LLMBenchmark.ai

Enter opens the highlighted result. Esc closes.

DeepSeek V4 Pro 0423 vs Qwen3.5-Flash

Updated October 21, 2026. 51 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace DeepSeek V4 Pro 0423 Replace Qwen3.5-Flash
DeepSeek V4 Pro 0423DeepSeek
Coding
35.3
Overall
30.4
Output $/M
$1.9105
47
Qwen3.5-FlashQwen
Coding
6.2
Overall
No data
Output $/M
$0.26
4
vs boards won of 51 shared

Decision reading

Pick DeepSeek V4 Pro 0423 for coding: it scores 35.3 on the Vibe Coding Index against 6.2 for Qwen3.5-Flash.

Pick Qwen3.5-Flash when output tokens cost 7.3x less, or when a solved task costs $0.40 against $0.57.

Model ADeepSeek V4 Pro 0423DeepSeek35.3/1001 independent benchmark$0.57 per solved taskModel BQwen3.5-FlashQwen6.2/1001 independent benchmark$0.40 per solved task
Shared results
51
DeepSeek V4 Pro 0423 only
32
Qwen3.5-Flash only
4
Like-for-like categories
6 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.

At a glance

Coding index
DeepSeek V4 Pro 0423 35.3 from 1 benchmark, Qwen3.5-Flash 6.2 from 1 benchmark.
Widest category
Coding, 18.6 points apart on average over 8 shared benchmarks.
Widest single gap
LMArena WebDev: React: DeepSeek V4 Pro 0423 1441 against Qwen3.5-Flash 1229, 35.2 points apart on a 0 to 100 scale.
Output price
DeepSeek V4 Pro 0423 $1.9105 against Qwen3.5-Flash $0.26 per million output tokens, 7.3x apart.
Context window
DeepSeek V4 Pro 0423 takes 1.05M tokens, Qwen3.5-Flash 1M.

