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o1 vs Qwen3.6 35B A3B

Qwen3.6 35B A3B leads o1 on the Artificial Analysis Intelligence Index, 18.2 to 15.2, and Qwen3.6 35B A3B costs 60.0x less per output token ($1.00 vs $60.00 per million). Updated October 21, 2026; 15 benchmarks have a result for both.

Replace o1 Replace Qwen3.6 35B A3B
o1OpenAI
Coding
No data
Overall
44.6
Output $/M
$60.00
3
Qwen3.6 35B A3BQwen
Coding
No data
Overall
45.0
Output $/M
$1.00
12
vs boards won of 15 shared

o1 or Qwen3.6 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 AA Coding Index or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Model Ao1OpenAINot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3.6 35B A3BQwenNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
15
o1 only
54
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
o1 not benchmarked, Qwen3.6 35B A3B not benchmarked.
Widest category
Agentic work, 32.7 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: o1 62.6% against Qwen3.6 35B A3B 95.3%, 32.7 points apart on a 0 to 100 scale.
Output price
o1 $60.00 against Qwen3.6 35B A3B $1.00 per million output tokens, 60.0x apart.
Context window
o1 takes 200K 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
Categoryo1BarsQwen3.6 35B A3BTies
Coding12
Intelligence02
Agentic work01
Reasoning03
Math02
General22
All shared boards312
CodingLike-for-likeSix of these boards feed the Vibe Coding Index12
Coding benchmarks for o1 and Qwen3.6 35B A3B
Benchmarko1Qwen3.6 35B A3BLeader
Aider polyglot61.7%Not measuredOnly o1 measured
AA Coding Index39.741.9Qwen3.6 35B A3B
LiveCodeBench (Vals AI)50.3%Not measuredOnly o1 measured
LiveCodeBench (Artificial Analysis run)67.9%Not measuredOnly o1 measured
SciCodeNot measured36.6%Only Qwen3.6 35B A3B measured
Terminal-Bench Hard (Artificial Analysis run)12.9%34.8%Qwen3.6 35B A3B
Terminal-BenchNot measured24.6%Only Qwen3.6 35B A3B measured
WeirdML46.1%34.5%o1
LMArena Text: coding1367Not measuredOnly o1 measured
LMArena Text: software and IT1366Not measuredOnly o1 measured
SciCode4.6%Not measuredOnly o1 measured
Neither model has a result on the other 28 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for o1 and Qwen3.6 35B A3B
Benchmarko1Qwen3.6 35B A3BLeader
AA Intelligence Index15.218.2Qwen3.6 35B A3B
Epoch Capabilities Index141.7143.7Qwen3.6 35B A3B
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for o1 and Qwen3.6 35B A3B
Benchmarko1Qwen3.6 35B A3BLeader
AA Agentic IndexNot measured13.1Only Qwen3.6 35B A3B measured
τ²-Bench Telecom62.6%95.3%Qwen3.6 35B A3B
METR time horizons51.1%Not measuredOnly o1 measured
Neither model has a result on the other 38 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
Reasoning benchmarks for o1 and Qwen3.6 35B A3B
Benchmarko1Qwen3.6 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%84.1%Qwen3.6 35B A3B
Humanity's Last Exam (Artificial Analysis run)7.0%22.2%Qwen3.6 35B A3B
GPQA Diamond (Epoch AI run)76.8%84.8%Qwen3.6 35B A3B
ARC-AGI30.7%Not measuredOnly o1 measured
SimpleBench40.1%Not measuredOnly o1 measured
Chess puzzles10.0%Not measuredOnly o1 measured
Mystery game puzzlesNot measured22.0%Only Qwen3.6 35B A3B measured
VPCT37.0%Not measuredOnly o1 measured
LMArena Text: hard prompts1371Not measuredOnly o1 measured
LMArena Text: expert1361Not measuredOnly o1 measured
LMArena Text: hard prompts in English1372Not measuredOnly o1 measured
Neither model has a result on the other 9 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for o1 and Qwen3.6 35B A3B
Benchmarko1Qwen3.6 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%86.7%Qwen3.6 35B A3B
