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o1 vs Qwen3.5-35B-A3B

Qwen3.5-35B-A3B leads o1 on the Artificial Analysis Intelligence Index, 19.3 to 15.2, and Qwen3.5-35B-A3B costs 46.2x less per output token ($1.30 vs $60.00 per million). Updated October 22, 2026; 39 benchmarks have a result for both.

Replace o1 Replace Qwen3.5-35B-A3B
o1OpenAI
Coding
No data
Overall
44.6
Output $/M
$60.00
5
Qwen3.5-35B-A3BQwen
Coding
No data
Overall
53.5
Output $/M
$1.30
34
vs boards won of 39 shared

o1 or Qwen3.5-35B-A3B: 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 BQwen3.5-35B-A3BQwenNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
39
o1 only
30
Qwen3.5-35B-A3B only
6
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, Qwen3.5-35B-A3B not benchmarked.
Widest category
Agentic work, 26.6 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: o1 62.6% against Qwen3.5-35B-A3B 89.2%, 26.6 points apart on a 0 to 100 scale.
Output price
o1 $60.00 against Qwen3.5-35B-A3B $1.30 per million output tokens, 46.2x apart.
Context window
o1 takes 200K 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.

Boards won, where both have a result
Categoryo1BarsQwen3.5-35B-A3BTies
Coding03
Intelligence02
Agentic work01
Reasoning06
Math12
Knowledge04
General416
All shared boards534
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
Coding benchmarks for o1 and Qwen3.5-35B-A3B
Benchmarko1Qwen3.5-35B-A3BLeader
Aider polyglot61.7%Not measuredOnly o1 measured
LMArena WebDevNot measured1254Only Qwen3.5-35B-A3B 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%26.5%Qwen3.5-35B-A3B
WeirdML46.1%Not measuredOnly o1 measured
ALE-BenchNot measured221.8Only Qwen3.5-35B-A3B measured
LMArena Text: coding13671410Qwen3.5-35B-A3B
LMArena Text: software and IT13661415Qwen3.5-35B-A3B
LMArena WebDev: ReactNot measured1238Only Qwen3.5-35B-A3B measured
LMArena WebDev: HTMLNot measured1301Only Qwen3.5-35B-A3B 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 index02
Intelligence benchmarks for o1 and Qwen3.5-35B-A3B
Benchmarko1Qwen3.5-35B-A3BLeader
AA Intelligence Index15.219.3Qwen3.5-35B-A3B
Epoch Capabilities Index141.7142.5Qwen3.5-35B-A3B
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for o1 and Qwen3.5-35B-A3B
Benchmarko1Qwen3.5-35B-A3BLeader
τ²-Bench Telecom62.6%89.2%Qwen3.5-35B-A3B
METR time horizons51.1%Not measuredOnly o1 measured
Vending-Bench 2Not measured$463Only Qwen3.5-35B-A3B measured
Neither model has a result on the other 38 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
Reasoning benchmarks for o1 and Qwen3.5-35B-A3B
Benchmarko1Qwen3.5-35B-A3BLeader
GPQA Diamond73.2%Not measuredOnly o1 measured
MMMU Pro (Vals AI)77.4%Not measuredOnly o1 measured
GPQA Diamond (Artificial Analysis run)74.7%84.5%Qwen3.5-35B-A3B
Humanity's Last Exam (Artificial Analysis run)7.0%21.0%Qwen3.5-35B-A3B
GPQA Diamond (Epoch AI run)76.8%83.5%Qwen3.5-35B-A3B
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 prompts13711400Qwen3.5-35B-A3B
LMArena Text: expert13611408Qwen3.5-35B-A3B
LMArena Text: hard prompts in English13721411Qwen3.5-35B-A3B
Neither model has a result on the other 10 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index12
Math benchmarks for o1 and Qwen3.5-35B-A3B
Benchmarko1Qwen3.5-35B-A3BLeader
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%70.0%o1
FrontierMath, tiers 1 to 314.7%Not measuredOnly o1 measured
MATH level 594.7%Not measuredOnly o1 measured
LMArena Text: math13881404Qwen3.5-35B-A3B
LMArena Text: mathematical work13901410Qwen3.5-35B-A3B
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 Qwen3.5-35B-A3B
Benchmarko1Qwen3.5-35B-A3BLeader
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: science13691413Qwen3.5-35B-A3B
LMArena Text: medicine and healthcare13551421Qwen3.5-35B-A3B
LMArena Text: legal and government13681399Qwen3.5-35B-A3B
LMArena Text: business and finance13281394Qwen3.5-35B-A3B
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 Qwen3.5-35B-A3B
Benchmarko1Qwen3.5-35B-A3BLeader
Neither model has a result on 9 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index416
General benchmarks for o1 and Qwen3.5-35B-A3B
Benchmarko1Qwen3.5-35B-A3BLeader
IFBench70.3%72.5%Qwen3.5-35B-A3B
Long context reasoning (AA-LCR)65.0%72.0%Qwen3.5-35B-A3B
DTBench74.7%80.0%Qwen3.5-35B-A3B
LMCA22.3%29.5%Qwen3.5-35B-A3B
Fiction.LiveBench83.3%Not measuredOnly o1 measured
Creative writing (Lech Mazur)70.2%Not measuredOnly o1 measured
LMArena Text13661395Qwen3.5-35B-A3B
LMArena Text: instruction following13671379Qwen3.5-35B-A3B
LMArena Text: creative writing13471347o1
LMArena Vision1168Not measuredOnly o1 measured
LMArena Text: English13721407Qwen3.5-35B-A3B
LMArena Text: non-English13581378Qwen3.5-35B-A3B
LMArena Text: Chinese13941457Qwen3.5-35B-A3B
LMArena Text: German13371367Qwen3.5-35B-A3B
LMArena Text: Japanese13461326o1
LMArena Text: Korean13961355o1
LMArena Text: French13441412Qwen3.5-35B-A3B
LMArena Text: Spanish13451390Qwen3.5-35B-A3B
LMArena Text: Russian13561376Qwen3.5-35B-A3B
LMArena Text: PolishNot measured1385Only Qwen3.5-35B-A3B measured
LMArena Text: multi-turn13551390Qwen3.5-35B-A3B
LMArena Text: longer queries13781389Qwen3.5-35B-A3B
LMArena Text: writing and language13601365Qwen3.5-35B-A3B
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 20 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for o1 and Qwen3.5-35B-A3B
Benchmarko1Qwen3.5-35B-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

