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DeepSeek V4 Pro 0813 vs Qwen3.6 35B A3B

DeepSeek V4 Pro 0813 leads Qwen3.6 35B A3B on the Artificial Analysis Intelligence Index, 36.0 to 18.2, and Qwen3.6 35B A3B costs 2.0x less per output token ($1.00 vs $1.98 per million). Updated October 22, 2026; 15 benchmarks have a result for both.

DeepSeek V4 Pro 0813DeepSeek
Coding
56.1
Overall
60.8
Output $/M
$1.98
15
Qwen3.6 35B A3BQwen
Coding
No data
Overall
45.0
Output $/M
$1.00
0
vs boards won of 15 shared

DeepSeek V4 Pro 0813 or Qwen3.6 35B A3B: which to pick

Only DeepSeek V4 Pro 0813 has a coding score, 56.1 on the Vibe Coding Index; Qwen3.6 35B A3B has none, so the two cannot be ranked on coding.

Pick Qwen3.6 35B A3B for output tokens 2.0x cheaper ($1.00 vs $1.98 per million).

Shared results
15
DeepSeek V4 Pro 0813 only
63
Qwen3.6 35B A3B only
5
Like-for-like categories
6 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.

At a glance

Coding index
DeepSeek V4 Pro 0813 56.1 from 5 benchmarks, Qwen3.6 35B A3B not benchmarked.
Widest category
Agentic work, 28.2 points apart on average over 1 shared benchmark.
Widest single gap
FrontierMath, tiers 1 to 3: DeepSeek V4 Pro 0813 64.6% against Qwen3.6 35B A3B 20.4%, 44.2 points apart on a 0 to 100 scale.
Output price
DeepSeek V4 Pro 0813 $1.98 against Qwen3.6 35B A3B $1.00 per million output tokens, 2.0x apart.
Context window
DeepSeek V4 Pro 0813 takes 1.05M tokens, Qwen3.6 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.