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 0423BarsQwen3.5-FlashTies
Coding80
Intelligence10
Reasoning61
Math40
Knowledge83
General200
All shared boards474
CodingLike-for-likeFour of these boards feed the Vibe Coding Index80
Coding benchmarks for DeepSeek V4 Pro 0423 and Qwen3.5-Flash
BenchmarkDeepSeek V4 Pro 0423Qwen3.5-FlashLeader
LMArena WebDev14471244DeepSeek V4 Pro 0423
AA Coding Index59.4Not measuredOnly DeepSeek V4 Pro 0423 measured
LiveCodeBench (Vals AI)87.5%83.3%DeepSeek V4 Pro 0423
SWE-bench Verified (Vals AI)77.4%64.4%DeepSeek V4 Pro 0423
Terminal-Bench 4 (Vals AI)11.1%Not measuredOnly DeepSeek V4 Pro 0423 measured
Vibe Code Bench (Vals AI)49.9%Not measuredOnly DeepSeek V4 Pro 0423 measured
Code migration (Vals AI)26.2%Not measuredOnly DeepSeek V4 Pro 0423 measured
ProgramBench (Vals AI)0.0%Not measuredOnly DeepSeek V4 Pro 0423 measured
Terminal-Bench 2.1 (Vals AI)50.2%Not measuredOnly DeepSeek V4 Pro 0423 measured
SkillsBench (Vals AI)51.3%Not measuredOnly DeepSeek V4 Pro 0423 measured
FrontierCode17.6%Not measuredOnly DeepSeek V4 Pro 0423 measured
WeirdML48.9%Not measuredOnly DeepSeek V4 Pro 0423 measured
ALE-Bench1006.1265.9DeepSeek V4 Pro 0423
LMArena Text: coding14701412DeepSeek V4 Pro 0423
LMArena Text: software and IT14691416DeepSeek V4 Pro 0423
LMArena WebDev: React14411229DeepSeek V4 Pro 0423
LMArena WebDev: HTML14351291DeepSeek V4 Pro 0423
Neither model has a result on the other 21 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for DeepSeek V4 Pro 0423 and Qwen3.5-Flash
BenchmarkDeepSeek V4 Pro 0423Qwen3.5-FlashLeader
AA Intelligence Index30.4Not measuredOnly DeepSeek V4 Pro 0423 measured
Epoch Capabilities Index149.1143.5DeepSeek V4 Pro 0423
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
Agentic work benchmarks for DeepSeek V4 Pro 0423 and Qwen3.5-Flash
BenchmarkDeepSeek V4 Pro 0423Qwen3.5-FlashLeader
AA Agentic Index26.3Not measuredOnly DeepSeek V4 Pro 0423 measured
Design Arena: agents building web apps1000Not measuredOnly DeepSeek V4 Pro 0423 measured
Design Arena: agents building full-stack apps948Not measuredOnly DeepSeek V4 Pro 0423 measured
Design Arena: agents building Godot games1059Not measuredOnly DeepSeek V4 Pro 0423 measured
Finance Agent (Vals AI)44.1%Not measuredOnly DeepSeek V4 Pro 0423 measured
Tax Agent Bench (Vals AI)25.2%Not measuredOnly DeepSeek V4 Pro 0423 measured
Legal Research Bench (Vals AI)23.1%Not measuredOnly DeepSeek V4 Pro 0423 measured
Excel Modeling Benchmark (Vals AI)51.6%Not measuredOnly DeepSeek V4 Pro 0423 measured
Vending-Bench 2$3,285Not measuredOnly DeepSeek V4 Pro 0423 measured
Neither model has a result on the other 30 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index61
Reasoning benchmarks for DeepSeek V4 Pro 0423 and Qwen3.5-Flash
BenchmarkDeepSeek V4 Pro 0423Qwen3.5-FlashLeader
GPQA Diamond89.4%82.8%DeepSeek V4 Pro 0423
MMMU Pro (Vals AI)Not measured81.9%Only Qwen3.5-Flash measured
GPQA Diamond (Epoch AI run)90.9%82.3%DeepSeek V4 Pro 0423
Chess puzzles19.0%14.0%DeepSeek V4 Pro 0423
Mystery game puzzles17.0%20.0%Qwen3.5-Flash
LMArena Text: hard prompts14611403DeepSeek V4 Pro 0423
LMArena Text: expert14631407DeepSeek V4 Pro 0423
LMArena Text: hard prompts in English14661409DeepSeek V4 Pro 0423
Neither model has a result on the other 13 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index40
Math benchmarks for DeepSeek V4 Pro 0423 and Qwen3.5-Flash
BenchmarkDeepSeek V4 Pro 0423Qwen3.5-FlashLeader
AIMENot measured92.5%Only Qwen3.5-Flash measured
OTIS Mock AIME96.7%84.4%DeepSeek V4 Pro 0423
FrontierMath, tiers 1 to 345.3%18.2%DeepSeek V4 Pro 0423
FrontierMath, tier 42.4%Not measuredOnly DeepSeek V4 Pro 0423 measured
ProofBench16.0%Not measuredOnly DeepSeek V4 Pro 0423 measured
LMArena Text: math14431407DeepSeek V4 Pro 0423
LMArena Text: mathematical work14551413DeepSeek V4 Pro 0423
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index83
Knowledge benchmarks for DeepSeek V4 Pro 0423 and Qwen3.5-Flash
BenchmarkDeepSeek V4 Pro 0423Qwen3.5-FlashLeader
MMLU Pro87.3%84.1%DeepSeek V4 Pro 0423
LegalBench (Vals AI)80.3%84.3%Qwen3.5-Flash
CaseLaw (Vals AI)59.4%56.0%DeepSeek V4 Pro 0423
CorpFin (Vals AI)61.4%63.6%Qwen3.5-Flash
TaxEval (Vals AI)72.1%72.2%Qwen3.5-Flash
MortgageTax (Vals AI)Not measured67.4%Only Qwen3.5-Flash measured
MedCode (Vals AI)40.5%33.0%DeepSeek V4 Pro 0423
SimpleQA Verified47.0%20.3%DeepSeek V4 Pro 0423
LMArena Text: science14701415DeepSeek V4 Pro 0423
LMArena Text: medicine and healthcare14661407DeepSeek V4 Pro 0423
LMArena Text: legal and government14641398DeepSeek V4 Pro 0423
LMArena Text: business and finance14431394DeepSeek V4 Pro 0423
Neither model has a result on the other 5 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for DeepSeek V4 Pro 0423 and Qwen3.5-Flash
BenchmarkDeepSeek V4 Pro 0423Qwen3.5-FlashLeader
Design Arena: websites1235Not measuredOnly DeepSeek V4 Pro 0423 measured
Design Arena: UI components1221Not measuredOnly DeepSeek V4 Pro 0423 measured
Design Arena: game dev1229Not measuredOnly DeepSeek V4 Pro 0423 measured
Design Arena: data viz1203Not measuredOnly DeepSeek V4 Pro 0423 measured
Design Arena: 3D1254Not measuredOnly DeepSeek V4 Pro 0423 measured
Design Arena: all code categories1240Not measuredOnly DeepSeek V4 Pro 0423 measured
Design Arena: SVG1139Not measuredOnly DeepSeek V4 Pro 0423 measured
Design Arena: ASCII art1148Not measuredOnly DeepSeek V4 Pro 0423 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index200
General benchmarks for DeepSeek V4 Pro 0423 and Qwen3.5-Flash
BenchmarkDeepSeek V4 Pro 0423Qwen3.5-FlashLeader
Public Benefits Bench (Vals AI)62.9%Not measuredOnly DeepSeek V4 Pro 0423 measured
SAGE (Vals AI)Not measured42.5%Only Qwen3.5-Flash measured
MedScribe (Vals AI)75.1%70.6%DeepSeek V4 Pro 0423
DTBench90.7%82.9%DeepSeek V4 Pro 0423
LMCA41.2%29.1%DeepSeek V4 Pro 0423
Surface Evolver Bench40.0%Not measuredOnly DeepSeek V4 Pro 0423 measured
CL-bench Life13.5%Not measuredOnly DeepSeek V4 Pro 0423 measured
LMArena Text14511397DeepSeek V4 Pro 0423
LMArena Text: instruction following14451374DeepSeek V4 Pro 0423
LMArena Text: creative writing14461343DeepSeek V4 Pro 0423
LMArena Text: English14591404DeepSeek V4 Pro 0423
LMArena Text: non-English14391385DeepSeek V4 Pro 0423
LMArena Text: Chinese14811445DeepSeek V4 Pro 0423
LMArena Text: German14581390DeepSeek V4 Pro 0423
LMArena Text: Japanese14461369DeepSeek V4 Pro 0423
LMArena Text: Korean14091344DeepSeek V4 Pro 0423
LMArena Text: French14731413DeepSeek V4 Pro 0423
LMArena Text: Spanish14571400DeepSeek V4 Pro 0423
LMArena Text: Russian14531380DeepSeek V4 Pro 0423
LMArena Text: Polish14611376DeepSeek V4 Pro 0423
LMArena Text: multi-turn14681393DeepSeek V4 Pro 0423
LMArena Text: longer queries14581392DeepSeek V4 Pro 0423
LMArena Text: writing and language14471369DeepSeek V4 Pro 0423
LMArena Text: entertainment and media14311347DeepSeek V4 Pro 0423
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
Image generation benchmarks for DeepSeek V4 Pro 0423 and Qwen3.5-Flash
BenchmarkDeepSeek V4 Pro 0423Qwen3.5-FlashLeader
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 0423$0.0019Fits in one request
Qwen3.5-Flash$0.0002Fits in one request

