M tokens versus 200K.
- DeepSeek V4 Flash has downloadable open weights; the other is API-only.
Side by side
| DeepSeek V4 Flash | GLM-5V-Turbo | |
|---|---|---|
| Provider | DeepSeek | Z.ai (Zhipu) |
| Noometry Index | 53.6 | 43.8 |
| Released | 2026-04-24 | 2026-04-01 |
| Weights | Open | Proprietary |
| Context window | 1M | 200K |
| Max output | 393K | 131K |
| Input $ / M tokens | $0.15 | $1.20 |
| Output $ / M tokens | $0.60 | $4 |
| Results tracked | 41 | 19 |
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Category by category
Coding DeepSeek V4 Flash leads
DeepSeek V4 Flash: 47.9 (#59), GLM-5V-Turbo: 42.1 (#111)
| Benchmark | DeepSeek V4 Flash | GLM-5V-Turbo |
|---|---|---|
| LMArena WebDev | 1582 | 1401 |
| LMArena Coding | 1457 | 1466 |
| FrontierCode | 18.8% | — |
| SciCode | 49.9% | — |
| WeirdML | 63% | — |
| ALE-Bench | 1,306 | — |
Reasoning DeepSeek V4 Flash leads
DeepSeek V4 Flash: 53.7 (#30), GLM-5V-Turbo: 29.7 (#89)
| Benchmark | DeepSeek V4 Flash | GLM-5V-Turbo |
|---|---|---|
| LMArena Hard Prompts | 1444 | 1443 |
| ARC-AGI-2 | 61.4% | — |
| SimpleBench | 61.1% | — |
| Kagi LLM Benchmark | 52.2% | — |
| NYT Connections (extended) | 89.6% | — |
| ARC-AGI-1 | 89% | — |
| CritPt | 16.6% | — |
| Chess Puzzles | 33% | — |
| Mystery Game Puzzles | 34% | — |
| DTBench | 90.9% | — |
| LMCA | 41.7% | — |
| Epoch Capabilities Index | 154.49 | — |
Math DeepSeek V4 Flash leads
DeepSeek V4 Flash: 60.3 (#37), GLM-5V-Turbo: 39.4 (#106)
| Benchmark | DeepSeek V4 Flash | GLM-5V-Turbo |
|---|---|---|
| LMArena Math | 1427 | 1441 |
| FrontierMath (Tiers 1-3) | 57.5% | — |
| FrontierMath Tier 4 | 24.4% | — |
| MathArena Final-Answer Competitions | 76.5% | — |
| OTIS Mock AIME 2024-2025 | 94.4% | — |
| ProofBench | 56% | — |
Knowledge DeepSeek V4 Flash leads
DeepSeek V4 Flash: 55.4 (#48), GLM-5V-Turbo: 40.6 (#117)
| Benchmark | DeepSeek V4 Flash | GLM-5V-Turbo |
|---|---|---|
| LMArena Expert | 1441 | 1452 |
| GPQA Diamond | 91% | — |
| SimpleQA Verified | 33.6% | — |
Multimodal Not comparable
DeepSeek V4 Flash: —, GLM-5V-Turbo: 40.9 (#42)
| Benchmark | DeepSeek V4 Flash | GLM-5V-Turbo |
|---|---|---|
| LMArena Vision | — | 1264 |
| LMArena Document | — | 1416 |
Multilingual Too close to call
DeepSeek V4 Flash: 53.0 (#72), GLM-5V-Turbo: 53.0 (#73)
| Benchmark | DeepSeek V4 Flash | GLM-5V-Turbo |
|---|---|---|
| LMArena Non-English | 1420 | 1420 |
| LMArena Chinese | 1468 | 1488 |
| LMArena French | 1439 | 1444 |
| LMArena German | 1418 | 1423 |
| LMArena Korean | 1384 | 1396 |
| LMArena Russian | 1428 | 1431 |
| LMArena Spanish | 1436 | 1450 |
| LMArena Japanese | 1406 | — |
Instruction Following Too close to call
DeepSeek V4 Flash: 74.9 (#81), GLM-5V-Turbo: 75.0 (#80)
| Benchmark | DeepSeek V4 Flash | GLM-5V-Turbo |
|---|---|---|
| LMArena Instruction Following | 1421 | 1423 |
Long Context Too close to call
DeepSeek V4 Flash: 43.8 (#85), GLM-5V-Turbo: 44.0 (#80)
| Benchmark | DeepSeek V4 Flash | GLM-5V-Turbo |
|---|---|---|
| LMArena Longer Query | 1434 | 1438 |
Writing & Preference DeepSeek V4 Flash leads
DeepSeek V4 Flash: 63.8 (#61), GLM-5V-Turbo: 62.5 (#73)
| Benchmark | DeepSeek V4 Flash | GLM-5V-Turbo |
|---|---|---|
| LMArena Text | 1432 | 1437 |
| LMArena Creative Writing | 1403 | 1416 |
| LMArena Multi-Turn | 1449 | 1432 |
| EQ-Bench Creative Writing | 1559 | — |
Frequently asked questions
Is DeepSeek V4 Flash better than GLM-5V-Turbo?
DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 43.8 on the Noometry Index.
Which is cheaper, DeepSeek V4 Flash or GLM-5V-Turbo?
DeepSeek V4 Flash is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; GLM-5V-Turbo lists at $1.20 and $4.
Is DeepSeek V4 Flash or GLM-5V-Turbo better for coding?
DeepSeek V4 Flash scores higher on coding benchmarks: 47.9 versus 42.1 in the Noometry coding category.
Which has the bigger context window?
DeepSeek V4 Flash does, with 1M tokens against 200K.
How many benchmarks do DeepSeek V4 Flash and GLM-5V-Turbo share?
17 benchmarks have published results for both models. DeepSeek V4 Flash has 41 scored results on Noometry and GLM-5V-Turbo has 19.