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GLM 4.5V vs o1

Updated October 21, 2026. 34 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 4.5V Replace o1
GLM 4.5VZ.ai
Coding
No data
Overall
7.6
Output $/M
$1.80
1
o1OpenAI
Coding
No data
Overall
15.2
Output $/M
$60.00
33
vs boards won of 34 shared

Decision reading

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

GLM 4.5V has the edge where output tokens cost 33.3x less, and where what matters is LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Model AGLM 4.5VZ.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel Bo1OpenAINot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
34
GLM 4.5V only
6
o1 only
34
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
GLM 4.5V not benchmarked, o1 not benchmarked.
Widest category
Agentic work, 40.1 points apart on average over 1 shared benchmark.
Widest single gap
Long context reasoning (AA-LCR): GLM 4.5V 0.0% against o1 65.0%, 65.0 points apart on a 0 to 100 scale.
Output price
GLM 4.5V $1.80 against o1 $60.00 per million output tokens, 33.3x apart.
Context window
GLM 4.5V takes 66K tokens, o1 200K.

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
CategoryGLM 4.5VBarso1Ties
Coding04
Intelligence01
Agentic work01
Reasoning05
Math02
Knowledge05
General115
All shared boards133
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
Coding benchmarks for GLM 4.5V and o1
BenchmarkGLM 4.5Vo1Leader
Aider polyglotNot measured61.7%Only o1 measured
AA Coding IndexNot measured39.7Only o1 measured
LiveCodeBench (Vals AI)Not measured50.3%Only o1 measured
LiveCodeBench (Artificial Analysis run)60.4%67.9%o1
Terminal-Bench Hard (Artificial Analysis run)6.8%12.9%o1
WeirdMLNot measured46.1%Only o1 measured
LMArena Text: coding13471367o1
LMArena Text: software and IT13531366o1
Neither model has a result on the other 30 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 4.5V and o1
BenchmarkGLM 4.5Vo1Leader
AA Intelligence Index7.615.2o1
Epoch Capabilities IndexNot measured141.7Only o1 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for GLM 4.5V and o1
BenchmarkGLM 4.5Vo1Leader
τ²-Bench Telecom22.5%62.6%o1
METR time horizonsNot measured51.1%Only o1 measured
Neither model has a result on the other 37 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for GLM 4.5V and o1
BenchmarkGLM 4.5Vo1Leader
GPQA DiamondNot measured73.2%Only o1 measured
MMMU Pro (Vals AI)Not measured77.4%Only o1 measured
GPQA Diamond (Artificial Analysis run)68.4%74.7%o1
Humanity's Last Exam (Artificial Analysis run)6.3%7.0%o1
GPQA Diamond (Epoch AI run)Not measured76.8%Only o1 measured
ARC-AGINot measured30.7%Only o1 measured
SimpleBenchNot measured40.1%Only o1 measured
Chess puzzlesNot measured10.0%Only o1 measured
VPCTNot measured37.0%Only o1 measured
LMArena Text: hard prompts13351371o1
LMArena Text: expert13531361o1
LMArena Text: hard prompts in English13571372o1
LMArena Vision: diagrams1151Not measuredOnly GLM 4.5V measured
LMArena Vision: homework1163Not measuredOnly GLM 4.5V measured
Neither model has a result on the other 7 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for GLM 4.5V and o1
BenchmarkGLM 4.5Vo1Leader
AIMENot measured71.5%Only o1 measured
MATH 500 (Vals AI)Not measured90.4%Only o1 measured
MGSM (Vals AI)Not measured89.3%Only o1 measured
AA Math Index73.0Not measuredOnly GLM 4.5V measured
AIME 2025 (Artificial Analysis run)73.0%Not measuredOnly GLM 4.5V measured
OTIS Mock AIMENot measured73.3%Only o1 measured
FrontierMath, tiers 1 to 3Not measured14.7%Only o1 measured
MATH level 5Not measured94.7%Only o1 measured
LMArena Text: math13541388o1
LMArena Text: mathematical work13361390o1
Neither model has a result on the other 3 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
Knowledge benchmarks for GLM 4.5V and o1
BenchmarkGLM 4.5Vo1Leader
MMLU ProNot measured83.5%Only o1 measured
LegalBench (Vals AI)Not measured80.4%Only o1 measured
TaxEval (Vals AI)Not measured74.3%Only o1 measured
MedQA (Vals AI)Not measured96.5%Only o1 measured
MMLU Pro (Artificial Analysis run)78.8%84.1%o1
SimpleQA VerifiedNot measured41.1%Only o1 measured
GeoBenchNot measured80.0%Only o1 measured
LMArena Text: science13301369o1
LMArena Text: medicine and healthcare13491355o1
LMArena Text: legal and government13381368o1
LMArena Text: business and finance13091328o1
Neither model has a result on the other 6 boards here
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Web design benchmarks for GLM 4.5V and o1
BenchmarkGLM 4.5Vo1Leader
Neither model has a result on 9 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index115
General benchmarks for GLM 4.5V and o1
BenchmarkGLM 4.5Vo1Leader
IFBench34.2%70.3%o1
Long context reasoning (AA-LCR)0.0%65.0%o1
DTBenchNot measured74.7%Only o1 measured
LMCANot measured22.3%Only o1 measured
Fiction.LiveBenchNot measured83.3%Only o1 measured
Creative writing (Lech Mazur)Not measured70.2%Only o1 measured
LMArena Text13331366o1
LMArena Text: instruction following13111367o1
LMArena Text: creative writing12941347o1
LMArena Vision11541168o1
LMArena Text: English13621372o1
LMArena Text: non-English13031358o1
LMArena Text: Chinese13371394o1
LMArena Text: GermanNot measured1337Only o1 measured
LMArena Text: JapaneseNot measured1346Only o1 measured
LMArena Text: KoreanNot measured1396Only o1 measured
LMArena Text: FrenchNot measured1344Only o1 measured
LMArena Text: Spanish13361345o1
LMArena Text: Russian12981356o1
LMArena Text: Polish1302Not measuredOnly GLM 4.5V measured
LMArena Text: multi-turn13331355o1
LMArena Text: longer queries13051378o1
LMArena Text: writing and language13021360o1
LMArena Text: entertainment and media13031358o1
LMArena Vision: English11831162GLM 4.5V
LMArena Vision: ChineseNot measured1235Only o1 measured
LMArena Vision: reading text1151Not measuredOnly GLM 4.5V measured
Neither model has a result on the other 19 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.5V and o1
BenchmarkGLM 4.5Vo1Leader
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

