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o1 vs Qwen2.5 72B Instruct

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 Qwen2.5 72B Instruct
o1OpenAI
Coding
No data
Overall
15.2
Output $/M
$60.00
43
Qwen2.5 72B InstructQwen
Coding
No data
Overall
7.7
Output $/M
$0.40
0
vs boards won of 43 shared

Decision reading

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

o1 has the edge where the context window is larger, 200K against 33K tokens.

Model Ao1OpenAINot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen2.5 72B InstructQwenNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
43
o1 only
25
Qwen2.5 72B Instruct only
10
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 not benchmarked, Qwen2.5 72B Instruct not benchmarked.
Widest category
Math, 32.7 points apart on average over 4 shared benchmarks.
Widest single gap
OTIS Mock AIME: o1 73.3% against Qwen2.5 72B Instruct 8.1%, 65.3 points apart on a 0 to 100 scale.
Output price
o1 $60.00 against Qwen2.5 72B Instruct $0.40 per million output tokens, 150.0x apart.
Context window
o1 takes 200K tokens, Qwen2.5 72B Instruct 33K.

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
Categoryo1BarsQwen2.5 72B InstructTies
Coding50
Intelligence20
Agentic work20
Reasoning60
Math40
Knowledge50
General190
All shared boards430
CodingLike-for-likeSix of these boards feed the Vibe Coding Index50
Coding benchmarks for o1 and Qwen2.5 72B Instruct
Benchmarko1Qwen2.5 72B InstructLeader
Aider polyglot61.7%Not measuredOnly o1 measured
AA Coding Index39.7Not measuredOnly o1 measured
LiveCodeBench (Vals AI)50.3%Not measuredOnly o1 measured
LiveCodeBench (Artificial Analysis run)67.9%27.6%o1
Terminal-Bench Hard (Artificial Analysis run)12.9%4.5%o1
WeirdML46.1%16.0%o1
LMArena Text: coding13671292o1
LMArena Text: software and IT13661292o1
Neither model has a result on the other 30 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for o1 and Qwen2.5 72B Instruct
Benchmarko1Qwen2.5 72B InstructLeader
AA Intelligence Index15.27.7o1
Epoch Capabilities Index141.7129.0o1
Agentic workLike-for-likePublic boards, shown as published, kept out of the index20
Agentic work benchmarks for o1 and Qwen2.5 72B Instruct
Benchmarko1Qwen2.5 72B InstructLeader
τ²-Bench Telecom62.6%34.5%o1
METR time horizons51.1%35.8%o1
BALROGNot measured16.2%Only Qwen2.5 72B Instruct measured
TheAgentCompanyNot measured5.7%Only Qwen2.5 72B Instruct measured
Neither model has a result on the other 35 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
Reasoning benchmarks for o1 and Qwen2.5 72B Instruct
Benchmarko1Qwen2.5 72B InstructLeader
GPQA Diamond73.2%Not measuredOnly o1 measured
MMMU Pro (Vals AI)77.4%Not measuredOnly o1 measured
GPQA Diamond (Artificial Analysis run)74.7%49.1%o1
Humanity's Last Exam (Artificial Analysis run)7.0%3.6%o1
GPQA Diamond (Epoch AI run)76.8%49.1%o1
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
BIG-Bench HardNot measured79.8%Only Qwen2.5 72B Instruct measured
LMArena Text: hard prompts13711271o1
LMArena Text: expert13611245o1
LMArena Text: hard prompts in English13721279o1
Neither model has a result on the other 8 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index40
Math benchmarks for o1 and Qwen2.5 72B Instruct
Benchmarko1Qwen2.5 72B InstructLeader
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
AA Math IndexNot measured14.0Only Qwen2.5 72B Instruct measured
AIME 2025 (Artificial Analysis run)Not measured14.0%Only Qwen2.5 72B Instruct measured
OTIS Mock AIME73.3%8.1%o1
FrontierMath, tiers 1 to 314.7%Not measuredOnly o1 measured
MATH level 594.7%63.2%o1
LMArena Text: math13881283o1
LMArena Text: mathematical work13901290o1
Neither model has a result on the other 3 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
Knowledge benchmarks for o1 and Qwen2.5 72B Instruct
Benchmarko1Qwen2.5 72B InstructLeader
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%72.0%o1
SimpleQA Verified41.1%Not measuredOnly o1 measured
MMLUNot measured85.3%Only Qwen2.5 72B Instruct measured
GeoBench80.0%Not measuredOnly o1 measured
AI2 Reasoning ChallengeNot measured94.5%Only Qwen2.5 72B Instruct measured
LMArena Text: science13691275o1
LMArena Text: medicine and healthcare13551258o1
LMArena Text: legal and government13681288o1
LMArena Text: business and finance13281253o1
Neither model has a result on the other 4 boards here
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Web design benchmarks for o1 and Qwen2.5 72B Instruct
Benchmarko1Qwen2.5 72B InstructLeader
Neither model has a result on 9 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index190
General benchmarks for o1 and Qwen2.5 72B Instruct
Benchmarko1Qwen2.5 72B InstructLeader
IFBench70.3%36.9%o1
Long context reasoning (AA-LCR)65.0%Not measuredOnly o1 measured
DTBench74.7%62.9%o1
LMCA22.3%13.4%o1
Fiction.LiveBench83.3%Not measuredOnly o1 measured
Creative writing (Lech Mazur)70.2%Not measuredOnly o1 measured
HellaSwagNot measured84.8%Only Qwen2.5 72B Instruct measured
WinoGrandeNot measured82.3%Only Qwen2.5 72B Instruct measured
PIQANot measured82.6%Only Qwen2.5 72B Instruct measured
LMArena Text13661269o1
LMArena Text: instruction following13671254o1
LMArena Text: creative writing13471221o1
LMArena Vision1168Not measuredOnly o1 measured
LMArena Text: English13721283o1
LMArena Text: non-English13581252o1
LMArena Text: Chinese13941273o1
LMArena Text: German13371234o1
LMArena Text: Japanese13461180o1
LMArena Text: Korean13961188o1
LMArena Text: French13441280o1
LMArena Text: Spanish13451255o1
LMArena Text: Russian13561264o1
LMArena Text: multi-turn13551272o1
LMArena Text: longer queries13781281o1
LMArena Text: writing and language13601247o1
LMArena Text: entertainment and media13581211o1
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 Qwen2.5 72B Instruct
Benchmarko1Qwen2.5 72B InstructLeader
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
Qwen2.5 72B Instruct$0.0006Fits in one request

