Model comparison
DeepSeek-V3.1-Terminus vs Llama 3.3 Nemotron 49b Super v1
DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 40.1 on the Noometry Index.
Last verified . 9 shared benchmarks.
DeepSeek
NVIDIASummary
- They share 9 benchmarks with published results for both. DeepSeek-V3.1-Terminus scores higher in 6 categories and Llama 3.3 Nemotron 49b Super v1 in 0 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in multilingual, where DeepSeek-V3.1-Terminus leads 52.1 to 41.1.
Side by side
| DeepSeek-V3.1-Terminus | Llama 3.3 Nemotron 49b Super v1 | |
|---|---|---|
| Provider | DeepSeek | NVIDIA |
| Noometry Index | 43.1 | 40.1 |
| Released | 2025-09-22 | — |
| Weights | Open | Open |
| Context window | 164K | — |
| Max output | 147K | — |
| Input $ / M tokens | $0.27 | — |
| Output $ / M tokens | $1 | — |
| Results tracked | 16 | 10 |
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Category by category
Coding DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 42.0 (#113), Llama 3.3 Nemotron 49b Super v1: 37.9 (#186)
| Benchmark | DeepSeek-V3.1-Terminus | Llama 3.3 Nemotron 49b Super v1 |
|---|---|---|
| LMArena Coding | 1426 | 1296 |
| SciCode | 40.6% | — |
| ALE-Bench | 745.17 | — |
Reasoning Too close to call
DeepSeek-V3.1-Terminus: 26.4 (#133), Llama 3.3 Nemotron 49b Super v1: 26.2 (#135)
| Benchmark | DeepSeek-V3.1-Terminus | Llama 3.3 Nemotron 49b Super v1 |
|---|---|---|
| LMArena Hard Prompts | 1426 | 1311 |
| Kagi LLM Benchmark | 57.4% | — |
| CritPt | 1.7% | — |
| DTBench | 81.3% | — |
| LMCA | 28.6% | — |
Math Not comparable
DeepSeek-V3.1-Terminus: 38.5 (#137), Llama 3.3 Nemotron 49b Super v1: —
| Benchmark | DeepSeek-V3.1-Terminus | Llama 3.3 Nemotron 49b Super v1 |
|---|---|---|
| LMArena Math | 1402 | — |
Multilingual DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 52.1 (#92), Llama 3.3 Nemotron 49b Super v1: 41.1 (#211)
| Benchmark | DeepSeek-V3.1-Terminus | Llama 3.3 Nemotron 49b Super v1 |
|---|---|---|
| LMArena Non-English | 1407 | 1253 |
| LMArena Russian | 1436 | 1269 |
| LMArena Chinese | — | 1277 |
Instruction Following DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 74.0 (#106), Llama 3.3 Nemotron 49b Super v1: 68.3 (#189)
| Benchmark | DeepSeek-V3.1-Terminus | Llama 3.3 Nemotron 49b Super v1 |
|---|---|---|
| LMArena Instruction Following | 1404 | 1293 |
Long Context DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 43.4 (#97), Llama 3.3 Nemotron 49b Super v1: 39.5 (#176)
| Benchmark | DeepSeek-V3.1-Terminus | Llama 3.3 Nemotron 49b Super v1 |
|---|---|---|
| LMArena Longer Query | 1421 | 1299 |
Writing & Preference DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 61.0 (#92), Llama 3.3 Nemotron 49b Super v1: 50.8 (#179)
| Benchmark | DeepSeek-V3.1-Terminus | Llama 3.3 Nemotron 49b Super v1 |
|---|---|---|
| LMArena Text | 1419 | 1308 |
| LMArena Creative Writing | 1403 | 1288 |
| LMArena Multi-Turn | 1411 | 1315 |
Frequently asked questions
Is DeepSeek-V3.1-Terminus better than Llama 3.3 Nemotron 49b Super v1?
DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 40.1 on the Noometry Index.
Is DeepSeek-V3.1-Terminus or Llama 3.3 Nemotron 49b Super v1 better for coding?
DeepSeek-V3.1-Terminus scores higher on coding benchmarks: 42.0 versus 37.9 in the Noometry coding category.
How many benchmarks do DeepSeek-V3.1-Terminus and Llama 3.3 Nemotron 49b Super v1 share?
9 benchmarks have published results for both models. DeepSeek-V3.1-Terminus has 16 scored results on Noometry and Llama 3.3 Nemotron 49b Super v1 has 10.