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o1 vs Qwen3.5-27B

Qwen3.5-27B leads o1 on the Artificial Analysis Intelligence Index, 22.9 to 15.2, and Qwen3.5-27B costs 38.5x less per output token ($1.56 vs $60.00 per million). Updated October 22, 2026; 40 benchmarks have a result for both.

Replace o1 Replace Qwen3.5-27B
o1OpenAI
Coding
No data
Overall
44.6
Output $/M
$60.00
3
Qwen3.5-27BQwen
Coding
No data
Overall
No data
Output $/M
$1.56
37
vs boards won of 40 shared

o1 or Qwen3.5-27B: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Pick o1 for work measured by Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.

Model Ao1OpenAINot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3.5-27BQwenNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
40
o1 only
29
Qwen3.5-27B only
13
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.

At a glance

Coding index
o1 not benchmarked, Qwen3.5-27B not benchmarked.
Widest category
Agentic work, 31.3 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: o1 62.6% against Qwen3.5-27B 93.9%, 31.3 points apart on a 0 to 100 scale.
Output price
o1 $60.00 against Qwen3.5-27B $1.56 per million output tokens, 38.5x apart.
Context window
o1 takes 200K tokens, Qwen3.5-27B 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
Categoryo1BarsQwen3.5-27BTies
Coding13
Intelligence01
Agentic work01
Reasoning05
Math02
Knowledge04
General221
All shared boards337
CodingLike-for-likeSix of these boards feed the Vibe Coding Index13
Coding benchmarks for o1 and Qwen3.5-27B
Benchmarko1Qwen3.5-27BLeader
Aider polyglot61.7%Not measuredOnly o1 measured
LMArena WebDevNot measured1358Only Qwen3.5-27B measured
AA Coding Index39.7Not measuredOnly o1 measured
LiveCodeBench (Vals AI)50.3%Not measuredOnly o1 measured
LiveCodeBench (Artificial Analysis run)67.9%Not measuredOnly o1 measured
Terminal-Bench Hard (Artificial Analysis run)12.9%32.6%Qwen3.5-27B
WeirdML46.1%39.5%o1
ALE-BenchNot measured349.4Only Qwen3.5-27B measured
LMArena Text: coding13671427Qwen3.5-27B
LMArena Text: software and IT13661431Qwen3.5-27B
LMArena WebDev: ReactNot measured1346Only Qwen3.5-27B measured
LMArena WebDev: HTMLNot measured1386Only Qwen3.5-27B measured
SciCode4.6%Not measuredOnly o1 measured
Neither model has a result on the other 26 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for o1 and Qwen3.5-27B
Benchmarko1Qwen3.5-27BLeader
AA Intelligence Index15.222.9Qwen3.5-27B
Epoch Capabilities Index141.7Not measuredOnly o1 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for o1 and Qwen3.5-27B
Benchmarko1Qwen3.5-27BLeader
τ²-Bench Telecom62.6%93.9%Qwen3.5-27B
METR time horizons51.1%Not measuredOnly o1 measured
Vending-Bench 2Not measured$202Only Qwen3.5-27B measured
Neither model has a result on the other 38 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for o1 and Qwen3.5-27B
Benchmarko1Qwen3.5-27BLeader
GPQA Diamond73.2%Not measuredOnly o1 measured
MMMU Pro (Vals AI)77.4%Not measuredOnly o1 measured
GPQA Diamond (Artificial Analysis run)74.7%85.8%Qwen3.5-27B
Humanity's Last Exam (Artificial Analysis run)7.0%23.9%Qwen3.5-27B
GPQA Diamond (Epoch AI run)76.8%Not measuredOnly o1 measured
ARC-AGI30.7%Not measuredOnly o1 measured
SimpleBench40.1%Not measuredOnly o1 measured
Chess puzzles10.0%Not measuredOnly o1 measured
VPCT37.0%Not measuredOnly o1 measured
LMArena Text: hard prompts13711415Qwen3.5-27B
LMArena Text: expert13611427Qwen3.5-27B
LMArena Text: hard prompts in English13721429Qwen3.5-27B
LMArena Vision: diagramsNot measured1249Only Qwen3.5-27B measured
LMArena Vision: homeworkNot measured1256Only Qwen3.5-27B measured
Neither model has a result on the other 8 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for o1 and Qwen3.5-27B
Benchmarko1Qwen3.5-27BLeader
AIME71.5%Not measuredOnly o1 measured
MATH 500 (Vals AI)90.4%Not measuredOnly o1 measured
MGSM (Vals AI)89.3%Not measuredOnly o1 measured
OTIS Mock AIME73.3%Not measuredOnly o1 measured
FrontierMath, tiers 1 to 314.7%Not measuredOnly o1 measured
MATH level 594.7%Not measuredOnly o1 measured
LMArena Text: math13881429Qwen3.5-27B
LMArena Text: mathematical work13901429Qwen3.5-27B
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for o1 and Qwen3.5-27B
Benchmarko1Qwen3.5-27BLeader
MMLU Pro83.5%Not measuredOnly o1 measured
LegalBench (Vals AI)80.4%Not measuredOnly o1 measured
TaxEval (Vals AI)74.3%Not measuredOnly o1 measured
MedQA (Vals AI)96.5%Not measuredOnly o1 measured
MMLU Pro (Artificial Analysis run)84.1%Not measuredOnly o1 measured
SimpleQA Verified41.1%Not measuredOnly o1 measured
GeoBench80.0%Not measuredOnly o1 measured
LMArena Text: science13691437Qwen3.5-27B
LMArena Text: medicine and healthcare13551425Qwen3.5-27B
LMArena Text: legal and government13681405Qwen3.5-27B
LMArena Text: business and finance13281399Qwen3.5-27B
Neither model has a result on the other 7 boards here
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Web design benchmarks for o1 and Qwen3.5-27B
Benchmarko1Qwen3.5-27BLeader
Neither model has a result on 9 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index221
General benchmarks for o1 and Qwen3.5-27B
Benchmarko1Qwen3.5-27BLeader
IFBench70.3%75.6%Qwen3.5-27B
Long context reasoning (AA-LCR)65.0%77.7%Qwen3.5-27B
DTBench74.7%82.4%Qwen3.5-27B
LMCA22.3%34.0%Qwen3.5-27B
Fiction.LiveBench83.3%Not measuredOnly o1 measured
Creative writing (Lech Mazur)70.2%Not measuredOnly o1 measured
LMArena Text13661409Qwen3.5-27B
LMArena Text: instruction following13671393Qwen3.5-27B
LMArena Text: creative writing13471362Qwen3.5-27B
LMArena Vision11681241Qwen3.5-27B
LMArena Text: English13721425Qwen3.5-27B
LMArena Text: non-English13581390Qwen3.5-27B
LMArena Text: Chinese13941478Qwen3.5-27B
LMArena Text: German13371392Qwen3.5-27B
LMArena Text: Japanese13461346o1
LMArena Text: Korean13961359o1
LMArena Text: French13441410Qwen3.5-27B
LMArena Text: Spanish13451407Qwen3.5-27B
LMArena Text: Russian13561390Qwen3.5-27B
LMArena Text: PolishNot measured1375Only Qwen3.5-27B measured
LMArena Text: multi-turn13551410Qwen3.5-27B
LMArena Text: longer queries13781413Qwen3.5-27B
LMArena Text: writing and language13601380Qwen3.5-27B
LMArena Text: entertainment and media13581363Qwen3.5-27B
LMArena Vision: English11621246Qwen3.5-27B
LMArena Vision: Chinese12351301Qwen3.5-27B
LMArena Vision: reading textNot measured1246Only Qwen3.5-27B measured
LMArena Vision: humorNot measured1227Only Qwen3.5-27B measured
LMArena Vision: captioningNot measured1170Only Qwen3.5-27B measured
LMArena Vision: recognizing thingsNot measured1210Only Qwen3.5-27B measured
LMArena Vision: creative writingNot measured1224Only Qwen3.5-27B measured
Neither model has a result on the other 15 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for o1 and Qwen3.5-27B
Benchmarko1Qwen3.5-27BLeader
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
Qwen3.5-27B$0.0010Fits in one request

