Model comparison
DeepSeek-R1 vs Llama-3.3-70B-Instruct
DeepSeek-R1 is the stronger model overall, scoring 42.3 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 5.9× less per token, which makes it the better buy when DeepSeek-R1's lead doesn't matter for your workload.
Last verified . 37 shared benchmarks.
DeepSeek
MetaSummary
- They share 37 benchmarks with published results for both. DeepSeek-R1 scores higher in 9 categories and Llama-3.3-70B-Instruct in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where DeepSeek-R1 leads 43.8 to 15.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 66.4% for DeepSeek-R1 and 5.1% for Llama-3.3-70B-Instruct.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $0.50 / $2.15 for DeepSeek-R1.
- DeepSeek-R1 accepts more context: 164K tokens versus 128K.
- Llama-3.3-70B-Instruct has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-R1 | Llama-3.3-70B-Instruct | |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 42.3 | 30.6 |
| Released | 2025-01-20 | 2024-12-06 |
| Weights | Proprietary | Open |
| Context window | 164K | 128K |
| Max output | 64K | 4K |
| Input $ / M tokens | $0.50 | $0.10 |
| Output $ / M tokens | $2.15 | $0.32 |
| Results tracked | 52 | 43 |
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Category by category
Coding DeepSeek-R1 leads
DeepSeek-R1: 46.3 (#68), Llama-3.3-70B-Instruct: 31.0 (#290)
| Benchmark | DeepSeek-R1 | Llama-3.3-70B-Instruct |
|---|---|---|
| SciCode | 35.7% | 26% |
| WeirdML | 41.6% | 14.4% |
| LiveBench Coding | 66.7% | 36.6% |
| LMArena Coding | 1427 | 1268 |
| Aider Polyglot | 71.4% | — |
| BigCodeBench Instruct | — | 46.9% |
| BigCodeBench Complete | — | 57.5% |
| ALE-Bench | 804.12 | — |
| AlgoTune | 1.7 | — |
Agentic & Tool Use DeepSeek-R1 leads
DeepSeek-R1: 30.7 (#75), Llama-3.3-70B-Instruct: 25.8 (#105)
| Benchmark | DeepSeek-R1 | Llama-3.3-70B-Instruct |
|---|---|---|
| BALROG | 34.9% | 23% |
| Berkeley Function Calling Leaderboard | — | 31.9% |
| DeepResearch Bench | 35.1% | — |
| METR Time Horizons | 53.8% | — |
Reasoning DeepSeek-R1 leads
DeepSeek-R1: 18.6 (#278), Llama-3.3-70B-Instruct: 14.1 (#327)
| Benchmark | DeepSeek-R1 | Llama-3.3-70B-Instruct |
|---|---|---|
| SimpleBench | 40.8% | 19.9% |
| CritPt | 1.1% | 0% |
| LiveBench Reasoning | 83.2% | 50.8% |
| LMArena Hard Prompts | 1416 | 1257 |
| LiveBench Data Analysis | 69.8% | 49.5% |
| Epoch Capabilities Index | 141.29 | 127.33 |
| ForecastBench | 60 | 58.6 |
| LiveBench | 71.6% | 50.2% |
| ARC-AGI-2 | 1.3% | — |
| Kagi LLM Benchmark | 69.4% | — |
| ARC-AGI-1 | 21.2% | — |
| DTBench | — | 59.5% |
| LMCA | — | 17.5% |
Math DeepSeek-R1 leads
DeepSeek-R1: 43.8 (#79), Llama-3.3-70B-Instruct: 15.3 (#298)
| Benchmark | DeepSeek-R1 | Llama-3.3-70B-Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 66.4% | 5.1% |
| LiveBench Math | 80.7% | 42.2% |
| LMArena Math | 1400 | 1267 |
| MATH Level 5 | 96.6% | 41.6% |
| Omni-MATH | 42.4% | — |
Knowledge DeepSeek-R1 leads
DeepSeek-R1: 44.5 (#87), Llama-3.3-70B-Instruct: 30.6 (#226)
| Benchmark | DeepSeek-R1 | Llama-3.3-70B-Instruct |
|---|---|---|
| GPQA Diamond | 76.3% | 47.4% |
| Confabulations | 12.7% | 22.8% |
| Vectara Hallucination Rate | 11.3% | 4.1% |
| LMArena Expert | 1394 | 1225 |
| MMLU-Pro | 79.3% | — |
| GPQA (HELM) | 66.6% | — |
| MMLU | — | 86.3% |
Multilingual DeepSeek-R1 leads
DeepSeek-R1: 52.4 (#85), Llama-3.3-70B-Instruct: 39.9 (#220)
| Benchmark | DeepSeek-R1 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | 1412 | 1236 |
| LMArena Chinese | 1442 | 1217 |
| LMArena French | 1417 | 1281 |
| LMArena German | 1404 | 1251 |
| LMArena Japanese | 1391 | 1150 |
| LMArena Korean | 1360 | 1143 |
| LMArena Russian | 1423 | 1252 |
| LMArena Spanish | 1411 | 1270 |
Instruction Following Too close to call
DeepSeek-R1: 72.0 (#143), Llama-3.3-70B-Instruct: 71.1 (#157)
| Benchmark | DeepSeek-R1 | Llama-3.3-70B-Instruct |
|---|---|---|
| LiveBench Instruction Following | 80.5% | 82.7% |
| LMArena Instruction Following | 1382 | 1242 |
| IFEval | 78.4% | — |
Long Context DeepSeek-R1 leads
DeepSeek-R1: 45.4 (#36), Llama-3.3-70B-Instruct: 26.4 (#295)
| Benchmark | DeepSeek-R1 | Llama-3.3-70B-Instruct |
|---|---|---|
| Fiction.LiveBench | 75% | 33.3% |
| LMArena Longer Query | 1391 | 1256 |
Writing & Preference DeepSeek-R1 leads
DeepSeek-R1: 61.4 (#88), Llama-3.3-70B-Instruct: 47.6 (#207)
| Benchmark | DeepSeek-R1 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | 1428 | 1274 |
| LMArena Creative Writing | 1405 | 1250 |
| LMArena Multi-Turn | 1405 | 1280 |
| LiveBench Language | 48.5% | 39.2% |
| Short-Story Creative Writing | 83% | — |
| EQ-Bench Creative Writing | 1500 | — |
| WildBench | 82.8% | — |
Frequently asked questions
Is DeepSeek-R1 better than Llama-3.3-70B-Instruct?
DeepSeek-R1 is the stronger model overall, scoring 42.3 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 5.9× less per token, which makes it the better buy when DeepSeek-R1's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-R1 or Llama-3.3-70B-Instruct?
Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; DeepSeek-R1 lists at $0.50 and $2.15.
Is DeepSeek-R1 or Llama-3.3-70B-Instruct better for coding?
DeepSeek-R1 scores higher on coding benchmarks: 46.3 versus 31.0 in the Noometry coding category.
Which has the bigger context window?
DeepSeek-R1 does, with 164K tokens against 128K.
How many benchmarks do DeepSeek-R1 and Llama-3.3-70B-Instruct share?
37 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and Llama-3.3-70B-Instruct has 43.