Qwen2.5 72B Instruct costs less

Repository review

50K input + 3K output tokens

o1$0.930Fits in one request
Qwen2.5 72B Instruct$0.019Exceeds the context window

Qwen2.5 72B Instruct costs less

Agent loop

200K input + 10K output tokens

o1$3.60Exceeds the context window
Qwen2.5 72B Instruct$0.076Exceeds the context window

Qwen2.5 72B Instruct 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 Qwen2.5 72B Instruct
SpecificationOpenAI o1Qwen2.5 72B Instruct
MakerOpenAIQwen
WeightsNo public weights listedOpen weights
ReleasedDecember 17, 2024September 19, 2024
Context window200K33K
Input $/Mtok$15.00$0.36
Output $/Mtok$60.00$0.40
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is o1 better than Qwen2.5 72B Instruct for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. o1 has the edge where the context window is larger, 200K against 33K tokens.

Which is cheaper, o1 or Qwen2.5 72B Instruct?

o1 costs $15.00 per million input tokens and $60.00 per million output tokens; Qwen2.5 72B Instruct costs $0.36 and $0.40. Cost per solved task is known for neither of them.

Which has the bigger context window, o1 or Qwen2.5 72B Instruct?

o1, with 200K tokens against 33K.

How many benchmarks is the o1 vs Qwen2.5 72B Instruct comparison based on?

43 benchmarks have a result for both models as of October 21, 2026. o1's coding index rests on 0 independent benchmarks and Qwen2.5 72B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.

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