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o1 vs Qwen3.5 397B A17B

Updated October 21, 2026. 43 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace o1 Replace Qwen3.5 397B A17B
o1OpenAI
Coding
61.7
Overall
15.2
Output $/M
$60.00
1
Qwen3.5 397B A17BQwen
Coding
28.6
Overall
21.4
Output $/M
$2.34
42
vs boards won of 43 shared

Decision reading

Pick o1 for coding: it scores 61.7 on the Vibe Coding Index against 28.6 for Qwen3.5 397B A17B.

Pick Qwen3.5 397B A17B when output tokens cost 25.6x less, or when the context window is larger, 262K against 200K tokens.

Model Ao1OpenAI61.7/1001 independent benchmarkNo cost per solved taskModel BQwen3.5 397B A17BQwen28.6/1001 independent benchmarkNo cost per solved task
Shared results
43
o1 only
25
Qwen3.5 397B A17B only
22
Like-for-like categories
7 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
o1 61.7 from 1 benchmark, Qwen3.5 397B A17B 28.6 from 1 benchmark.
Widest category
Agentic work, 33.0 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: o1 62.6% against Qwen3.5 397B A17B 95.6%, 33.0 points apart on a 0 to 100 scale.
Output price
o1 $60.00 against Qwen3.5 397B A17B $2.34 per million output tokens, 25.6x apart.
Context window
o1 takes 200K tokens, Qwen3.5 397B A17B 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 397B A17BTies
Coding03
Intelligence02
Agentic work01
Reasoning06
Math04
Knowledge04
General122
All shared boards142
CodingLike-for-likeFour of these boards feed the Vibe Coding Index03
Coding benchmarks for o1 and Qwen3.5 397B A17B
Benchmarko1Qwen3.5 397B A17BLeader
Aider polyglot61.7%Not measuredOnly o1 measured
LMArena WebDevNot measured1400Only Qwen3.5 397B A17B 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
SciCodeNot measured44.8%Only Qwen3.5 397B A17B measured
Terminal-Bench Hard (Artificial Analysis run)12.9%40.9%Qwen3.5 397B A17B
WeirdML46.1%Not measuredOnly o1 measured
ALE-BenchNot measured621.9Only Qwen3.5 397B A17B measured
LMArena Text: coding13671464Qwen3.5 397B A17B
LMArena Text: software and IT13661463Qwen3.5 397B A17B
LMArena WebDev: ReactNot measured1388Only Qwen3.5 397B A17B measured
LMArena WebDev: HTMLNot measured1410Only Qwen3.5 397B A17B measured
Neither model has a result on the other 25 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for o1 and Qwen3.5 397B A17B
Benchmarko1Qwen3.5 397B A17BLeader
AA Intelligence Index15.221.4Qwen3.5 397B A17B
Epoch Capabilities Index141.7146.8Qwen3.5 397B A17B
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for o1 and Qwen3.5 397B A17B
Benchmarko1Qwen3.5 397B A17BLeader
τ²-Bench Telecom62.6%95.6%Qwen3.5 397B A17B
APEX-AgentsNot measured24.9%Only Qwen3.5 397B A17B measured
METR time horizons51.1%Not measuredOnly o1 measured
Neither model has a result on the other 36 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
Reasoning benchmarks for o1 and Qwen3.5 397B A17B
Benchmarko1Qwen3.5 397B A17BLeader
GPQA Diamond73.2%Not measuredOnly o1 measured
MMMU Pro (Vals AI)77.4%Not measuredOnly o1 measured
GPQA Diamond (Artificial Analysis run)74.7%89.3%Qwen3.5 397B A17B
Humanity's Last Exam (Artificial Analysis run)7.0%29.0%Qwen3.5 397B A17B
GPQA Diamond (Epoch AI run)76.8%86.4%Qwen3.5 397B A17B
ARC-AGI30.7%Not measuredOnly o1 measured
SimpleBench40.1%Not measuredOnly o1 measured
Chess puzzles10.0%Not measuredOnly o1 measured
Mystery game puzzlesNot measured18.0%Only Qwen3.5 397B A17B measured
VPCT37.0%Not measuredOnly o1 measured
LMArena Text: hard prompts13711448Qwen3.5 397B A17B
LMArena Text: expert13611462Qwen3.5 397B A17B
LMArena Text: hard prompts in English13721450Qwen3.5 397B A17B
LMArena Vision: diagramsNot measured1277Only Qwen3.5 397B A17B measured
LMArena Vision: homeworkNot measured1292Only Qwen3.5 397B A17B measured
Neither model has a result on the other 6 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index04
Math benchmarks for o1 and Qwen3.5 397B A17B
Benchmarko1Qwen3.5 397B A17BLeader
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%88.9%Qwen3.5 397B A17B
FrontierMath, tiers 1 to 314.7%31.2%Qwen3.5 397B A17B
MATH level 594.7%Not measuredOnly o1 measured
LMArena Text: math13881453Qwen3.5 397B A17B
LMArena Text: mathematical work13901454Qwen3.5 397B A17B
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 397B A17B
Benchmarko1Qwen3.5 397B A17BLeader
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: science13691457Qwen3.5 397B A17B
LMArena Text: medicine and healthcare13551455Qwen3.5 397B A17B
LMArena Text: legal and government13681441Qwen3.5 397B A17B
LMArena Text: business and finance13281435Qwen3.5 397B A17B
Neither model has a result on the other 6 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Web design benchmarks for o1 and Qwen3.5 397B A17B
Benchmarko1Qwen3.5 397B A17BLeader
Design Arena: websitesNot measured1191Only Qwen3.5 397B A17B measured
Design Arena: UI componentsNot measured1163Only Qwen3.5 397B A17B measured
Design Arena: game devNot measured1141Only Qwen3.5 397B A17B measured
Design Arena: data vizNot measured1175Only Qwen3.5 397B A17B measured
Design Arena: 3DNot measured1161Only Qwen3.5 397B A17B measured
Design Arena: all code categoriesNot measured1180Only Qwen3.5 397B A17B measured
Design Arena: SVGNot measured1139Only Qwen3.5 397B A17B measured
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index122
General benchmarks for o1 and Qwen3.5 397B A17B
Benchmarko1Qwen3.5 397B A17BLeader
IFBench70.3%78.8%Qwen3.5 397B A17B
Long context reasoning (AA-LCR)65.0%77.3%Qwen3.5 397B A17B
DTBench74.7%87.5%Qwen3.5 397B A17B
LMCA22.3%37.9%Qwen3.5 397B A17B
Fiction.LiveBench83.3%Not measuredOnly o1 measured
Creative writing (Lech Mazur)70.2%Not measuredOnly o1 measured
LMArena Text13661437Qwen3.5 397B A17B
LMArena Text: instruction following13671424Qwen3.5 397B A17B
LMArena Text: creative writing13471402Qwen3.5 397B A17B
LMArena Vision11681262Qwen3.5 397B A17B
LMArena Text: English13721443Qwen3.5 397B A17B
LMArena Text: non-English13581430Qwen3.5 397B A17B
LMArena Text: Chinese13941498Qwen3.5 397B A17B
LMArena Text: German13371445Qwen3.5 397B A17B
LMArena Text: Japanese13461429Qwen3.5 397B A17B
LMArena Text: Korean13961384o1
LMArena Text: French13441462Qwen3.5 397B A17B
LMArena Text: Spanish13451441Qwen3.5 397B A17B
LMArena Text: Russian13561430Qwen3.5 397B A17B
LMArena Text: PolishNot measured1447Only Qwen3.5 397B A17B measured
LMArena Text: multi-turn13551446Qwen3.5 397B A17B
LMArena Text: longer queries13781441Qwen3.5 397B A17B
LMArena Text: writing and language13601415Qwen3.5 397B A17B
LMArena Text: entertainment and media13581399Qwen3.5 397B A17B
LMArena Vision: English11621268Qwen3.5 397B A17B
LMArena Vision: Chinese12351314Qwen3.5 397B A17B
LMArena Vision: reading textNot measured1271Only Qwen3.5 397B A17B measured
LMArena Vision: humorNot measured1262Only Qwen3.5 397B A17B measured
LMArena Vision: captioningNot measured1230Only Qwen3.5 397B A17B measured
LMArena Vision: recognizing thingsNot measured1265Only Qwen3.5 397B A17B measured
LMArena Vision: creative writingNot measured1260Only Qwen3.5 397B A17B 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 397B A17B
Benchmarko1Qwen3.5 397B A17BLeader
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 397B A17B$0.0016Fits in one request

