Model comparison
DeepSeek-R1 vs DeepSeek-V3.1-Terminus
DeepSeek-R1 and DeepSeek-V3.1-Terminus score almost the same on the Noometry Index (42.3 vs 43.1), so choose on price, context window or the category you care about most.
Last verified . 14 shared benchmarks.
DeepSeekSummary
- They share 14 benchmarks with published results for both. DeepSeek-R1 scores higher in 5 categories and DeepSeek-V3.1-Terminus in 2 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek-V3.1-Terminus leads 26.4 to 18.6.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 69.4% for DeepSeek-R1 and 57.4% for DeepSeek-V3.1-Terminus.
- DeepSeek-V3.1-Terminus is cheaper at $0.27 / $1 per million input/output tokens, against $0.50 / $2.15 for DeepSeek-R1.
- DeepSeek-V3.1-Terminus has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-R1 | DeepSeek-V3.1-Terminus | |
|---|---|---|
| Provider | DeepSeek | DeepSeek |
| Noometry Index | 42.3 | 43.1 |
| Released | 2025-01-20 | 2025-09-22 |
| Weights | Proprietary | Open |
| Context window | 164K | 164K |
| Max output | 64K | 147K |
| Input $ / M tokens | $0.50 | $0.27 |
| Output $ / M tokens | $2.15 | $1 |
| Results tracked | 52 | 16 |
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Category by category
Coding DeepSeek-R1 leads
DeepSeek-R1: 46.3 (#68), DeepSeek-V3.1-Terminus: 42.0 (#113)
| Benchmark | DeepSeek-R1 | DeepSeek-V3.1-Terminus |
|---|---|---|
| SciCode | 35.7% | 40.6% |
| LMArena Coding | 1427 | 1426 |
| ALE-Bench | 804.12 | 745.17 |
| Aider Polyglot | 71.4% | — |
| WeirdML | 41.6% | — |
| LiveBench Coding | 66.7% | — |
| AlgoTune | 1.7 | — |
Agentic & Tool Use Not comparable
DeepSeek-R1: 30.7 (#75), DeepSeek-V3.1-Terminus: —
| Benchmark | DeepSeek-R1 | DeepSeek-V3.1-Terminus |
|---|---|---|
| DeepResearch Bench | 35.1% | — |
| BALROG | 34.9% | — |
| METR Time Horizons | 53.8% | — |
Reasoning DeepSeek-V3.1-Terminus leads
DeepSeek-R1: 18.6 (#278), DeepSeek-V3.1-Terminus: 26.4 (#133)
| Benchmark | DeepSeek-R1 | DeepSeek-V3.1-Terminus |
|---|---|---|
| Kagi LLM Benchmark | 69.4% | 57.4% |
| CritPt | 1.1% | 1.7% |
| LMArena Hard Prompts | 1416 | 1426 |
| ARC-AGI-2 | 1.3% | — |
| SimpleBench | 40.8% | — |
| ARC-AGI-1 | 21.2% | — |
| LiveBench Reasoning | 83.2% | — |
| DTBench | — | 81.3% |
| LiveBench Data Analysis | 69.8% | — |
| LMCA | — | 28.6% |
| Epoch Capabilities Index | 141.29 | — |
| ForecastBench | 60 | — |
| LiveBench | 71.6% | — |
Math DeepSeek-R1 leads
DeepSeek-R1: 43.8 (#79), DeepSeek-V3.1-Terminus: 38.5 (#137)
| Benchmark | DeepSeek-R1 | DeepSeek-V3.1-Terminus |
|---|---|---|
| LMArena Math | 1400 | 1402 |
| OTIS Mock AIME 2024-2025 | 66.4% | — |
| Omni-MATH | 42.4% | — |
| LiveBench Math | 80.7% | — |
| MATH Level 5 | 96.6% | — |
Knowledge Not comparable
DeepSeek-R1: 44.5 (#87), DeepSeek-V3.1-Terminus: —
| Benchmark | DeepSeek-R1 | DeepSeek-V3.1-Terminus |
|---|---|---|
| GPQA Diamond | 76.3% | — |
| MMLU-Pro | 79.3% | — |
| Confabulations | 12.7% | — |
| Vectara Hallucination Rate | 11.3% | — |
| GPQA (HELM) | 66.6% | — |
| LMArena Expert | 1394 | — |
Multilingual Too close to call
DeepSeek-R1: 52.4 (#85), DeepSeek-V3.1-Terminus: 52.1 (#92)
| Benchmark | DeepSeek-R1 | DeepSeek-V3.1-Terminus |
|---|---|---|
| LMArena Non-English | 1412 | 1407 |
| LMArena Russian | 1423 | 1436 |
| LMArena Chinese | 1442 | — |
| LMArena French | 1417 | — |
| LMArena German | 1404 | — |
| LMArena Japanese | 1391 | — |
| LMArena Korean | 1360 | — |
| LMArena Spanish | 1411 | — |
Instruction Following DeepSeek-V3.1-Terminus leads
DeepSeek-R1: 72.0 (#143), DeepSeek-V3.1-Terminus: 74.0 (#106)
| Benchmark | DeepSeek-R1 | DeepSeek-V3.1-Terminus |
|---|---|---|
| LMArena Instruction Following | 1382 | 1404 |
| LiveBench Instruction Following | 80.5% | — |
| IFEval | 78.4% | — |
Long Context DeepSeek-R1 leads
DeepSeek-R1: 45.4 (#36), DeepSeek-V3.1-Terminus: 43.4 (#97)
| Benchmark | DeepSeek-R1 | DeepSeek-V3.1-Terminus |
|---|---|---|
| LMArena Longer Query | 1391 | 1421 |
| Fiction.LiveBench | 75% | — |
Writing & Preference Too close to call
DeepSeek-R1: 61.4 (#88), DeepSeek-V3.1-Terminus: 61.0 (#92)
| Benchmark | DeepSeek-R1 | DeepSeek-V3.1-Terminus |
|---|---|---|
| LMArena Text | 1428 | 1419 |
| LMArena Creative Writing | 1405 | 1403 |
| LMArena Multi-Turn | 1405 | 1411 |
| Short-Story Creative Writing | 83% | — |
| EQ-Bench Creative Writing | 1500 | — |
| WildBench | 82.8% | — |
| LiveBench Language | 48.5% | — |
Frequently asked questions
Is DeepSeek-R1 better than DeepSeek-V3.1-Terminus?
DeepSeek-R1 and DeepSeek-V3.1-Terminus score almost the same on the Noometry Index (42.3 vs 43.1), so choose on price, context window or the category you care about most.
Which is cheaper, DeepSeek-R1 or DeepSeek-V3.1-Terminus?
DeepSeek-V3.1-Terminus is cheaper. It lists at $0.27 per million input tokens and $1 per million output tokens; DeepSeek-R1 lists at $0.50 and $2.15.
Is DeepSeek-R1 or DeepSeek-V3.1-Terminus better for coding?
DeepSeek-R1 scores higher on coding benchmarks: 46.3 versus 42.0 in the Noometry coding category.
Which has the bigger context window?
Both accept 164K tokens.
How many benchmarks do DeepSeek-R1 and DeepSeek-V3.1-Terminus share?
14 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and DeepSeek-V3.1-Terminus has 16.