Model comparison
DeepSeek V4 Flash vs GLM-5.3
GLM-5.3 is the stronger model overall, scoring 54.8 to 53.6 on the Noometry Index. DeepSeek V4 Flash costs 8.2× less per token, which makes it the better buy when GLM-5.3's lead doesn't matter for your workload.
Last verified . 36 shared benchmarks.
DeepSeekSummary
- They share 36 benchmarks with published results for both. DeepSeek V4 Flash scores higher in 1 category and GLM-5.3 in 7 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-5.3 leads 75.7 to 63.8.
- The biggest single-benchmark swing is FrontierCode: 18.8% for DeepSeek V4 Flash and 40.1% for GLM-5.3.
- DeepSeek V4 Flash is cheaper at $0.15 / $0.60 per million input/output tokens, against $1.40 / $4.40 for GLM-5.3.
Side by side
| DeepSeek V4 Flash | GLM-5.3 | |
|---|---|---|
| Provider | DeepSeek | Z.ai (Zhipu) |
| Noometry Index | 53.6 | 54.8 |
| Released | 2026-04-24 | 2026-08-14 |
| Weights | Open | Open |
| Context window | 1M | 1M |
| Max output | 393K | 131K |
| Input $ / M tokens | $0.15 | $1.40 |
| Output $ / M tokens | $0.60 | $4.40 |
| Results tracked | 41 | 42 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GLM-5.3 leads
DeepSeek V4 Flash: 47.9 (#59), GLM-5.3: 59.5 (#14)
| Benchmark | DeepSeek V4 Flash | GLM-5.3 |
|---|---|---|
| FrontierCode | 18.8% | 40.1% |
| LMArena WebDev | 1582 | 1622 |
| SciCode | 49.9% | 59% |
| WeirdML | 63% | 75.4% |
| LMArena Coding | 1457 | 1496 |
| ALE-Bench | 1,306 | 1,317 |
| DeepSWE | — | 69% |
| CursorBench | — | 42.6% |
| FrontierSWE | — | 30.2% |
Agentic & Tool Use Not comparable
DeepSeek V4 Flash: —, GLM-5.3: 36.4 (#38)
| Benchmark | DeepSeek V4 Flash | GLM-5.3 |
|---|---|---|
| APEX-Agents | — | 56.6% |
| Vending-Bench 2 | — | 8,164 |
Reasoning DeepSeek V4 Flash leads
DeepSeek V4 Flash: 53.7 (#30), GLM-5.3: 46.1 (#46)
| Benchmark | DeepSeek V4 Flash | GLM-5.3 |
|---|---|---|
| NYT Connections (extended) | 89.6% | 74.2% |
| CritPt | 16.6% | 19.1% |
| Chess Puzzles | 33% | 21% |
| LMArena Hard Prompts | 1444 | 1489 |
| Mystery Game Puzzles | 34% | 33% |
| DTBench | 90.9% | 87.7% |
| LMCA | 41.7% | 55.5% |
| Epoch Capabilities Index | 154.49 | 155.61 |
| ARC-AGI-2 | 61.4% | — |
| SimpleBench | 61.1% | — |
| Kagi LLM Benchmark | 52.2% | — |
| ARC-AGI-1 | 89% | — |
| Bench to the Future 3 | — | 0.15 |
Math GLM-5.3 leads
DeepSeek V4 Flash: 60.3 (#37), GLM-5.3: 62.3 (#33)
| Benchmark | DeepSeek V4 Flash | GLM-5.3 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 57.5% | 68.8% |
| FrontierMath Tier 4 | 24.4% | 29.3% |
| OTIS Mock AIME 2024-2025 | 94.4% | 91.1% |
| ProofBench | 56% | 49% |
| LMArena Math | 1427 | 1489 |
| MathArena Final-Answer Competitions | 76.5% | — |
Knowledge GLM-5.3 leads
DeepSeek V4 Flash: 55.4 (#48), GLM-5.3: 58.3 (#37)
| Benchmark | DeepSeek V4 Flash | GLM-5.3 |
|---|---|---|
| GPQA Diamond | 91% | 90.9% |
| SimpleQA Verified | 33.6% | 41% |
| LMArena Expert | 1441 | 1516 |
Multilingual GLM-5.3 leads
DeepSeek V4 Flash: 53.0 (#72), GLM-5.3: 55.7 (#28)
| Benchmark | DeepSeek V4 Flash | GLM-5.3 |
|---|---|---|
| LMArena Non-English | 1420 | 1457 |
| LMArena Chinese | 1468 | 1528 |
| LMArena French | 1439 | 1499 |
| LMArena German | 1418 | 1499 |
| LMArena Japanese | 1406 | 1453 |
| LMArena Korean | 1384 | 1472 |
| LMArena Russian | 1428 | 1463 |
| LMArena Spanish | 1436 | 1460 |
Instruction Following GLM-5.3 leads
DeepSeek V4 Flash: 74.9 (#81), GLM-5.3: 77.5 (#23)
| Benchmark | DeepSeek V4 Flash | GLM-5.3 |
|---|---|---|
| LMArena Instruction Following | 1421 | 1477 |
Long Context GLM-5.3 leads
DeepSeek V4 Flash: 43.8 (#85), GLM-5.3: 45.4 (#41)
| Benchmark | DeepSeek V4 Flash | GLM-5.3 |
|---|---|---|
| LMArena Longer Query | 1434 | 1482 |
Writing & Preference GLM-5.3 leads
DeepSeek V4 Flash: 63.8 (#61), GLM-5.3: 75.7 (#6)
| Benchmark | DeepSeek V4 Flash | GLM-5.3 |
|---|---|---|
| LMArena Text | 1432 | 1471 |
| LMArena Creative Writing | 1403 | 1457 |
| EQ-Bench Creative Writing | 1559 | 2075 |
| LMArena Multi-Turn | 1449 | 1472 |
Frequently asked questions
Is DeepSeek V4 Flash better than GLM-5.3?
GLM-5.3 is the stronger model overall, scoring 54.8 to 53.6 on the Noometry Index. DeepSeek V4 Flash costs 8.2× less per token, which makes it the better buy when GLM-5.3's lead doesn't matter for your workload.
Which is cheaper, DeepSeek V4 Flash or GLM-5.3?
DeepSeek V4 Flash is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; GLM-5.3 lists at $1.40 and $4.40.
Is DeepSeek V4 Flash or GLM-5.3 better for coding?
GLM-5.3 scores higher on coding benchmarks: 59.5 versus 47.9 in the Noometry coding category.
Which has the bigger context window?
Both accept 1M tokens.
How many benchmarks do DeepSeek V4 Flash and GLM-5.3 share?
36 benchmarks have published results for both models. DeepSeek V4 Flash has 41 scored results on Noometry and GLM-5.3 has 42.