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GLM 5.1 vs o3

GLM 5.1 leads o3 on the Artificial Analysis Intelligence Index, 26.1 to 20.2, and GLM 5.1 costs 1.8x less per output token ($4.40 vs $8.00 per million). Updated October 22, 2026; 58 benchmarks have a result for both.

Replace GLM 5.1 Replace o3
GLM 5.1Z.ai
Coding
49.2
Overall
57.5
Output $/M
$4.40
50
o3OpenAI
Coding
No data
Overall
51.1
Output $/M
$8.00
8
vs boards won of 58 shared

GLM 5.1 or o3: which to pick

Only GLM 5.1 has a coding score, 49.2 on the Vibe Coding Index; o3 has none, so the two cannot be ranked on coding.

Pick o3 for a lower cost per solved task ($0.571 against $0.60) and work measured by LiveCodeBench (Vals AI) or ALE-Bench, where it leads.

Model AGLM 5.1Z.ai49.2/1003 independent benchmarks$0.60 per solved taskModel Bo3OpenAINot benchmarkedNo coding benchmark$0.571 per solved task
Shared results
58
GLM 5.1 only
31
o3 only
44
Like-for-like categories
8 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
GLM 5.1 49.2 from 3 benchmarks, o3 not benchmarked.
Widest category
Web design, 29.4 points apart on average over 5 shared benchmarks.
Widest single gap
Design Arena: UI components: GLM 5.1 1292 against o3 1017, 34.4 points apart on a 0 to 100 scale.
Output price
GLM 5.1 $4.40 against o3 $8.00 per million output tokens, 1.8x apart.
Context window
GLM 5.1 takes 205K tokens, o3 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 5.1Barso3Ties
Coding42
Intelligence20
Agentic work10
Reasoning81
Math50
Knowledge73
Web design50
General182
All shared boards508
CodingLike-for-likeSix of these boards feed the Vibe Coding Index42
Coding benchmarks for GLM 5.1 and o3
BenchmarkGLM 5.1o3Leader
Aider polyglotNot measured81.3%Only o3 measured
SWE-bench VerifiedNot measured58.4%Only o3 measured
SWE-bench MultimodalNot measured36.0%Only o3 measured
LMArena WebDev1508Not measuredOnly GLM 5.1 measured
AA Coding Index55.8Not measuredOnly GLM 5.1 measured
LiveCodeBench (Vals AI)81.4%83.9%o3
SWE-bench Verified (Vals AI)76.4%Not measuredOnly GLM 5.1 measured
Vibe Code Bench (Vals AI)31.5%Not measuredOnly GLM 5.1 measured
Code migration (Vals AI)25.8%Not measuredOnly GLM 5.1 measured
ProgramBench (Vals AI)0.0%Not measuredOnly GLM 5.1 measured
Terminal-Bench 2.1 (Vals AI)56.9%Not measuredOnly GLM 5.1 measured
LiveCodeBench (Artificial Analysis run)Not measured80.8%Only o3 measured
SciCode44.8%Not measuredOnly GLM 5.1 measured
Terminal-Bench Hard (Artificial Analysis run)43.2%37.1%GLM 5.1
GSO-BenchNot measured8.8%Only o3 measured
WeirdML57.1%52.4%GLM 5.1
ALE-Bench887.1933.5o3
LMArena Text: coding14851407GLM 5.1
LMArena Text: software and IT14841415GLM 5.1
LMArena WebDev: React1505Not measuredOnly GLM 5.1 measured
LMArena WebDev: HTML1508Not measuredOnly GLM 5.1 measured
SciCodeNot measured9.2%Only o3 measured
Neither model has a result on the other 17 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for GLM 5.1 and o3
BenchmarkGLM 5.1o3Leader
AA Intelligence Index26.120.2GLM 5.1
Epoch Capabilities Index149.8146.7GLM 5.1
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GLM 5.1 and o3
BenchmarkGLM 5.1o3Leader
AA Agentic Index23.9Not measuredOnly GLM 5.1 measured
Design Arena: agents building web apps1175Not measuredOnly GLM 5.1 measured
Design Arena: agents building full-stack apps1167Not measuredOnly GLM 5.1 measured
Design Arena: agents building mobile apps1162Not measuredOnly GLM 5.1 measured
Design Arena: agents building Android apps1182Not measuredOnly GLM 5.1 measured
Design Arena: agents building Godot games1053Not measuredOnly GLM 5.1 measured
Design Arena: agents building games1164Not measuredOnly GLM 5.1 measured
Design Arena: HTML slides1120Not measuredOnly GLM 5.1 measured
Design Arena: PowerPoint slides1163Not measuredOnly GLM 5.1 measured
Design Arena: spreadsheets1315Not measuredOnly GLM 5.1 measured
Finance Agent (Vals AI)44.8%Not measuredOnly GLM 5.1 measured
Legal Research Bench (Vals AI)27.9%Not measuredOnly GLM 5.1 measured
τ²-Bench Telecom97.7%80.7%GLM 5.1
APEX-Agents40.9%Not measuredOnly GLM 5.1 measured
METR time horizonsNot measured65.4%Only o3 measured
GDPvalNot measured30.8%Only o3 measured
DeepResearch BenchNot measured45.2%Only o3 measured
OSWorldNot measured23.0%Only o3 measured
Vending-Bench 2$5,634Not measuredOnly GLM 5.1 measured
Berkeley Function Calling Leaderboard (BFCL)Not measured63.0%Only o3 measured
BrowseComp-PlusNot measured65.9%Only o3 measured
Neither model has a result on the other 20 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index81
Reasoning benchmarks for GLM 5.1 and o3
BenchmarkGLM 5.1o3Leader
GPQA Diamond84.5%84.1%GLM 5.1
MMMU Pro (Vals AI)Not measured80.4%Only o3 measured
GPQA Diamond (Artificial Analysis run)86.8%82.7%GLM 5.1
