Model comparison
DeepSeek-V3.2-Exp vs Llama-3.3-70B-Instruct
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.9 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 1.9× less per token, which makes it the better buy when DeepSeek-V3.2-Exp's lead doesn't matter for your workload.
Last verified . 28 shared benchmarks.
DeepSeek
MetaSummary
- They share 28 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 9 categories and Llama-3.3-70B-Instruct in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where DeepSeek-V3.2-Exp leads 40.8 to 14.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 87.8% for DeepSeek-V3.2-Exp 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 $0.26 / $0.38 for DeepSeek-V3.2-Exp.
- DeepSeek-V3.2-Exp accepts more context: 164K tokens versus 128K.
Side by side
| DeepSeek-V3.2-Exp | Llama-3.3-70B-Instruct | |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 44.9 | 30.3 |
| Released | 2025-09-29 | 2024-12-06 |
| Weights | Open | Open |
| Context window | 164K | 128K |
| Max output | 66K | 4K |
| Input $ / M tokens | $0.26 | $0.10 |
| Output $ / M tokens | $0.38 | $0.32 |
| Results tracked | 48 | 43 |
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Category by category
Coding DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 46.4 (#61), Llama-3.3-70B-Instruct: 30.6 (#284)
| Benchmark | DeepSeek-V3.2-Exp | Llama-3.3-70B-Instruct |
|---|---|---|
| SciCode | 38.9% | 26% |
| WeirdML | 39.5% | 14.4% |
| LMArena Coding | 1454 | 1268 |
| SWE-bench Verified (bash only) | 70% | — |
| Aider Polyglot | 74.2% | — |
| LMArena WebDev | 1362 | — |
| SWE-bench Multilingual | 59% | — |
| BigCodeBench Instruct | — | 46.9% |
| LiveBench Coding | — | 36.6% |
| BigCodeBench Complete | — | 57.5% |
Agentic & Tool Use DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 37.9 (#31), Llama-3.3-70B-Instruct: 25.6 (#100)
| Benchmark | DeepSeek-V3.2-Exp | Llama-3.3-70B-Instruct |
|---|---|---|
| Berkeley Function Calling Leaderboard | 56.7% | 31.9% |
| Terminal-Bench | 39.6% | — |
| TheAgentCompany | 42.9% | — |
| BALROG | — | 23% |
| Vending-Bench 2 | 1,034 | — |
Reasoning DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 22.0 (#185), Llama-3.3-70B-Instruct: 13.7 (#325)
| Benchmark | DeepSeek-V3.2-Exp | Llama-3.3-70B-Instruct |
|---|---|---|
| CritPt | 2.9% | 0% |
| LMArena Hard Prompts | 1433 | 1257 |
| DTBench | 87.7% | 59.5% |
| LMCA | 29.1% | 17.5% |
| Epoch Capabilities Index | 146.28 | 127.32 |
| ARC-AGI-2 | 4% | — |
| SimpleBench | — | 19.9% |
| Kagi LLM Benchmark | 52.2% | — |
| NYT Connections (extended) | 36.7% | — |
| ARC-AGI-1 | 57% | — |
| Chess Puzzles | 14% | — |
| Thematic Generalization | 65% | — |
| LiveBench Reasoning | — | 50.8% |
| LiveBench Data Analysis | — | 49.5% |
| ForecastBench | — | 58.6 |
| LiveBench | — | 50.2% |
Math DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 40.8 (#75), Llama-3.3-70B-Instruct: 14.3 (#292)
| Benchmark | DeepSeek-V3.2-Exp | Llama-3.3-70B-Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 87.8% | 5.1% |
| LMArena Math | 1435 | 1268 |
| MathArena Final-Answer Competitions | 57.7% | — |
| ProofBench | 8% | — |
| LiveBench Math | — | 42.2% |
| MATH Level 5 | — | 41.6% |
| FrontierMath (Feb 2025 set) | 22.1% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 51.6 (#61), Llama-3.3-70B-Instruct: 30.5 (#220)
| Benchmark | DeepSeek-V3.2-Exp | Llama-3.3-70B-Instruct |
|---|---|---|
| GPQA Diamond | 83.4% | 47.4% |
| Vectara Hallucination Rate | 5.3% | 4.1% |
| LMArena Expert | 1436 | 1225 |
| Confabulations | — | 22.8% |
| MMLU | — | 86.3% |
Multilingual DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 52.4 (#85), Llama-3.3-70B-Instruct: 40.1 (#215)
| Benchmark | DeepSeek-V3.2-Exp | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | 1409 | 1236 |
| LMArena Chinese | 1461 | 1217 |
| LMArena French | 1435 | 1280 |
| LMArena German | 1440 | 1251 |
| LMArena Japanese | 1377 | 1149 |
| LMArena Korean | 1372 | 1142 |
| LMArena Russian | 1423 | 1252 |
| LMArena Spanish | 1441 | 1269 |
Instruction Following DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 74.7 (#89), Llama-3.3-70B-Instruct: 71.3 (#152)
| Benchmark | DeepSeek-V3.2-Exp | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Instruction Following | 1413 | 1242 |
| LiveBench Instruction Following | — | 82.7% |
Long Context DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 47.6 (#15), Llama-3.3-70B-Instruct: 26.4 (#290)
| Benchmark | DeepSeek-V3.2-Exp | Llama-3.3-70B-Instruct |
|---|---|---|
| Fiction.LiveBench | 83.3% | 33.3% |
| LMArena Longer Query | 1428 | 1256 |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |
Writing & Preference DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 62.5 (#73), Llama-3.3-70B-Instruct: 47.8 (#203)
| Benchmark | DeepSeek-V3.2-Exp | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | 1425 | 1274 |
| LMArena Creative Writing | 1403 | 1250 |
| LMArena Multi-Turn | 1427 | 1279 |
| EQ-Bench Creative Writing | 1515 | — |
| LiveBench Language | — | 39.2% |
Frequently asked questions
Is DeepSeek-V3.2-Exp better than Llama-3.3-70B-Instruct?
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.9 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 1.9× less per token, which makes it the better buy when DeepSeek-V3.2-Exp's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3.2-Exp 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-V3.2-Exp lists at $0.26 and $0.38.
Is DeepSeek-V3.2-Exp or Llama-3.3-70B-Instruct better for coding?
DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.4 versus 30.6 in the Noometry coding category.
Which has the bigger context window?
DeepSeek-V3.2-Exp does, with 164K tokens against 128K.
How many benchmarks do DeepSeek-V3.2-Exp and Llama-3.3-70B-Instruct share?
28 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 48 scored results on Noometry and Llama-3.3-70B-Instruct has 43.