Qwen3.5-Flash costs less

Repository review

50K input + 3K output tokens

DeepSeek V4 Pro 0423$0.053Fits in one request
Qwen3.5-Flash$0.0040Fits in one request

Qwen3.5-Flash costs less

Agent loop

200K input + 10K output tokens

DeepSeek V4 Pro 0423$0.210Fits in one request
Qwen3.5-Flash$0.016Fits in one request

Qwen3.5-Flash 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 0423 and Qwen3.5-Flash
SpecificationDeepSeek V4 Pro 0423Qwen3.5-Flash
MakerDeepSeekQwen
WeightsOpen weightsNo public weights listed
ReleasedApril 24, 2026February 25, 2026
Context window1.05M1M
Input $/Mtok$0.9553$0.065
Output $/Mtok$1.9105$0.26
Coding index35.36.2
Independent measurements behind it11
Cost per solved task$0.57$0.40
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V4 Pro 0423 better than Qwen3.5-Flash for coding?

Pick DeepSeek V4 Pro 0423 for coding: it scores 35.3 on the Vibe Coding Index against 6.2 for Qwen3.5-Flash. Pick Qwen3.5-Flash when output tokens cost 7.3x less, or when a solved task costs $0.40 against $0.57.

Which is cheaper, DeepSeek V4 Pro 0423 or Qwen3.5-Flash?

DeepSeek V4 Pro 0423 costs $0.9553 per million input tokens and $1.9105 per million output tokens; Qwen3.5-Flash costs $0.065 and $0.26. Per solved SWE-bench Verified task, DeepSeek V4 Pro 0423 costs $0.57 and Qwen3.5-Flash $0.40.

Which has the bigger context window, DeepSeek V4 Pro 0423 or Qwen3.5-Flash?

DeepSeek V4 Pro 0423, with 1.05M tokens against 1M.

How many benchmarks is the DeepSeek V4 Pro 0423 vs Qwen3.5-Flash comparison based on?

51 benchmarks have a result for both models as of October 21, 2026. DeepSeek V4 Pro 0423's coding index rests on 1 independent benchmark and Qwen3.5-Flash's on 1. Where only one model has a result, the page says so instead of naming a winner.

Compare next