Humanity's Last Exam (Artificial Analysis run)30.1%20.1%GLM 5.1
GPQA Diamond (Epoch AI run)89.9%81.8%GLM 5.1
ARC-AGINot measured60.8%Only o3 measured
ARC-AGI-2Not measured6.5%Only o3 measured
SimpleBench55.1%53.1%GLM 5.1
Chess puzzles20.0%37.0%o3
Mystery game puzzlesNot measured29.0%Only o3 measured
VPCTNot measured52.0%Only o3 measured
LMArena Text: hard prompts14731402GLM 5.1
LMArena Text: expert14781402GLM 5.1
LMArena Text: hard prompts in English14821411GLM 5.1
LMArena Vision: diagramsNot measured1220Only o3 measured
LMArena Vision: homeworkNot measured1234Only o3 measured
Kagi LLM BenchmarkNot measured67.6%Only o3 measured
Neither model has a result on the other 5 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index50
Math benchmarks for GLM 5.1 and o3
BenchmarkGLM 5.1o3Leader
AIME91.9%85.3%GLM 5.1
MATH 500 (Vals AI)Not measured94.6%Only o3 measured
MGSM (Vals AI)Not measured91.8%Only o3 measured
AA Math IndexNot measured88.3Only o3 measured
AIME 2025 (Artificial Analysis run)Not measured88.3%Only o3 measured
OTIS Mock AIME93.3%84.4%GLM 5.1
FrontierMath, tiers 1 to 336.8%33.3%GLM 5.1
ProofBench22.2%Not measuredOnly GLM 5.1 measured
MATH level 5Not measured97.8%Only o3 measured
LMArena Text: math14731426GLM 5.1
LMArena Text: mathematical work14701423GLM 5.1
Neither model has a result on the other 2 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index73
Knowledge benchmarks for GLM 5.1 and o3
BenchmarkGLM 5.1o3Leader
MMLU Pro86.9%85.6%GLM 5.1
LegalBench (Vals AI)84.4%83.8%GLM 5.1
CaseLaw (Vals AI)51.5%Not measuredOnly GLM 5.1 measured
CorpFin (Vals AI)64.5%59.7%GLM 5.1
TaxEval (Vals AI)71.2%74.6%o3
MortgageTax (Vals AI)Not measured65.7%Only o3 measured
MedQA (Vals AI)Not measured96.1%Only o3 measured
MedCode (Vals AI)41.6%47.3%o3
MMLU Pro (Artificial Analysis run)Not measured85.3%Only o3 measured
SimpleQA Verified34.0%49.4%o3
GeoBenchNot measured74.0%Only o3 measured
LMArena Text: science14781429GLM 5.1
LMArena Text: medicine and healthcare14741448GLM 5.1
LMArena Text: legal and government14651428GLM 5.1
LMArena Text: business and finance14521393GLM 5.1
FACTS Benchmark SuiteNot measured44.4%Only o3 measured
Neither model has a result on the other 2 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index50
Web design benchmarks for GLM 5.1 and o3
BenchmarkGLM 5.1o3Leader
Design Arena: websites12901036GLM 5.1
Design Arena: UI components12921017GLM 5.1
Design Arena: game dev12651034GLM 5.1
Design Arena: data viz13661200GLM 5.1
Design Arena: 3D1336Not measuredOnly GLM 5.1 measured
Design Arena: all code categories12681020GLM 5.1
Design Arena: SVG1218Not measuredOnly GLM 5.1 measured
Design Arena: ASCII art1141Not measuredOnly GLM 5.1 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index182
General benchmarks for GLM 5.1 and o3
BenchmarkGLM 5.1o3Leader
Public Benefits Bench (Vals AI)61.8%Not measuredOnly GLM 5.1 measured
SAGE (Vals AI)Not measured41.8%Only o3 measured
MedScribe (Vals AI)72.3%76.7%o3
IFBench76.3%71.4%GLM 5.1
Long context reasoning (AA-LCR)73.7%74.7%o3
DTBenchNot measured84.8%Only o3 measured
LMCANot measured39.7%Only o3 measured
Fiction.LiveBenchNot measured88.9%Only o3 measured
Creative writing (Lech Mazur)Not measured83.9%Only o3 measured
CL-benchNot measured17.8%Only o3 measured
ExploitBench18.1%Not measuredOnly GLM 5.1 measured
LMArena Text14631410GLM 5.1
LMArena Text: instruction following14531368GLM 5.1
LMArena Text: creative writing14531359GLM 5.1
LMArena VisionNot measured1214Only o3 measured
LMArena Text: English14761417GLM 5.1
LMArena Text: non-English14481401GLM 5.1
LMArena Text: Chinese15151437GLM 5.1
LMArena Text: German14671420GLM 5.1
LMArena Text: Japanese14351403GLM 5.1
LMArena Text: Korean14241370GLM 5.1
LMArena Text: French14701430GLM 5.1
LMArena Text: Spanish14681395GLM 5.1
LMArena Text: Russian14541406GLM 5.1
LMArena Text: Polish14491438GLM 5.1
LMArena Text: multi-turn14731405GLM 5.1
LMArena Text: longer queries14671372GLM 5.1
LMArena Text: writing and language14541372GLM 5.1
LMArena Text: entertainment and media14421373GLM 5.1
LMArena Vision: EnglishNot measured1221Only o3 measured
LMArena Vision: ChineseNot measured1237Only o3 measured
LMArena Vision: reading textNot measured1218Only o3 measured
LMArena Vision: humorNot measured1222Only o3 measured
LMArena Vision: captioningNot measured1210Only o3 measured
LMArena Vision: recognizing thingsNot measured1234Only o3 measured
LMArena Vision: creative writingNot measured1201Only o3 measured
Neither model has a result on the other 10 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 5.1 and o3
BenchmarkGLM 5.1o3Leader
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 5.1$0.0036Fits in one request
o3$0.0060Fits in one request