FrontierMath, tiers 1 to 314.7%20.4%Qwen3.6 35B A3B
MATH level 594.7%Not measuredOnly o1 measured
LMArena Text: math1388Not measuredOnly o1 measured
LMArena Text: mathematical work1390Not measuredOnly o1 measured
Neither model has a result on the other 5 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
Knowledge benchmarks for o1 and Qwen3.6 35B A3B
Benchmarko1Qwen3.6 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: science1369Not measuredOnly o1 measured
LMArena Text: medicine and healthcare1355Not measuredOnly o1 measured
LMArena Text: legal and government1368Not measuredOnly o1 measured
LMArena Text: business and finance1328Not measuredOnly o1 measured
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 Qwen3.6 35B A3B
Benchmarko1Qwen3.6 35B A3BLeader
Neither model has a result on 9 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index22
General benchmarks for o1 and Qwen3.6 35B A3B
Benchmarko1Qwen3.6 35B A3BLeader
IFBench70.3%64.4%o1
Long context reasoning (AA-LCR)65.0%71.7%Qwen3.6 35B A3B
DTBench74.7%73.9%o1
LMCA22.3%29.7%Qwen3.6 35B A3B
Fiction.LiveBench83.3%Not measuredOnly o1 measured
Creative writing (Lech Mazur)70.2%Not measuredOnly o1 measured
Surface Evolver BenchNot measured44.4%Only Qwen3.6 35B A3B measured
LMArena Text1366Not measuredOnly o1 measured
LMArena Text: instruction following1367Not measuredOnly o1 measured
LMArena Text: creative writing1347Not measuredOnly o1 measured
LMArena Vision1168Not measuredOnly o1 measured
LMArena Text: English1372Not measuredOnly o1 measured
LMArena Text: non-English1358Not measuredOnly o1 measured
LMArena Text: Chinese1394Not measuredOnly o1 measured
LMArena Text: German1337Not measuredOnly o1 measured
LMArena Text: Japanese1346Not measuredOnly o1 measured
LMArena Text: Korean1396Not measuredOnly o1 measured
LMArena Text: French1344Not measuredOnly o1 measured
LMArena Text: Spanish1345Not measuredOnly o1 measured
LMArena Text: Russian1356Not measuredOnly o1 measured
LMArena Text: multi-turn1355Not measuredOnly o1 measured
LMArena Text: longer queries1378Not measuredOnly o1 measured
LMArena Text: writing and language1360Not measuredOnly o1 measured
LMArena Text: entertainment and media1358Not measuredOnly o1 measured
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 Qwen3.6 35B A3B
Benchmarko1Qwen3.6 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

o1$0.045Fits 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

o1$0.930Fits 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

o1$3.60Exceeds the context window
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 o1 and Qwen3.6 35B A3B
SpecificationOpenAI o1Qwen3.6 35B A3B
MakerOpenAIQwen
WeightsNo public weights listedOpen weights
ReleasedDecember 17, 2024April 27, 2026
Context window200K262K
Input $/Mtok$15.00$0.10
Output $/Mtok$60.00$1.00
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is o1 better than Qwen3.6 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 AA Coding Index or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Which is cheaper, o1 or Qwen3.6 35B A3B?

o1 costs $15.00 per million input tokens and $60.00 per million output tokens; Qwen3.6 35B A3B costs $0.10 and $1.00. Cost per solved task is known for neither of them.

Which has the bigger context window, o1 or Qwen3.6 35B A3B?

Qwen3.6 35B A3B, with 262K tokens against 200K.

How many benchmarks is the o1 vs Qwen3.6 35B A3B comparison based on?

15 benchmarks have a result for both models as of October 21, 2026. o1's coding index rests on 0 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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