DeepSeek V3.2 Exp vs Llama 3.3 70B Instruct
DeepSeek V3.2 Exp leads Llama 3.3 70B Instruct on the Artificial Analysis Intelligence Index, 16.6 to 7.7, and DeepSeek V3.2 Exp costs 1.8x less per output token ($0.41 vs $0.72 per million). Updated October 21, 2026; 45 benchmarks have a result for both.
DeepSeek V3.2 Exp or Llama 3.3 70B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick DeepSeek V3.2 Exp for output tokens 1.8x cheaper ($0.41 vs $0.72 per million) and a larger context window (164K against 131K tokens).
- Shared results
- 45
- DeepSeek V3.2 Exp only
- 14
- Llama 3.3 70B Instruct only
- 7
- Like-for-like categories
- 7 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
DeepSeek V3.2 Exp
DeepSeek V3.2 Exp leads, 16.6 against 7.7.
Same board, both measured
Web design
Design Arena: websites
No call
Only DeepSeek V3.2 Exp has a Design Arena: websites result, 1179.
Directional only
Long documents
Listed context window
DeepSeek V3.2 Exp
DeepSeek V3.2 Exp takes 164K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3.2 Exp
DeepSeek V3.2 Exp $0.0005, Llama 3.3 70B Instruct $0.0008 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3.2 Exp not benchmarked, Llama 3.3 70B Instruct not benchmarked.
- Widest category
- Math, 53.4 points apart on average over 4 shared benchmarks.
- Widest single gap
- AIME 2025 (Artificial Analysis run): DeepSeek V3.2 Exp 87.7% against Llama 3.3 70B Instruct 7.7%, 80.0 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.2 Exp $0.41 against Llama 3.3 70B Instruct $0.72 per million output tokens, 1.8x apart.
- Context window
- DeepSeek V3.2 Exp takes 164K tokens, Llama 3.3 70B Instruct 131K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | DeepSeek V3.2 Exp | Bars | Llama 3.3 70B Instruct | Ties |
|---|---|---|---|---|
| Coding | 5 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 2 | 0 | ||
| Reasoning | 5 | 0 | ||
| Math | 4 | 0 | ||
| Knowledge | 5 | 0 | ||
| General | 22 | 0 | ||
| All shared boards | 45 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index50
| Benchmark | DeepSeek V3.2 Exp | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| Aider polyglot | 74.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena WebDev | 1272 | Not measured | Only DeepSeek V3.2 Exp measured |
| AA Coding Index | Not measured | 11.9 | Only Llama 3.3 70B Instruct measured |
| LiveCodeBench (Artificial Analysis run) | 78.9% | 28.8% | DeepSeek V3.2 Exp |
| Terminal-Bench Hard (Artificial Analysis run) | 31.1% | 3.0% | DeepSeek V3.2 Exp |
| WeirdML | 39.5% | 14.4% | DeepSeek V3.2 Exp |
| LMArena Text: coding | 1438 | 1268 | DeepSeek V3.2 Exp |
| LMArena Text: software and IT | 1438 | 1273 | DeepSeek V3.2 Exp |
| LMArena WebDev: HTML | 1281 | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 30 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V3.2 Exp | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 16.6 | 7.7 | DeepSeek V3.2 Exp |
| Epoch Capabilities Index | 144.9 | 127.3 | DeepSeek V3.2 Exp |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V3.2 Exp | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 33.9% | 26.6% | DeepSeek V3.2 Exp |
| BALROG | Not measured | 23.0% | Only Llama 3.3 70B Instruct measured |
| TheAgentCompany | 42.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| Berkeley Function Calling Leaderboard (BFCL) | 56.7% | 31.9% | DeepSeek V3.2 Exp |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | DeepSeek V3.2 Exp | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 79.7% | 49.8% | DeepSeek V3.2 Exp |
| Humanity's Last Exam (Artificial Analysis run) | 14.9% | 3.6% | DeepSeek V3.2 Exp |
| GPQA Diamond (Epoch AI run) | Not measured | 47.4% | Only Llama 3.3 70B Instruct measured |
| SimpleBench | Not measured | 19.9% | Only Llama 3.3 70B Instruct measured |
| LMArena Text: hard prompts | 1429 | 1257 | DeepSeek V3.2 Exp |
| LMArena Text: expert | 1422 | 1225 | DeepSeek V3.2 Exp |
| LMArena Text: hard prompts in English | 1444 | 1277 | DeepSeek V3.2 Exp |
| Kagi LLM Benchmark | 52.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 14 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | DeepSeek V3.2 Exp | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| AA Math Index | 87.7 | 7.7 | DeepSeek V3.2 Exp |
| AIME 2025 (Artificial Analysis run) | 87.7% | 7.7% | DeepSeek V3.2 Exp |
| OTIS Mock AIME | Not measured | 5.1% | Only Llama 3.3 70B Instruct measured |
