Model comparison
DeepSeek V4 Pro vs GLM-5.3-Flash
DeepSeek V4 Pro is the stronger model overall, scoring 54.3 to 51.8 on the Noometry Index. GLM-5.3-Flash costs 4.2× less per token, which makes it the better buy when DeepSeek V4 Pro's lead doesn't matter for your workload.
Last verified . 34 shared benchmarks.
DeepSeekSummary
- They share 34 benchmarks with published results for both. DeepSeek V4 Pro scores higher in 4 categories and GLM-5.3-Flash in 5 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in math, where DeepSeek V4 Pro leads 64.8 to 53.3.
- The biggest single-benchmark swing is Mystery Game Puzzles: 43% for DeepSeek V4 Pro and 8% for GLM-5.3-Flash.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $0.66 / $1.98 for DeepSeek V4 Pro.
Side by side
| DeepSeek V4 Pro | GLM-5.3-Flash | |
|---|---|---|
| Provider | DeepSeek | Z.ai (Zhipu) |
| Noometry Index | 54.3 | 51.8 |
| Released | 2026-04-24 | 2026-08-20 |
| Weights | Open | Open |
| Context window | 1M | 1M |
| Max output | 393K | 131K |
| Input $ / M tokens | $0.66 | $0.15 |
| Output $ / M tokens | $1.98 | $0.50 |
| Results tracked | 48 | 40 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Too close to call
DeepSeek V4 Pro: 52.4 (#34), GLM-5.3-Flash: 53.1 (#31)
| Benchmark | DeepSeek V4 Pro | GLM-5.3-Flash |
|---|---|---|
| FrontierCode | 28.6% | 31.8% |
| LMArena WebDev | 1582 | 1609 |
| SciCode | 51% | 51.6% |
| LMArena Coding | 1470 | 1508 |
| ALE-Bench | 1,403 | 303.55 |
| SWE-bench Verified | 77.6% | — |
| DeepSWE | — | 63.4% |
| CursorBench | — | 36.8% |
| FrontierSWE | — | 18.1% |
| WeirdML | 66.2% | — |
Agentic & Tool Use GLM-5.3-Flash leads
DeepSeek V4 Pro: 32.8 (#58), GLM-5.3-Flash: 34.2 (#47)
| Benchmark | DeepSeek V4 Pro | GLM-5.3-Flash |
|---|---|---|
| APEX-Agents | 47.3% | 52.8% |
| GDP.pdf | — | 14% |
| Vending-Bench 2 | 3,285 | — |
Reasoning DeepSeek V4 Pro leads
DeepSeek V4 Pro: 56.5 (#24), GLM-5.3-Flash: 48.0 (#42)
| Benchmark | DeepSeek V4 Pro | GLM-5.3-Flash |
|---|---|---|
| ARC-AGI-2 | 61.3% | 65.8% |
| ARC-AGI-1 | 90.5% | 91% |
| CritPt | 18% | 15.4% |
| Chess Puzzles | 47% | 14% |
| LMArena Hard Prompts | 1461 | 1491 |
| Mystery Game Puzzles | 43% | 8% |
| Surface Evolver Bench | 40% | 52.5% |
| Epoch Capabilities Index | 155.31 | 151.88 |
| Kagi LLM Benchmark | 53.5% | — |
| NYT Connections (extended) | 91.3% | — |
| DTBench | 93.9% | — |
| LMCA | 45.5% | — |
| Bench to the Future 3 | — | 0.15 |
| ForecastBench | 56.1 | — |
Math DeepSeek V4 Pro leads
DeepSeek V4 Pro: 64.8 (#30), GLM-5.3-Flash: 53.3 (#47)
| Benchmark | DeepSeek V4 Pro | GLM-5.3-Flash |
|---|---|---|
| FrontierMath (Tiers 1-3) | 64.6% | 55.8% |
| FrontierMath Tier 4 | 26.8% | 17.1% |
| OTIS Mock AIME 2024-2025 | 98.6% | 93.9% |
| ProofBench | 50% | 21% |
| LMArena Math | 1455 | 1500 |
| MathArena Final-Answer Competitions | 76.6% | — |
Knowledge DeepSeek V4 Pro leads
DeepSeek V4 Pro: 59.5 (#31), GLM-5.3-Flash: 58.4 (#36)
| Benchmark | DeepSeek V4 Pro | GLM-5.3-Flash |
|---|---|---|
| GPQA Diamond | 91.7% | 90.2% |
| LMArena Expert | 1464 | 1513 |
| SimpleQA Verified | 52.9% | — |
| Vectara Hallucination Rate | 8.6% | — |
Multimodal Not comparable
DeepSeek V4 Pro: —, GLM-5.3-Flash: 42.8 (#27)
| Benchmark | DeepSeek V4 Pro | GLM-5.3-Flash |
|---|---|---|
| LMArena Vision | — | 1296 |
Multilingual GLM-5.3-Flash leads
DeepSeek V4 Pro: 54.4 (#45), GLM-5.3-Flash: 56.0 (#25)
| Benchmark | DeepSeek V4 Pro | GLM-5.3-Flash |
|---|---|---|
| LMArena Non-English | 1439 | 1462 |
| LMArena Chinese | 1486 | 1527 |
| LMArena French | 1472 | 1496 |
| LMArena German | 1458 | 1470 |
| LMArena Japanese | 1445 | 1429 |
| LMArena Korean | 1447 | 1446 |
| LMArena Russian | 1453 | 1469 |
| LMArena Spanish | 1458 | 1471 |
Instruction Following GLM-5.3-Flash leads
DeepSeek V4 Pro: 76.1 (#47), GLM-5.3-Flash: 77.5 (#20)
| Benchmark | DeepSeek V4 Pro | GLM-5.3-Flash |
|---|---|---|
| LMArena Instruction Following | 1448 | 1478 |
Long Context Too close to call
DeepSeek V4 Pro: 45.0 (#51), GLM-5.3-Flash: 45.4 (#39)
| Benchmark | DeepSeek V4 Pro | GLM-5.3-Flash |
|---|---|---|
| LMArena Longer Query | 1458 | 1482 |
| CL-bench Life | 13.5% | — |
Writing & Preference Too close to call
DeepSeek V4 Pro: 65.5 (#46), GLM-5.3-Flash: 65.3 (#50)
| Benchmark | DeepSeek V4 Pro | GLM-5.3-Flash |
|---|---|---|
| LMArena Text | 1451 | 1471 |
| LMArena Creative Writing | 1446 | 1442 |
| LMArena Multi-Turn | 1467 | 1467 |
| EQ-Bench Creative Writing | 1553 | — |
| EQ-Bench 4 | 1166 | — |
Frequently asked questions
Is DeepSeek V4 Pro better than GLM-5.3-Flash?
DeepSeek V4 Pro is the stronger model overall, scoring 54.3 to 51.8 on the Noometry Index. GLM-5.3-Flash costs 4.2× less per token, which makes it the better buy when DeepSeek V4 Pro's lead doesn't matter for your workload.
Which is cheaper, DeepSeek V4 Pro or GLM-5.3-Flash?
GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; DeepSeek V4 Pro lists at $0.66 and $1.98.
Is DeepSeek V4 Pro or GLM-5.3-Flash better for coding?
They score almost the same on coding (52.4 vs 53.1); test both on your own repository before choosing.
Which has the bigger context window?
Both accept 1M tokens.
How many benchmarks do DeepSeek V4 Pro and GLM-5.3-Flash share?
34 benchmarks have published results for both models. DeepSeek V4 Pro has 48 scored results on Noometry and GLM-5.3-Flash has 40.