GLM 5.1 costs less

Repository review

50K input + 3K output tokens

GLM 5.1$0.083Fits in one request
o3$0.124Fits in one request

GLM 5.1 costs less

Agent loop

200K input + 10K output tokens

GLM 5.1$0.324Exceeds the context window
o3$0.480Exceeds the context window

GLM 5.1 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 5.1 and o3
SpecificationZ.ai GLM 5.1OpenAI o3
MakerZ.aiOpenAI
WeightsOpen weightsNo public weights listed
ReleasedApril 7, 2026April 16, 2025
Context window205K200K
Input $/Mtok$1.40$2.00
Output $/Mtok$4.40$8.00
Coding index49.2Not benchmarked
Independent measurements behind it30
Cost per solved task$0.60$0.571
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5.1 better than o3 for coding?

Only GLM 5.1 has a coding score, 49.2 on the Vibe Coding Index; o3 has none, so the two cannot be ranked on coding. Pick o3 for a lower cost per solved task ($0.571 against $0.60) and work measured by LiveCodeBench (Vals AI) or ALE-Bench, where it leads.

Which is cheaper, GLM 5.1 or o3?

GLM 5.1 costs $1.40 per million input tokens and $4.40 per million output tokens; o3 costs $2.00 and $8.00. Per solved SWE-bench Verified task, GLM 5.1 costs $0.60 and o3 $0.571.

Which has the bigger context window, GLM 5.1 or o3?

GLM 5.1, with 205K tokens against 200K.

How many benchmarks is the GLM 5.1 vs o3 comparison based on?

58 benchmarks have a result for both models as of October 22, 2026. GLM 5.1's coding index rests on 3 independent benchmarks and o3's on 0. Where only one model has a result, the page says so instead of naming a winner.

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