DeepSeek V3 0324 vs Qwen3 Coder 30B A3B Instruct
DeepSeek V3 0324 leads Qwen3 Coder 30B A3B Instruct on the Artificial Analysis Intelligence Index, 9.7 to 9.6, and Qwen3 Coder 30B A3B Instruct costs 2.4x less per output token ($0.44 vs $1.07 per million). Updated October 21, 2026; 12 benchmarks have a result for both.
DeepSeek V3 0324 or Qwen3 Coder 30B A3B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick DeepSeek V3 0324 for work measured by SWE-bench Verified, where it leads.
- Shared results
- 12
- DeepSeek V3 0324 only
- 23
- Qwen3 Coder 30B A3B Instruct only
- 4
- 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 0324
DeepSeek V3 0324 leads, 9.7 against 9.6.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only DeepSeek V3 0324 has a AA Agentic Index result, 0.8.
Directional only
Reasoning
GPQA Diamond
No call
Only DeepSeek V3 0324 has a GPQA Diamond result, 61.6%.
Directional only
Math
AIME
No call
Only DeepSeek V3 0324 has a AIME result, 52.2%.
Directional only
Knowledge
MMLU Pro
No call
Only DeepSeek V3 0324 has a MMLU Pro result, 79.5%.
Directional only
Web design
Design Arena: websites
No call
Only Qwen3 Coder 30B A3B Instruct has a Design Arena: websites result, 1087.
Directional only
Long documents
Listed context window
Qwen3 Coder 30B A3B Instruct
Qwen3 Coder 30B A3B Instruct takes 262K tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 Coder 30B A3B Instruct
DeepSeek V3 0324 $0.0008, Qwen3 Coder 30B A3B Instruct $0.0003 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3 0324 not benchmarked, Qwen3 Coder 30B A3B Instruct not benchmarked.
- Widest category
- Agentic work, 12.6 points apart on average over 1 shared benchmark.
- Widest single gap
- SWE-bench Verified: DeepSeek V3 0324 42.0% against Qwen3 Coder 30B A3B Instruct 60.4%, 18.4 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3 0324 $1.07 against Qwen3 Coder 30B A3B Instruct $0.44 per million output tokens, 2.4x apart.
- Context window
- DeepSeek V3 0324 takes 164K tokens, Qwen3 Coder 30B A3B Instruct 262K.
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 0324 | Bars | Qwen3 Coder 30B A3B Instruct | Ties |
|---|---|---|---|---|
| Coding | 1 | 1 | 1 tie | |
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 1 | 0 | ||
| General | 2 | 0 | ||
| All shared boards | 10 | 1 | 1 tie |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index11
| Benchmark | DeepSeek V3 0324 | Qwen3 Coder 30B A3B Instruct | Leader |
|---|---|---|---|
| SWE-bench Verified | 42.0% | 60.4% | Qwen3 Coder 30B A3B Instruct |
| SWE-bench Lite | Not measured | 49.7% | Only Qwen3 Coder 30B A3B Instruct measured |
| AA Coding Index | 21.2 | Not measured | Only DeepSeek V3 0324 measured |
| LiveCodeBench (Vals AI) | 65.5% | Not measured | Only DeepSeek V3 0324 measured |
| LiveCodeBench (Artificial Analysis run) | 40.5% | 40.3% | DeepSeek V3 0324 |
| SciCode | 39.0% | Not measured | Only DeepSeek V3 0324 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 15.2% | 15.2% | Tie |
| WeirdML | 36.1% | Not measured | Only DeepSeek V3 0324 measured |
| Neither model has a result on the other 31 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V3 0324 | Qwen3 Coder 30B A3B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 9.7 | 9.6 | DeepSeek V3 0324 |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V3 0324 | Qwen3 Coder 30B A3B Instruct | Leader |
|---|---|---|---|
| AA Agentic Index | 0.8 | Not measured | Only DeepSeek V3 0324 measured |
| τ²-Bench Telecom | 47.1% | 34.5% | DeepSeek V3 0324 |
| METR time horizons | 49.6% | Not measured | Only DeepSeek V3 0324 measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V3 0324 | Qwen3 Coder 30B A3B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond | 61.6% | Not measured | Only DeepSeek V3 0324 measured |
| GPQA Diamond (Artificial Analysis run) | 65.5% | 51.6% | DeepSeek V3 0324 |
| Humanity's Last Exam (Artificial Analysis run) | 4.7% | 3.8% | DeepSeek V3 0324 |
