DeepSeek V3 vs Llama 3.2 3B Instruct
Updated October 21, 2026. No benchmark has tested both models yet. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.9594
- Coding
- No data
- Overall
- 5.7
- Output $/M
- $0.3325
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
DeepSeek V3 has the edge where the context window is larger, 164K against 131K tokens.
No benchmark has tested both yet. DeepSeek V3 has results on Aider polyglot, SWE-bench Lite and GPQA Diamond (Epoch AI run); Llama 3.2 3B Instruct on LiveCodeBench (Artificial Analysis run), LMArena Text: coding and LMArena Text: software and IT. Compare them on price and context instead.
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
No call
Only Llama 3.2 3B Instruct has a AA Intelligence Index result, 5.7.
Directional only
Web design
Design Arena: websites
No call
Only DeepSeek V3 has a Design Arena: websites result, 1120.
Directional only
Long documents
Listed context window
DeepSeek V3
DeepSeek V3 takes 164K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.2 3B Instruct
DeepSeek V3 $0.0008, Llama 3.2 3B Instruct $0.0002 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3 not benchmarked, Llama 3.2 3B Instruct not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- DeepSeek V3 $0.9594 against Llama 3.2 3B Instruct $0.3325 per million output tokens, 2.9x apart.
- Context window
- DeepSeek V3 takes 164K tokens, Llama 3.2 3B Instruct 131K.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index5 boards measured
| Benchmark | DeepSeek V3 | Llama 3.2 3B Instruct | Leader |
|---|---|---|---|
| Aider polyglot | 70.2% | Not measured | Only DeepSeek V3 measured |
| SWE-bench Lite | 30.7% | Not measured | Only DeepSeek V3 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 8.3% | Only Llama 3.2 3B Instruct measured |
| LMArena Text: coding | Not measured | 1098 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: software and IT | Not measured | 1103 | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 33 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | DeepSeek V3 | Llama 3.2 3B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 5.7 | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | DeepSeek V3 | Llama 3.2 3B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 21.1% | Only Llama 3.2 3B Instruct measured |
| BALROG | Not measured | 10.1% | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | DeepSeek V3 | Llama 3.2 3B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 25.5% | Only Llama 3.2 3B Instruct measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 5.3% | Only Llama 3.2 3B Instruct measured |
| GPQA Diamond (Epoch AI run) | 71.2% | Not measured | Only DeepSeek V3 measured |
| LMArena Text: hard prompts | Not measured | 1095 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: expert | Not measured | 1090 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: hard prompts in English | Not measured | 1141 | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | DeepSeek V3 | Llama 3.2 3B Instruct | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 3.3 | Only Llama 3.2 3B Instruct measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 3.3% | Only Llama 3.2 3B Instruct measured |
| OTIS Mock AIME | 48.9% | Not measured | Only DeepSeek V3 measured |
| LMArena Text: math | Not measured | 1126 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: mathematical work | Not measured | 1128 | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | DeepSeek V3 | Llama 3.2 3B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 34.7% | Only Llama 3.2 3B Instruct measured |
| LMArena Text: science | Not measured | 1106 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: medicine and healthcare | Not measured | 1104 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: legal and government | Not measured | 1115 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: business and finance | Not measured | 1074 | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 12 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | DeepSeek V3 | Llama 3.2 3B Instruct | Leader |
|---|---|---|---|
| Design Arena: websites | 1120 | Not measured | Only DeepSeek V3 measured |
| Design Arena: UI components | 1093 | Not measured | Only DeepSeek V3 measured |
| Design Arena: game dev | 1053 | Not measured | Only DeepSeek V3 measured |
| Design Arena: data viz | 1089 | Not measured | Only DeepSeek V3 measured |
| Design Arena: 3D | 1087 | Not measured | Only DeepSeek V3 measured |
| Design Arena: all code categories | 1108 | Not measured | Only DeepSeek V3 measured |
| Design Arena: SVG | 977 | Not measured | Only DeepSeek V3 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index16 boards measured
| Benchmark | DeepSeek V3 | Llama 3.2 3B Instruct | Leader |
|---|---|---|---|
| IFBench | Not measured | 26.2% | Only Llama 3.2 3B Instruct measured |
| Long context reasoning (AA-LCR) | Not measured | 4.3% | Only Llama 3.2 3B Instruct measured |
| DTBench | 62.7% | Not measured | Only DeepSeek V3 measured |
| LMCA | 15.2% | Not measured | Only DeepSeek V3 measured |
| LMArena Text | Not measured | 1110 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: instruction following | Not measured | 1089 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: creative writing | Not measured | 1094 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: English | Not measured | 1180 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: non-English | Not measured | 1019 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: Chinese | Not measured | 1017 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: German | Not measured | 1056 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: Russian | Not measured | 949 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: multi-turn | Not measured | 1105 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: longer queries | Not measured | 1100 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: writing and language | Not measured | 1096 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: entertainment and media | Not measured | 1083 | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 30 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
Llama 3.2 3B Instruct costs less
Repository review
50K input + 3K output tokens
Llama 3.2 3B Instruct costs less
Agent loop
200K input + 10K output tokens
Llama 3.2 3B Instruct 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 | Meta Llama 3.2 3B Instruct |
|---|---|---|
| Maker | DeepSeek | Meta Llama |
| Weights | Open weights | Open weights |
| Released | December 26, 2024 | September 25, 2024 |
| Context window | 164K | 131K |
| Input $/Mtok | $0.2887 | $0.0505 |
| Output $/Mtok | $0.9594 | $0.3325 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3 better than Llama 3.2 3B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. DeepSeek V3 has the edge where the context window is larger, 164K against 131K tokens.
Which is cheaper, DeepSeek V3 or Llama 3.2 3B Instruct?
DeepSeek V3 costs $0.2887 per million input tokens and $0.9594 per million output tokens; Llama 3.2 3B Instruct costs $0.0505 and $0.3325. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V3 or Llama 3.2 3B Instruct?
DeepSeek V3, with 164K tokens against 131K.
Has any benchmark tested both DeepSeek V3 and Llama 3.2 3B Instruct?
No benchmark has tested both yet. DeepSeek V3 has results on Aider polyglot, SWE-bench Lite and GPQA Diamond (Epoch AI run); Llama 3.2 3B Instruct on LiveCodeBench (Artificial Analysis run), LMArena Text: coding and LMArena Text: software and IT. Compare them on price and context instead. Data as of October 21, 2026.