Model comparison
DeepSeek-R1-Distill-Llama-70B vs GLM-5.3
GLM-5.3 is the stronger model overall, scoring 54.8 to 37.8 on the Noometry Index.
Last verified . 2 shared benchmarks.
DeepSeekSummary
- They share 2 benchmarks with published results for both. DeepSeek-R1-Distill-Llama-70B scores higher in 0 categories and GLM-5.3 in 6 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-5.3 leads 58.3 to 30.7.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 51.4% for DeepSeek-R1-Distill-Llama-70B and 91.1% for GLM-5.3.
Side by side
| DeepSeek-R1-Distill-Llama-70B | GLM-5.3 | |
|---|---|---|
| Provider | DeepSeek | Z.ai (Zhipu) |
| Noometry Index | 37.8 | 54.8 |
| Released | 2025-01-20 | 2026-08-14 |
| Weights | Open | Open |
| Context window | — | 1M |
| Max output | — | 131K |
| Input $ / M tokens | — | $1.40 |
| Output $ / M tokens | — | $4.40 |
| Results tracked | 13 | 42 |
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Category by category
Coding GLM-5.3 leads
DeepSeek-R1-Distill-Llama-70B: 36.8 (#202), GLM-5.3: 59.5 (#14)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | GLM-5.3 |
|---|---|---|
| DeepSWE | — | 69% |
| FrontierCode | — | 40.1% |
| CursorBench | — | 42.6% |
| LMArena WebDev | — | 1622 |
| FrontierSWE | — | 30.2% |
| SciCode | — | 59% |
| WeirdML | — | 75.4% |
| BigCodeBench Instruct | 35.3% | — |
| LiveBench Coding | 51.6% | — |
| LMArena Coding | — | 1496 |
| BigCodeBench Complete | 49.9% | — |
| ALE-Bench | — | 1,317 |
Agentic & Tool Use Not comparable
DeepSeek-R1-Distill-Llama-70B: —, GLM-5.3: 36.4 (#38)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | GLM-5.3 |
|---|---|---|
| APEX-Agents | — | 56.6% |
| Vending-Bench 2 | — | 8,164 |
Reasoning GLM-5.3 leads
DeepSeek-R1-Distill-Llama-70B: 24.9 (#156), GLM-5.3: 46.1 (#46)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | GLM-5.3 |
|---|---|---|
| Kagi LLM Benchmark | 52.3% | — |
| NYT Connections (extended) | — | 74.2% |
| CritPt | — | 19.1% |
| Chess Puzzles | — | 21% |
| LiveBench Reasoning | 67.6% | — |
| LMArena Hard Prompts | — | 1489 |
| Mystery Game Puzzles | — | 33% |
| DTBench | — | 87.7% |
| LiveBench Data Analysis | 55.9% | — |
| LMCA | — | 55.5% |
| Bench to the Future 3 | — | 0.15 |
| Epoch Capabilities Index | — | 155.61 |
| LiveBench | 54.5% | — |
Math GLM-5.3 leads
DeepSeek-R1-Distill-Llama-70B: 36.0 (#176), GLM-5.3: 62.3 (#33)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | GLM-5.3 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 51.4% | 91.1% |
| FrontierMath (Tiers 1-3) | — | 68.8% |
| FrontierMath Tier 4 | — | 29.3% |
| ProofBench | — | 49% |
| LiveBench Math | 58.1% | — |
| LMArena Math | — | 1489 |
| MATH Level 5 | 89.9% | — |
Knowledge GLM-5.3 leads
DeepSeek-R1-Distill-Llama-70B: 30.7 (#225), GLM-5.3: 58.3 (#37)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | GLM-5.3 |
|---|---|---|
| GPQA Diamond | 55.7% | 90.9% |
| SimpleQA Verified | — | 41% |
| LMArena Expert | — | 1516 |
Multilingual Not comparable
DeepSeek-R1-Distill-Llama-70B: —, GLM-5.3: 55.7 (#28)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | GLM-5.3 |
|---|---|---|
| LMArena Non-English | — | 1457 |
| LMArena Chinese | — | 1528 |
| LMArena French | — | 1499 |
| LMArena German | — | 1499 |
| LMArena Japanese | — | 1453 |
| LMArena Korean | — | 1472 |
| LMArena Russian | — | 1463 |
| LMArena Spanish | — | 1460 |
Instruction Following GLM-5.3 leads
DeepSeek-R1-Distill-Llama-70B: 68.2 (#190), GLM-5.3: 77.5 (#23)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | GLM-5.3 |
|---|---|---|
| LiveBench Instruction Following | 69.9% | — |
| LMArena Instruction Following | — | 1477 |
Long Context Not comparable
DeepSeek-R1-Distill-Llama-70B: —, GLM-5.3: 45.4 (#41)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | GLM-5.3 |
|---|---|---|
| LMArena Longer Query | — | 1482 |
Writing & Preference GLM-5.3 leads
DeepSeek-R1-Distill-Llama-70B: 49.0 (#194), GLM-5.3: 75.7 (#6)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | GLM-5.3 |
|---|---|---|
| LMArena Text | — | 1471 |
| LMArena Creative Writing | — | 1457 |
| EQ-Bench Creative Writing | — | 2075 |
| LMArena Multi-Turn | — | 1472 |
| LiveBench Language | 23.8% | — |
Frequently asked questions
Is DeepSeek-R1-Distill-Llama-70B better than GLM-5.3?
GLM-5.3 is the stronger model overall, scoring 54.8 to 37.8 on the Noometry Index.
Is DeepSeek-R1-Distill-Llama-70B or GLM-5.3 better for coding?
GLM-5.3 scores higher on coding benchmarks: 59.5 versus 36.8 in the Noometry coding category.
How many benchmarks do DeepSeek-R1-Distill-Llama-70B and GLM-5.3 share?
2 benchmarks have published results for both models. DeepSeek-R1-Distill-Llama-70B has 13 scored results on Noometry and GLM-5.3 has 42.