| GPQA Diamond (Epoch AI run) | 67.6% | Not measured | Only DeepSeek V3 0324 measured |
| SimpleBench | 27.2% | Not measured | Only DeepSeek V3 0324 measured |
| Neither model has a result on the other 17 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V3 0324 | Qwen3 Coder 30B A3B Instruct | Leader |
|---|---|---|---|
| AIME | 52.2% | Not measured | Only DeepSeek V3 0324 measured |
| MATH 500 (Vals AI) | 88.6% | Not measured | Only DeepSeek V3 0324 measured |
| MGSM (Vals AI) | 91.7% | Not measured | Only DeepSeek V3 0324 measured |
| AA Math Index | 41.0 | 29.0 | DeepSeek V3 0324 |
| AIME 2025 (Artificial Analysis run) | 41.0% | 29.0% | DeepSeek V3 0324 |
| OTIS Mock AIME | 37.8% | Not measured | Only DeepSeek V3 0324 measured |
| MATH level 5 | 75.5% | Not measured | Only DeepSeek V3 0324 measured |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V3 0324 | Qwen3 Coder 30B A3B Instruct | Leader |
|---|---|---|---|
| MMLU Pro | 79.5% | Not measured | Only DeepSeek V3 0324 measured |
| LegalBench (Vals AI) | 77.7% | Not measured | Only DeepSeek V3 0324 measured |
| CorpFin (Vals AI) | 54.7% | Not measured | Only DeepSeek V3 0324 measured |
| TaxEval (Vals AI) | 71.1% | Not measured | Only DeepSeek V3 0324 measured |
| MedQA (Vals AI) | 82.0% | Not measured | Only DeepSeek V3 0324 measured |
| MMLU Pro (Artificial Analysis run) | 81.9% | 70.6% | DeepSeek V3 0324 |
| Neither model has a result on the other 12 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | DeepSeek V3 0324 | Qwen3 Coder 30B A3B Instruct | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1087 | Only Qwen3 Coder 30B A3B Instruct measured |
| Design Arena: UI components | Not measured | 1046 | Only Qwen3 Coder 30B A3B Instruct measured |
| Design Arena: data viz | Not measured | 1085 | Only Qwen3 Coder 30B A3B Instruct measured |
| Neither model has a result on the other 6 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V3 0324 | Qwen3 Coder 30B A3B Instruct | Leader |
|---|---|---|---|
| IFBench | 41.0% | 32.7% | DeepSeek V3 0324 |
| Long context reasoning (AA-LCR) | 40.7% | 32.7% | DeepSeek V3 0324 |
| DTBench | 64.8% | Not measured | Only DeepSeek V3 0324 measured |
| LMCA | 15.5% | Not measured | Only DeepSeek V3 0324 measured |
| Fiction.LiveBench | 50.0% | Not measured | Only DeepSeek V3 0324 measured |
| Creative writing (Lech Mazur) | 77.0% | Not measured | Only DeepSeek V3 0324 measured |
| Neither model has a result on the other 40 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3 0324 | Qwen3 Coder 30B A3B 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
Qwen3 Coder 30B A3B Instruct costs less
Repository review
50K input + 3K output tokens
Qwen3 Coder 30B A3B Instruct costs less
Agent loop
200K input + 10K output tokens
Qwen3 Coder 30B A3B 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 0324 | Qwen3 Coder 30B A3B Instruct |
|---|---|---|
| Maker | DeepSeek | Qwen |
| Weights | Open weights | Open weights |
| Released | March 24, 2025 | July 31, 2025 |
| Context window | 164K | 262K |
| Input $/Mtok | $0.27 | $0.11 |
| Output $/Mtok | $1.07 | $0.44 |
| 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 0324 better than Qwen3 Coder 30B A3B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick DeepSeek V3 0324 for work measured by SWE-bench Verified, where it leads.
Which is cheaper, DeepSeek V3 0324 or Qwen3 Coder 30B A3B Instruct?
DeepSeek V3 0324 costs $0.27 per million input tokens and $1.07 per million output tokens; Qwen3 Coder 30B A3B Instruct costs $0.11 and $0.44. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V3 0324 or Qwen3 Coder 30B A3B Instruct?
Qwen3 Coder 30B A3B Instruct, with 262K tokens against 164K.
How many benchmarks is the DeepSeek V3 0324 vs Qwen3 Coder 30B A3B Instruct comparison based on?
12 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3 0324's coding index rests on 0 independent benchmarks and Qwen3 Coder 30B A3B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.