Boards won, where both have a result
CategoryDeepSeek V4 Pro 0813BarsQwen3.6 35B A3BTies
Coding30
Intelligence20
Agentic work10
Reasoning40
Math20
General30
All shared boards150
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
Coding benchmarks for DeepSeek V4 Pro 0813 and Qwen3.6 35B A3B
BenchmarkDeepSeek V4 Pro 0813Qwen3.6 35B A3BLeader
LMArena WebDev1582Not measuredOnly DeepSeek V4 Pro 0813 measured
AA Coding Index68.841.9DeepSeek V4 Pro 0813
LiveCodeBench (Vals AI)87.5%Not measuredOnly DeepSeek V4 Pro 0813 measured
SWE-bench Verified (Vals AI)96.4%Not measuredOnly DeepSeek V4 Pro 0813 measured
Terminal-Bench 4 (Vals AI)14.1%Not measuredOnly DeepSeek V4 Pro 0813 measured
Vibe Code Bench (Vals AI)82.3%Not measuredOnly DeepSeek V4 Pro 0813 measured
Code migration (Vals AI)41.5%Not measuredOnly DeepSeek V4 Pro 0813 measured
ProgramBench (Vals AI)0.0%Not measuredOnly DeepSeek V4 Pro 0813 measured
IOI (Vals AI)51.6%Not measuredOnly DeepSeek V4 Pro 0813 measured
Terminal-Bench 2.1 (Vals AI)54.7%Not measuredOnly DeepSeek V4 Pro 0813 measured
Vibe Code Bench 1-100 (Vals AI)17.5%Not measuredOnly DeepSeek V4 Pro 0813 measured
SkillsBench (Vals AI)53.8%Not measuredOnly DeepSeek V4 Pro 0813 measured
CyberBench (Vals AI)72.9%Not measuredOnly DeepSeek V4 Pro 0813 measured
SciCode51.0%36.6%DeepSeek V4 Pro 0813
Terminal-Bench Hard (Artificial Analysis run)Not measured34.8%Only Qwen3.6 35B A3B measured
Terminal-BenchNot measured24.6%Only Qwen3.6 35B A3B measured
FrontierCode28.5%Not measuredOnly DeepSeek V4 Pro 0813 measured
WeirdML66.2%34.5%DeepSeek V4 Pro 0813
ALE-Bench1403.2Not measuredOnly DeepSeek V4 Pro 0813 measured
LMArena Text: coding1469Not measuredOnly DeepSeek V4 Pro 0813 measured
LMArena Text: software and IT1462Not measuredOnly DeepSeek V4 Pro 0813 measured
LMArena WebDev: React1588Not measuredOnly DeepSeek V4 Pro 0813 measured
LMArena WebDev: HTML1566Not measuredOnly DeepSeek V4 Pro 0813 measured
Neither model has a result on the other 20 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for DeepSeek V4 Pro 0813 and Qwen3.6 35B A3B
BenchmarkDeepSeek V4 Pro 0813Qwen3.6 35B A3BLeader
AA Intelligence Index36.018.2DeepSeek V4 Pro 0813
Epoch Capabilities Index155.6143.7DeepSeek V4 Pro 0813
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for DeepSeek V4 Pro 0813 and Qwen3.6 35B A3B
BenchmarkDeepSeek V4 Pro 0813Qwen3.6 35B A3BLeader
AA Agentic Index41.313.1DeepSeek V4 Pro 0813
SREBench (Vals AI)1.9%Not measuredOnly DeepSeek V4 Pro 0813 measured
Finance Agent (Vals AI)50.4%Not measuredOnly DeepSeek V4 Pro 0813 measured
Tax Agent Bench (Vals AI)26.8%Not measuredOnly DeepSeek V4 Pro 0813 measured
Legal Research Bench (Vals AI)40.9%Not measuredOnly DeepSeek V4 Pro 0813 measured
Harvey's Legal Agent Benchmark (Vals AI)10.8%Not measuredOnly DeepSeek V4 Pro 0813 measured
Excel Modeling Benchmark (Vals AI)52.8%Not measuredOnly DeepSeek V4 Pro 0813 measured
Terminal-Bench-Science (Vals AI)4.3%Not measuredOnly DeepSeek V4 Pro 0813 measured
τ²-Bench TelecomNot measured95.3%Only Qwen3.6 35B A3B measured
APEX-Agents47.3%Not measuredOnly DeepSeek V4 Pro 0813 measured
Neither model has a result on the other 32 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index40
Reasoning benchmarks for DeepSeek V4 Pro 0813 and Qwen3.6 35B A3B
BenchmarkDeepSeek V4 Pro 0813Qwen3.6 35B A3BLeader
GPQA Diamond92.4%Not measuredOnly DeepSeek V4 Pro 0813 measured
GPQA Diamond (Artificial Analysis run)92.8%84.1%DeepSeek V4 Pro 0813
Humanity's Last Exam (Artificial Analysis run)41.0%22.2%DeepSeek V4 Pro 0813
GPQA Diamond (Epoch AI run)91.7%84.8%DeepSeek V4 Pro 0813
ARC-AGI90.5%Not measuredOnly DeepSeek V4 Pro 0813 measured
ARC-AGI-261.3%Not measuredOnly DeepSeek V4 Pro 0813 measured
Chess puzzles46.0%Not measuredOnly DeepSeek V4 Pro 0813 measured
Mystery game puzzles43.0%22.0%DeepSeek V4 Pro 0813
LMArena Text: hard prompts1458Not measuredOnly DeepSeek V4 Pro 0813 measured
LMArena Text: expert1459Not measuredOnly DeepSeek V4 Pro 0813 measured
LMArena Text: hard prompts in English1463Not measuredOnly DeepSeek V4 Pro 0813 measured
Neither model has a result on the other 12 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index20
Math benchmarks for DeepSeek V4 Pro 0813 and Qwen3.6 35B A3B
BenchmarkDeepSeek V4 Pro 0813Qwen3.6 35B A3BLeader
OTIS Mock AIME98.6%86.7%DeepSeek V4 Pro 0813
FrontierMath, tiers 1 to 364.6%20.4%DeepSeek V4 Pro 0813
FrontierMath, tier 426.8%Not measuredOnly DeepSeek V4 Pro 0813 measured
ProofBench50.0%Not measuredOnly DeepSeek V4 Pro 0813 measured
LMArena Text: math1441Not measuredOnly DeepSeek V4 Pro 0813 measured
LMArena Text: mathematical work1443Not measuredOnly DeepSeek V4 Pro 0813 measured
Neither model has a result on the other 8 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
Knowledge benchmarks for DeepSeek V4 Pro 0813 and Qwen3.6 35B A3B
BenchmarkDeepSeek V4 Pro 0813Qwen3.6 35B A3BLeader
MMLU Pro87.0%Not measuredOnly DeepSeek V4 Pro 0813 measured
LegalBench (Vals AI)82.4%Not measuredOnly DeepSeek V4 Pro 0813 measured
CorpFin (Vals AI)65.4%Not measuredOnly DeepSeek V4 Pro 0813 measured
TaxEval (Vals AI)73.1%Not measuredOnly DeepSeek V4 Pro 0813 measured
MedCode (Vals AI)42.5%Not measuredOnly DeepSeek V4 Pro 0813 measured
SimpleQA Verified52.9%Not measuredOnly DeepSeek V4 Pro 0813 measured
LMArena Text: science1457Not measuredOnly DeepSeek V4 Pro 0813 measured
LMArena Text: medicine and healthcare1446Not measuredOnly DeepSeek V4 Pro 0813 measured
LMArena Text: legal and government1459Not measuredOnly DeepSeek V4 Pro 0813 measured
LMArena Text: business and finance1442Not measuredOnly DeepSeek V4 Pro 0813 measured
Neither model has a result on the other 10 boards here
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Web design benchmarks for DeepSeek V4 Pro 0813 and Qwen3.6 35B A3B
BenchmarkDeepSeek V4 Pro 0813Qwen3.6 35B A3BLeader
Neither model has a result on 9 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index30
General benchmarks for DeepSeek V4 Pro 0813 and Qwen3.6 35B A3B
BenchmarkDeepSeek V4 Pro 0813Qwen3.6 35B A3BLeader
Vals Index53.0%Not measuredOnly DeepSeek V4 Pro 0813 measured
MedScribe (Vals AI)80.2%Not measuredOnly DeepSeek V4 Pro 0813 measured
IFBenchNot measured64.4%Only Qwen3.6 35B A3B measured
Long context reasoning (AA-LCR)80.3%71.7%DeepSeek V4 Pro 0813
DTBench93.9%73.9%DeepSeek V4 Pro 0813
LMCA45.5%29.7%DeepSeek V4 Pro 0813
Surface Evolver BenchNot measured44.4%Only Qwen3.6 35B A3B measured
LMArena Text1446Not measuredOnly DeepSeek V4 Pro 0813 measured
LMArena Text: instruction following1448Not measuredOnly DeepSeek V4 Pro 0813 measured
LMArena Text: creative writing1439Not measuredOnly DeepSeek V4 Pro 0813 measured
LMArena Text: English1457Not measuredOnly DeepSeek V4 Pro 0813 measured
LMArena Text: non-English1431Not measuredOnly DeepSeek V4 Pro 0813 measured
LMArena Text: Chinese1481Not measuredOnly DeepSeek V4 Pro 0813 measured
LMArena Text: Korean1449Not measuredOnly DeepSeek V4 Pro 0813 measured
LMArena Text: French1446Not measuredOnly DeepSeek V4 Pro 0813 measured
LMArena Text: Spanish1435Not measuredOnly DeepSeek V4 Pro 0813 measured
LMArena Text: Russian1448Not measuredOnly DeepSeek V4 Pro 0813 measured
LMArena Text: multi-turn1454Not measuredOnly DeepSeek V4 Pro 0813 measured
LMArena Text: longer queries1458Not measuredOnly DeepSeek V4 Pro 0813 measured
LMArena Text: writing and language1443Not measuredOnly DeepSeek V4 Pro 0813 measured
LMArena Text: entertainment and media1424Not measuredOnly DeepSeek V4 Pro 0813 measured
Neither model has a result on the other 27 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for DeepSeek V4 Pro 0813 and Qwen3.6 35B A3B
BenchmarkDeepSeek V4 Pro 0813Qwen3.6 35B A3BLeader
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 V4 Pro 0813$0.0016Fits in one request
Qwen3.6 35B A3B$0.0006Fits in one request

