Model comparison
DeepSeek V4 Pro vs GLM-5.3
DeepSeek V4 Pro and GLM-5.3 score almost the same on the Noometry Index (54.3 vs 54.8), so choose on price, context window or the category you care about most.
Last verified . 38 shared benchmarks.
DeepSeekSummary
- They share 38 benchmarks with published results for both. DeepSeek V4 Pro scores higher in 3 categories and GLM-5.3 in 6 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek V4 Pro leads 56.5 to 46.1.
- The biggest single-benchmark swing is Chess Puzzles: 47% for DeepSeek V4 Pro and 21% for GLM-5.3.
- DeepSeek V4 Pro is cheaper at $0.66 / $1.98 per million input/output tokens, against $1.40 / $4.40 for GLM-5.3.
Side by side
| DeepSeek V4 Pro | GLM-5.3 | |
|---|---|---|
| Provider | DeepSeek | Z.ai (Zhipu) |
| Noometry Index | 54.3 | 54.8 |
| Released | 2026-04-24 | 2026-08-14 |
| Weights | Open | Open |
| Context window | 1M | 1M |
| Max output | 393K | 131K |
| Input $ / M tokens | $0.66 | $1.40 |
| Output $ / M tokens | $1.98 | $4.40 |
| Results tracked | 48 | 42 |
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Category by category
Coding GLM-5.3 leads
DeepSeek V4 Pro: 52.4 (#34), GLM-5.3: 59.5 (#14)
| Benchmark | DeepSeek V4 Pro | GLM-5.3 |
|---|---|---|
| FrontierCode | 28.6% | 40.1% |
| LMArena WebDev | 1582 | 1622 |
| SciCode | 51% | 59% |
| WeirdML | 66.2% | 75.4% |
| LMArena Coding | 1470 | 1496 |
| ALE-Bench | 1,403 | 1,317 |
| SWE-bench Verified | 77.6% | — |
| DeepSWE | — | 69% |
| CursorBench | — | 42.6% |
| FrontierSWE | — | 30.2% |
Agentic & Tool Use GLM-5.3 leads
DeepSeek V4 Pro: 32.8 (#58), GLM-5.3: 36.4 (#38)
| Benchmark | DeepSeek V4 Pro | GLM-5.3 |
|---|---|---|
| APEX-Agents | 47.3% | 56.6% |
| Vending-Bench 2 | 3,285 | 8,164 |
Reasoning DeepSeek V4 Pro leads
DeepSeek V4 Pro: 56.5 (#24), GLM-5.3: 46.1 (#46)
| Benchmark | DeepSeek V4 Pro | GLM-5.3 |
|---|---|---|
| NYT Connections (extended) | 91.3% | 74.2% |
| CritPt | 18% | 19.1% |
| Chess Puzzles | 47% | 21% |
| LMArena Hard Prompts | 1461 | 1489 |
| Mystery Game Puzzles | 43% | 33% |
| DTBench | 93.9% | 87.7% |
| LMCA | 45.5% | 55.5% |
| Epoch Capabilities Index | 155.31 | 155.61 |
| ARC-AGI-2 | 61.3% | — |
| Kagi LLM Benchmark | 53.5% | — |
| ARC-AGI-1 | 90.5% | — |
| Surface Evolver Bench | 40% | — |
| Bench to the Future 3 | — | 0.15 |
| ForecastBench | 56.1 | — |
Math DeepSeek V4 Pro leads
DeepSeek V4 Pro: 64.8 (#30), GLM-5.3: 62.3 (#33)
| Benchmark | DeepSeek V4 Pro | GLM-5.3 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 64.6% | 68.8% |
| FrontierMath Tier 4 | 26.8% | 29.3% |
| OTIS Mock AIME 2024-2025 | 98.6% | 91.1% |
| ProofBench | 50% | 49% |
| LMArena Math | 1455 | 1489 |
| MathArena Final-Answer Competitions | 76.6% | — |
Knowledge DeepSeek V4 Pro leads
DeepSeek V4 Pro: 59.5 (#31), GLM-5.3: 58.3 (#37)
| Benchmark | DeepSeek V4 Pro | GLM-5.3 |
|---|---|---|
| GPQA Diamond | 91.7% | 90.9% |
| SimpleQA Verified | 52.9% | 41% |
| LMArena Expert | 1464 | 1516 |
| Vectara Hallucination Rate | 8.6% | — |
Multilingual GLM-5.3 leads
DeepSeek V4 Pro: 54.4 (#45), GLM-5.3: 55.7 (#28)
| Benchmark | DeepSeek V4 Pro | GLM-5.3 |
|---|---|---|
| LMArena Non-English | 1439 | 1457 |
| LMArena Chinese | 1486 | 1528 |
| LMArena French | 1472 | 1499 |
| LMArena German | 1458 | 1499 |
| LMArena Japanese | 1445 | 1453 |
| LMArena Korean | 1447 | 1472 |
| LMArena Russian | 1453 | 1463 |
| LMArena Spanish | 1458 | 1460 |
Instruction Following GLM-5.3 leads
DeepSeek V4 Pro: 76.1 (#47), GLM-5.3: 77.5 (#23)
| Benchmark | DeepSeek V4 Pro | GLM-5.3 |
|---|---|---|
| LMArena Instruction Following | 1448 | 1477 |
Long Context Too close to call
DeepSeek V4 Pro: 45.0 (#51), GLM-5.3: 45.4 (#41)
| Benchmark | DeepSeek V4 Pro | GLM-5.3 |
|---|---|---|
| LMArena Longer Query | 1458 | 1482 |
| CL-bench Life | 13.5% | — |
Writing & Preference GLM-5.3 leads
DeepSeek V4 Pro: 65.5 (#46), GLM-5.3: 75.7 (#6)
| Benchmark | DeepSeek V4 Pro | GLM-5.3 |
|---|---|---|
| LMArena Text | 1451 | 1471 |
| LMArena Creative Writing | 1446 | 1457 |
| EQ-Bench Creative Writing | 1553 | 2075 |
| LMArena Multi-Turn | 1467 | 1472 |
| EQ-Bench 4 | 1166 | — |
Frequently asked questions
Is DeepSeek V4 Pro better than GLM-5.3?
DeepSeek V4 Pro and GLM-5.3 score almost the same on the Noometry Index (54.3 vs 54.8), so choose on price, context window or the category you care about most.
Which is cheaper, DeepSeek V4 Pro or GLM-5.3?
DeepSeek V4 Pro is cheaper. It lists at $0.66 per million input tokens and $1.98 per million output tokens; GLM-5.3 lists at $1.40 and $4.40.
Is DeepSeek V4 Pro or GLM-5.3 better for coding?
GLM-5.3 scores higher on coding benchmarks: 59.5 versus 52.4 in the Noometry coding category.
Which has the bigger context window?
Both accept 1M tokens.
How many benchmarks do DeepSeek V4 Pro and GLM-5.3 share?
38 benchmarks have published results for both models. DeepSeek V4 Pro has 48 scored results on Noometry and GLM-5.3 has 42.