Qwen3.5 397B A17B costs less

Repository review

50K input + 3K output tokens

o1$0.930Fits in one request
Qwen3.5 397B A17B$0.027Fits in one request

Qwen3.5 397B A17B costs less

Agent loop

200K input + 10K output tokens

o1$3.60Exceeds the context window
Qwen3.5 397B A17B$0.101Fits in one request

Qwen3.5 397B A17B 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 397B A17B
SpecificationOpenAI o1Qwen3.5 397B A17B
MakerOpenAIQwen
WeightsNo public weights listedOpen weights
ReleasedDecember 17, 2024February 16, 2026
Context window200K262K
Input $/Mtok$15.00$0.39
Output $/Mtok$60.00$2.34
Coding index61.728.6
Independent measurements behind it11
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is o1 better than Qwen3.5 397B A17B for coding?

Pick o1 for coding: it scores 61.7 on the Vibe Coding Index against 28.6 for Qwen3.5 397B A17B. Pick Qwen3.5 397B A17B when output tokens cost 25.6x less, or when the context window is larger, 262K against 200K tokens.

Which is cheaper, o1 or Qwen3.5 397B A17B?

o1 costs $15.00 per million input tokens and $60.00 per million output tokens; Qwen3.5 397B A17B costs $0.39 and $2.34. Cost per solved task is known for neither of them.

Which has the bigger context window, o1 or Qwen3.5 397B A17B?

Qwen3.5 397B A17B, with 262K tokens against 200K.

How many benchmarks is the o1 vs Qwen3.5 397B A17B comparison based on?

43 benchmarks have a result for both models as of October 21, 2026. o1's coding index rests on 1 independent benchmark and Qwen3.5 397B A17B's on 1. Where only one model has a result, the page says so instead of naming a winner.

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