| MATH level 5 | Not measured | 41.6% | Only Llama 3.3 70B Instruct measured |
| LMArena Text: math | 1422 | 1267 | DeepSeek V3.2 Exp |
| LMArena Text: mathematical work | 1433 | 1267 | DeepSeek V3.2 Exp |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | DeepSeek V3.2 Exp | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 85.0% | 71.3% | DeepSeek V3.2 Exp |
| MMLU | Not measured | 86.3% | Only Llama 3.3 70B Instruct measured |
| LMArena Text: science | 1443 | 1284 | DeepSeek V3.2 Exp |
| LMArena Text: medicine and healthcare | 1468 | 1275 | DeepSeek V3.2 Exp |
| LMArena Text: legal and government | 1419 | 1284 | DeepSeek V3.2 Exp |
| LMArena Text: business and finance | 1412 | 1243 | DeepSeek V3.2 Exp |
| Neither model has a result on the other 12 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| Design Arena: websites | 1179 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: UI components | 1167 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: game dev | 1144 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: data viz | 1152 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: 3D | 1148 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: all code categories | 1171 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: SVG | 1032 | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index220
| Benchmark | DeepSeek V3.2 Exp | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| IFBench | 54.1% | 47.1% | DeepSeek V3.2 Exp |
| Long context reasoning (AA-LCR) | 72.3% | 15.7% | DeepSeek V3.2 Exp |
| DTBench | 87.7% | 59.5% | DeepSeek V3.2 Exp |
| LMCA | 29.1% | 17.5% | DeepSeek V3.2 Exp |
| Fiction.LiveBench | 83.3% | 33.3% | DeepSeek V3.2 Exp |
| CL-bench | 13.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| CL-bench Life | 9.5% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text | 1425 | 1274 | DeepSeek V3.2 Exp |
| LMArena Text: instruction following | 1401 | 1242 | DeepSeek V3.2 Exp |
| LMArena Text: creative writing | 1404 | 1250 | DeepSeek V3.2 Exp |
| LMArena Text: English | 1441 | 1303 | DeepSeek V3.2 Exp |
| LMArena Text: non-English | 1404 | 1236 | DeepSeek V3.2 Exp |
| LMArena Text: Chinese | 1461 | 1217 | DeepSeek V3.2 Exp |
| LMArena Text: German | 1440 | 1251 | DeepSeek V3.2 Exp |
| LMArena Text: Japanese | 1331 | 1150 | DeepSeek V3.2 Exp |
| LMArena Text: Korean | 1372 | 1142 | DeepSeek V3.2 Exp |
| LMArena Text: French | 1434 | 1282 | DeepSeek V3.2 Exp |
| LMArena Text: Spanish | 1441 | 1269 | DeepSeek V3.2 Exp |
| LMArena Text: Russian | 1412 | 1252 | DeepSeek V3.2 Exp |
| LMArena Text: Polish | 1435 | 1259 | DeepSeek V3.2 Exp |
| LMArena Text: multi-turn | 1422 | 1280 | DeepSeek V3.2 Exp |
| LMArena Text: longer queries | 1418 | 1255 | DeepSeek V3.2 Exp |
| LMArena Text: writing and language | 1407 | 1255 | DeepSeek V3.2 Exp |
| LMArena Text: entertainment and media | 1407 | 1241 | DeepSeek V3.2 Exp |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
DeepSeek V3.2 Exp costs less
Repository review
50K input + 3K output tokens
DeepSeek V3.2 Exp costs less
Agent loop
200K input + 10K output tokens
DeepSeek V3.2 Exp costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | DeepSeek V3.2 Exp | Meta Llama 3.3 70B Instruct |
|---|---|---|
| Maker | DeepSeek | Meta Llama |
| Weights | Open weights | Open weights |
| Released | September 29, 2025 | December 6, 2024 |
| Context window | 164K | 131K |
| Input $/Mtok | $0.27 | $0.45 |
| Output $/Mtok | $0.41 | $0.72 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.2 Exp better than Llama 3.3 70B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick DeepSeek V3.2 Exp for output tokens 1.8x cheaper ($0.41 vs $0.72 per million) and a larger context window (164K against 131K tokens).
Which is cheaper, DeepSeek V3.2 Exp or Llama 3.3 70B Instruct?
DeepSeek V3.2 Exp costs $0.27 per million input tokens and $0.41 per million output tokens; Llama 3.3 70B Instruct costs $0.45 and $0.72. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V3.2 Exp or Llama 3.3 70B Instruct?
DeepSeek V3.2 Exp, with 164K tokens against 131K.
How many benchmarks is the DeepSeek V3.2 Exp vs Llama 3.3 70B Instruct comparison based on?
45 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.2 Exp's coding index rests on 1 independent benchmark and Llama 3.3 70B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.