DeepSeek V3 vs Qwen3.5-35B-A3B
Updated October 21, 2026. 4 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
DeepSeek V3 scores 70.2 and Qwen3.5-35B-A3B 7.7 on the Vibe Coding Index, but no coding benchmark measured both, so the gap is directional, not a ranking.
Pick Qwen3.5-35B-A3B when output tokens cost 1.0x less, or when the context window is larger, 262K against 164K tokens.
- Shared results
- 4
- DeepSeek V3 only
- 9
- Qwen3.5-35B-A3B only
- 41
- Like-for-like categories
- 3 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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, four public coding benchmarks
No call
70.2 against 7.7, from different benchmarks with none in common.
Directional only
Intelligence
AA Intelligence Index
No call
Only Qwen3.5-35B-A3B has a AA Intelligence Index result, 19.3.
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
Qwen3.5-35B-A3B
Qwen3.5-35B-A3B takes 262K tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5-35B-A3B
DeepSeek V3 $0.0008, Qwen3.5-35B-A3B $0.0006 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3 70.2 from 1 benchmark, Qwen3.5-35B-A3B 7.7 from 1 benchmark.
- Widest category
- Math, 21.1 points apart on average over 1 shared benchmark.
- Widest single gap
- OTIS Mock AIME: DeepSeek V3 48.9% against Qwen3.5-35B-A3B 70.0%, 21.1 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3 $1.0287 against Qwen3.5-35B-A3B $1.00 per million output tokens, 1.0x apart.
- Context window
- DeepSeek V3 takes 164K tokens, Qwen3.5-35B-A3B 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 | Bars | Qwen3.5-35B-A3B | Ties |
|---|---|---|---|---|
| Reasoning | 0 | 1 | ||
| Math | 0 | 1 | ||
| General | 0 | 2 | ||
| All shared boards | 0 | 4 |
CodingDirectional onlyFour of these boards feed the Vibe Coding Index9 boards, none shared
| Benchmark | DeepSeek V3 | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| Aider polyglot | 70.2% | Not measured | Only DeepSeek V3 measured |
| SWE-bench Lite | 30.7% | Not measured | Only DeepSeek V3 measured |
| LMArena WebDev | Not measured | 1254 | Only Qwen3.5-35B-A3B measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 26.5% | Only Qwen3.5-35B-A3B measured |
| ALE-Bench | Not measured | 221.8 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: coding | Not measured | 1410 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: software and IT | Not measured | 1415 | Only Qwen3.5-35B-A3B measured |
| LMArena WebDev: React | Not measured | 1238 | Only Qwen3.5-35B-A3B measured |
| LMArena WebDev: HTML | Not measured | 1301 | Only Qwen3.5-35B-A3B measured |
| Neither model has a result on the other 29 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | DeepSeek V3 | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 19.3 | Only Qwen3.5-35B-A3B measured |
| Epoch Capabilities Index | Not measured | 142.5 | Only Qwen3.5-35B-A3B measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | DeepSeek V3 | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 89.2% | Only Qwen3.5-35B-A3B measured |
| Vending-Bench 2 | Not measured | $463 | Only Qwen3.5-35B-A3B measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3 | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 84.5% | Only Qwen3.5-35B-A3B measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 21.0% | Only Qwen3.5-35B-A3B measured |
| GPQA Diamond (Epoch AI run) | 71.2% | 83.5% | Qwen3.5-35B-A3B |
| LMArena Text: hard prompts | Not measured | 1400 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: expert | Not measured | 1408 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: hard prompts in English | Not measured | 1411 | Only Qwen3.5-35B-A3B measured |
| Neither model has a result on the other 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3 | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| OTIS Mock AIME | 48.9% | 70.0% | Qwen3.5-35B-A3B |
| LMArena Text: math | Not measured | 1404 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: mathematical work | Not measured | 1410 | Only Qwen3.5-35B-A3B measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | DeepSeek V3 | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| LMArena Text: science | Not measured | 1413 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: medicine and healthcare | Not measured | 1421 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: legal and government | Not measured | 1399 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: business and finance | Not measured | 1394 | Only Qwen3.5-35B-A3B measured |
| Neither model has a result on the other 13 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | DeepSeek V3 | Qwen3.5-35B-A3B | 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 | 1054 | 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 | 976 | Not measured | Only DeepSeek V3 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V3 | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| IFBench | Not measured | 72.5% | Only Qwen3.5-35B-A3B measured |
| Long context reasoning (AA-LCR) | Not measured | 72.0% | Only Qwen3.5-35B-A3B measured |
| DTBench | 62.7% | 80.0% | Qwen3.5-35B-A3B |
| LMCA | 15.2% | 29.5% | Qwen3.5-35B-A3B |
| LMArena Text | Not measured | 1395 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: instruction following | Not measured | 1379 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: creative writing | Not measured | 1347 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: English | Not measured | 1407 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: non-English | Not measured | 1378 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: Chinese | Not measured | 1457 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: German | Not measured | 1367 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: Japanese | Not measured | 1326 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: Korean | Not measured | 1355 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: French | Not measured | 1412 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: Spanish | Not measured | 1390 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: Russian | Not measured | 1376 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: Polish | Not measured | 1385 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: multi-turn | Not measured | 1390 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: longer queries | Not measured | 1389 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: writing and language | Not measured | 1365 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: entertainment and media | Not measured | 1347 | Only Qwen3.5-35B-A3B measured |
| Neither model has a result on the other 25 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3 | Qwen3.5-35B-A3B | 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
Qwen3.5-35B-A3B costs less
Repository review
50K input + 3K output tokens
Qwen3.5-35B-A3B costs less
Agent loop
200K input + 10K output tokens
Qwen3.5-35B-A3B 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 | Qwen3.5-35B-A3B |
|---|---|---|
| Maker | DeepSeek | Qwen |
| Weights | Open weights | Open weights |
| Released | December 26, 2024 | February 25, 2026 |
| Context window | 164K | 262K |
| Input $/Mtok | $0.2574 | $0.14 |
| Output $/Mtok | $1.0287 | $1.00 |
| Coding index | 70.2 | 7.7 |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3 better than Qwen3.5-35B-A3B for coding?
DeepSeek V3 scores 70.2 and Qwen3.5-35B-A3B 7.7 on the Vibe Coding Index, but no coding benchmark measured both, so the gap is directional, not a ranking. Pick Qwen3.5-35B-A3B when output tokens cost 1.0x less, or when the context window is larger, 262K against 164K tokens.
Which is cheaper, DeepSeek V3 or Qwen3.5-35B-A3B?
DeepSeek V3 costs $0.2574 per million input tokens and $1.0287 per million output tokens; Qwen3.5-35B-A3B costs $0.14 and $1.00. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V3 or Qwen3.5-35B-A3B?
Qwen3.5-35B-A3B, with 262K tokens against 164K.
How many benchmarks is the DeepSeek V3 vs Qwen3.5-35B-A3B comparison based on?
4 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3's coding index rests on 1 independent benchmark and Qwen3.5-35B-A3B's on 1. Where only one model has a result, the page says so instead of naming a winner.