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o1 vs Qwen3.5-Flash

Qwen3.5-Flash costs 230.8x less per output token than o1 ($0.26 vs $60.00 per million). Updated October 22, 2026; 42 benchmarks have a result for both.

Replace o1 Replace Qwen3.5-Flash
o1OpenAI
Coding
No data
Overall
44.6
Output $/M
$60.00
5
Qwen3.5-FlashQwen
Coding
41.1
Overall
50.1
Output $/M
$0.26
37
vs boards won of 42 shared

o1 or Qwen3.5-Flash: which to pick

Only Qwen3.5-Flash has a coding score, 41.1 on the Vibe Coding Index; o1 has none, so the two cannot be ranked on coding.

Pick o1 for work measured by TaxEval (Vals AI) or SimpleQA Verified, where it leads.

Model Ao1OpenAINot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3.5-FlashQwen41.1/1002 independent benchmarks$0.40 per solved task
Shared results
42
o1 only
27
Qwen3.5-Flash only
13
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
o1 not benchmarked, Qwen3.5-Flash 41.1 from 2 benchmarks.
Widest category
Coding, 16.3 points apart on average over 3 shared benchmarks.
Widest single gap
LiveCodeBench (Vals AI): o1 50.3% against Qwen3.5-Flash 83.3%, 33.0 points apart on a 0 to 100 scale.
Output price
o1 $60.00 against Qwen3.5-Flash $0.26 per million output tokens, 230.8x apart.
Context window
o1 takes 200K 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
Categoryo1BarsQwen3.5-FlashTies
Coding03
Intelligence01
Reasoning07
Math05
Knowledge26
General315
All shared boards537
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
Coding benchmarks for o1 and Qwen3.5-Flash
Benchmarko1Qwen3.5-FlashLeader
Aider polyglot61.7%Not measuredOnly o1 measured
LMArena WebDevNot measured1244Only Qwen3.5-Flash measured
AA Coding Index39.7Not measuredOnly o1 measured
LiveCodeBench (Vals AI)50.3%83.3%Qwen3.5-Flash
SWE-bench Verified (Vals AI)Not measured64.4%Only Qwen3.5-Flash measured
LiveCodeBench (Artificial Analysis run)67.9%Not measuredOnly o1 measured
Terminal-Bench Hard (Artificial Analysis run)12.9%Not measuredOnly o1 measured
WeirdML46.1%Not measuredOnly o1 measured
ALE-BenchNot measured265.9Only Qwen3.5-Flash measured
LMArena Text: coding13671412Qwen3.5-Flash
LMArena Text: software and IT13661416Qwen3.5-Flash
LMArena WebDev: ReactNot measured1229Only Qwen3.5-Flash measured
LMArena WebDev: HTMLNot measured1291Only Qwen3.5-Flash measured
SciCode4.6%Not measuredOnly o1 measured
Neither model has a result on the other 25 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for o1 and Qwen3.5-Flash
Benchmarko1Qwen3.5-FlashLeader
AA Intelligence Index15.2Not measuredOnly o1 measured
Epoch Capabilities Index141.7143.5Qwen3.5-Flash
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Agentic work benchmarks for o1 and Qwen3.5-Flash
Benchmarko1Qwen3.5-FlashLeader
τ²-Bench Telecom62.6%Not measuredOnly o1 measured
METR time horizons51.1%Not measuredOnly o1 measured
Neither model has a result on the other 39 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index07
Reasoning benchmarks for o1 and Qwen3.5-Flash
Benchmarko1Qwen3.5-FlashLeader
GPQA Diamond73.2%82.8%Qwen3.5-Flash
MMMU Pro (Vals AI)77.4%81.9%Qwen3.5-Flash
GPQA Diamond (Artificial Analysis run)74.7%Not measuredOnly o1 measured
Humanity's Last Exam (Artificial Analysis run)7.0%Not measuredOnly o1 measured
GPQA Diamond (Epoch AI run)76.8%82.3%Qwen3.5-Flash
ARC-AGI30.7%Not measuredOnly o1 measured
SimpleBench40.1%Not measuredOnly o1 measured
Chess puzzles10.0%14.0%Qwen3.5-Flash
Mystery game puzzlesNot measured20.0%Only Qwen3.5-Flash measured
VPCT37.0%Not measuredOnly o1 measured
LMArena Text: hard prompts13711403Qwen3.5-Flash
LMArena Text: expert13611407Qwen3.5-Flash
LMArena Text: hard prompts in English13721409Qwen3.5-Flash
Neither model has a result on the other 9 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index05
Math benchmarks for o1 and Qwen3.5-Flash
Benchmarko1Qwen3.5-FlashLeader
AIME71.5%92.5%Qwen3.5-Flash
MATH 500 (Vals AI)90.4%Not measuredOnly o1 measured
MGSM (Vals AI)89.3%Not measuredOnly o1 measured
OTIS Mock AIME73.3%84.4%Qwen3.5-Flash
FrontierMath, tiers 1 to 314.7%18.2%Qwen3.5-Flash
MATH level 594.7%Not measuredOnly o1 measured
LMArena Text: math13881407Qwen3.5-Flash
LMArena Text: mathematical work13901413Qwen3.5-Flash
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index26
Knowledge benchmarks for o1 and Qwen3.5-Flash
Benchmarko1Qwen3.5-FlashLeader
MMLU Pro83.5%84.1%Qwen3.5-Flash
LegalBench (Vals AI)80.4%84.3%Qwen3.5-Flash
CaseLaw (Vals AI)Not measured56.0%Only Qwen3.5-Flash measured
CorpFin (Vals AI)Not measured63.6%Only Qwen3.5-Flash measured
TaxEval (Vals AI)74.3%72.2%o1
MortgageTax (Vals AI)Not measured67.4%Only Qwen3.5-Flash measured
MedQA (Vals AI)96.5%Not measuredOnly o1 measured
MedCode (Vals AI)Not measured33.0%Only Qwen3.5-Flash measured
MMLU Pro (Artificial Analysis run)84.1%Not measuredOnly o1 measured
SimpleQA Verified41.1%20.3%o1
GeoBench80.0%Not measuredOnly o1 measured
LMArena Text: science13691415Qwen3.5-Flash
LMArena Text: medicine and healthcare13551407Qwen3.5-Flash
LMArena Text: legal and government13681398Qwen3.5-Flash
LMArena Text: business and finance13281394Qwen3.5-Flash
Neither model has a result on the other 3 boards here
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Web design benchmarks for o1 and Qwen3.5-Flash
Benchmarko1Qwen3.5-FlashLeader
Neither model has a result on 9 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index315
General benchmarks for o1 and Qwen3.5-Flash
Benchmarko1Qwen3.5-FlashLeader
SAGE (Vals AI)Not measured42.5%Only Qwen3.5-Flash measured
MedScribe (Vals AI)Not measured70.6%Only Qwen3.5-Flash measured
IFBench70.3%Not measuredOnly o1 measured
Long context reasoning (AA-LCR)65.0%Not measuredOnly o1 measured
DTBench74.7%82.9%Qwen3.5-Flash
LMCA22.3%29.1%Qwen3.5-Flash
Fiction.LiveBench83.3%Not measuredOnly o1 measured
Creative writing (Lech Mazur)70.2%Not measuredOnly o1 measured
LMArena Text13661397Qwen3.5-Flash
LMArena Text: instruction following13671374Qwen3.5-Flash
LMArena Text: creative writing13471343o1
LMArena Vision1168Not measuredOnly o1 measured
LMArena Text: English13721404Qwen3.5-Flash
LMArena Text: non-English13581385Qwen3.5-Flash
LMArena Text: Chinese13941445Qwen3.5-Flash
LMArena Text: German13371390Qwen3.5-Flash
LMArena Text: Japanese13461369Qwen3.5-Flash
LMArena Text: Korean13961344o1
LMArena Text: French13441413Qwen3.5-Flash
LMArena Text: Spanish13451400Qwen3.5-Flash
LMArena Text: Russian13561380Qwen3.5-Flash
LMArena Text: PolishNot measured1376Only Qwen3.5-Flash measured
LMArena Text: multi-turn13551393Qwen3.5-Flash
LMArena Text: longer queries13781392Qwen3.5-Flash
LMArena Text: writing and language13601369Qwen3.5-Flash
LMArena Text: entertainment and media13581347o1
LMArena Vision: English1162Not measuredOnly o1 measured
LMArena Vision: Chinese1235Not measuredOnly o1 measured
Neither model has a result on the other 18 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for o1 and Qwen3.5-Flash
Benchmarko1Qwen3.5-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