o1$0.045Fits in one request
Qwen3.5-35B-A3B$0.0008Fits in one request

Qwen3.5-35B-A3B costs less

Repository review

50K input + 3K output tokens

o1$0.930Fits in one request
Qwen3.5-35B-A3B$0.012Fits in one request

Qwen3.5-35B-A3B costs less

Agent loop

200K input + 10K output tokens

o1$3.60Exceeds the context window
Qwen3.5-35B-A3B$0.045Fits in one request

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.

Specifications of o1 and Qwen3.5-35B-A3B
SpecificationOpenAI o1Qwen3.5-35B-A3B
MakerOpenAIQwen
WeightsNo public weights listedOpen weights
ReleasedDecember 17, 2024February 25, 2026
Context window200K262K
Input $/Mtok$15.00$0.1625
Output $/Mtok$60.00$1.30
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it01
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is o1 better than Qwen3.5-35B-A3B 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 Qwen3.5-35B-A3B?

o1 costs $15.00 per million input tokens and $60.00 per million output tokens; Qwen3.5-35B-A3B costs $0.1625 and $1.30. Cost per solved task is known for neither of them.

Which has the bigger context window, o1 or Qwen3.5-35B-A3B?

Qwen3.5-35B-A3B, with 262K tokens against 200K.

How many benchmarks is the o1 vs Qwen3.5-35B-A3B comparison based on?

39 benchmarks have a result for both models as of October 22, 2026. o1's coding index rests on 0 independent benchmarks and Qwen3.5-35B-A3B's on 1. Where only one model has a result, the page says so instead of naming a winner.

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