Qwen3.5-27B costs less

Repository review

50K input + 3K output tokens

o1$0.930Fits in one request
Qwen3.5-27B$0.014Fits in one request

Qwen3.5-27B costs less

Agent loop

200K input + 10K output tokens

o1$3.60Exceeds the context window
Qwen3.5-27B$0.055Fits in one request

Qwen3.5-27B 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 Qwen3.5-27B
SpecificationOpenAI o1Qwen3.5-27B
MakerOpenAIQwen
WeightsNo public weights listedOpen weights
ReleasedDecember 17, 2024February 25, 2026
Context window200K262K
Input $/Mtok$15.00$0.195
Output $/Mtok$60.00$1.56
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it01
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is o1 better than Qwen3.5-27B for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick o1 for work measured by Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.

Which is cheaper, o1 or Qwen3.5-27B?

o1 costs $15.00 per million input tokens and $60.00 per million output tokens; Qwen3.5-27B costs $0.195 and $1.56. Cost per solved task is known for neither of them.

Which has the bigger context window, o1 or Qwen3.5-27B?

Qwen3.5-27B, with 262K tokens against 200K.

How many benchmarks is the o1 vs Qwen3.5-27B comparison based on?

40 benchmarks have a result for both models as of October 22, 2026. o1's coding index rests on 0 independent benchmarks and Qwen3.5-27B's on 1. Where only one model has a result, the page says so instead of naming a winner.

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