Qwen3.6 35B A3B costs less

Repository review

50K input + 3K output tokens

DeepSeek V4 Pro 0813$0.039Fits in one request
Qwen3.6 35B A3B$0.0080Fits in one request

Qwen3.6 35B A3B costs less

Agent loop

200K input + 10K output tokens

DeepSeek V4 Pro 0813$0.152Fits in one request
Qwen3.6 35B A3B$0.030Fits in one request

Qwen3.6 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.

Specifications of DeepSeek V4 Pro 0813 and Qwen3.6 35B A3B
SpecificationDeepSeek V4 Pro 0813Qwen3.6 35B A3B
MakerDeepSeekQwen
WeightsOpen weightsOpen weights
ReleasedAugust 12, 2026April 27, 2026
Context window1.05M262K
Input $/Mtok$0.66$0.10
Output $/Mtok$1.98$1.00
Coding index56.1Not benchmarked
Independent measurements behind it50
Cost per solved task$0.10Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V4 Pro 0813 better than Qwen3.6 35B A3B for coding?

Only DeepSeek V4 Pro 0813 has a coding score, 56.1 on the Vibe Coding Index; Qwen3.6 35B A3B has none, so the two cannot be ranked on coding. Pick Qwen3.6 35B A3B for output tokens 2.0x cheaper ($1.00 vs $1.98 per million).

Which is cheaper, DeepSeek V4 Pro 0813 or Qwen3.6 35B A3B?

DeepSeek V4 Pro 0813 costs $0.66 per million input tokens and $1.98 per million output tokens; Qwen3.6 35B A3B costs $0.10 and $1.00. Cost per solved task is known for DeepSeek V4 Pro 0813 only.

Which has the bigger context window, DeepSeek V4 Pro 0813 or Qwen3.6 35B A3B?

DeepSeek V4 Pro 0813, with 1.05M tokens against 262K.

How many benchmarks is the DeepSeek V4 Pro 0813 vs Qwen3.6 35B A3B comparison based on?

15 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4 Pro 0813's coding index rests on 5 independent benchmarks and Qwen3.6 35B A3B's on 0. Where only one model has a result, the page says so instead of naming a winner.

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