o1$0.045Fits in one request
Qwen3.5-Flash$0.0002Fits in one request

Qwen3.5-Flash costs less

Repository review

50K input + 3K output tokens

o1$0.930Fits in one request
Qwen3.5-Flash$0.0040Fits in one request

Qwen3.5-Flash costs less

Agent loop

200K input + 10K output tokens

o1$3.60Exceeds the context window
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 o1 and Qwen3.5-Flash
SpecificationOpenAI o1Qwen3.5-Flash
MakerOpenAIQwen
WeightsNo public weights listedNo public weights listed
ReleasedDecember 17, 2024February 25, 2026
Context window200K1M
Input $/Mtok$15.00$0.065
Output $/Mtok$60.00$0.26
Coding indexNot benchmarked41.1
Independent measurements behind it02
Cost per solved taskNot computable$0.40
SourceOpenRouter listingOpenRouter listing

Questions

Is o1 better than Qwen3.5-Flash for coding?

Only Qwen3.5-Flash has a coding score, 41.1 on the Vibe Coding Index; o1 has none, so the two cannot be ranked on coding. Pick o1 for work measured by TaxEval (Vals AI) or SimpleQA Verified, where it leads.

Which is cheaper, o1 or Qwen3.5-Flash?

o1 costs $15.00 per million input tokens and $60.00 per million output tokens; Qwen3.5-Flash costs $0.065 and $0.26. Cost per solved task is known for Qwen3.5-Flash only.

Which has the bigger context window, o1 or Qwen3.5-Flash?

Qwen3.5-Flash, with 1M tokens against 200K.

How many benchmarks is the o1 vs Qwen3.5-Flash comparison based on?

42 benchmarks have a result for both models as of October 22, 2026. o1's coding index rests on 0 independent benchmarks and Qwen3.5-Flash's on 2. Where only one model has a result, the page says so instead of naming a winner.

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