GLM 4.5V$0.0015Fits in one request
o1$0.045Fits in one request

GLM 4.5V costs less

Repository review

50K input + 3K output tokens

GLM 4.5V$0.035Fits in one request
o1$0.930Fits in one request

GLM 4.5V costs less

Agent loop

200K input + 10K output tokens

GLM 4.5V$0.138Exceeds the context window
o1$3.60Exceeds the context window

GLM 4.5V 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 GLM 4.5V and o1
SpecificationZ.ai GLM 4.5VOpenAI o1
MakerZ.aiOpenAI
WeightsOpen weightsNo public weights listed
ReleasedAugust 11, 2025December 17, 2024
Context window66K200K
Input $/Mtok$0.60$15.00
Output $/Mtok$1.80$60.00
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.5V better than o1 for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. GLM 4.5V has the edge where output tokens cost 33.3x less, and where what matters is LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Which is cheaper, GLM 4.5V or o1?

GLM 4.5V costs $0.60 per million input tokens and $1.80 per million output tokens; o1 costs $15.00 and $60.00. Cost per solved task is known for neither of them.

Which has the bigger context window, GLM 4.5V or o1?

o1, with 200K tokens against 66K.

How many benchmarks is the GLM 4.5V vs o1 comparison based on?

34 benchmarks have a result for both models as of October 21, 2026. GLM 4.5V's coding index rests on 0 independent benchmarks and o1's on 0. Where only one model has a result, the page says so instead of naming a winner.

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