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Command A vs GLM 5.3

GLM 5.3 leads Command A on the Artificial Analysis Intelligence Index, 44.8 to 13.1, and GLM 5.3 costs 2.3x less per output token ($4.40 vs $10.00 per million). Updated October 22, 2026; 47 benchmarks have a result for both.

Replace Command A Replace GLM 5.3
Command ACohere
Coding
No data
Overall
No data
Output $/M
$10.00
0
GLM 5.3Z.ai
Coding
70.6
Overall
67.9
Output $/M
$4.40
47
vs boards won of 47 shared

Command A or GLM 5.3: which to pick

Only GLM 5.3 has a coding score, 70.6 on the Vibe Coding Index; Command A has none, so the two cannot be ranked on coding.

Compare them on price, context and the boards below.

Model ACommand ACohereNot benchmarked1 independent benchmark$2.69 per solved taskModel BGLM 5.3Z.ai70.6/1006 independent benchmarks$0.36 per solved task
Shared results
47
Command A only
15
GLM 5.3 only
53
Like-for-like categories
7 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
Command A not benchmarked, GLM 5.3 70.6 from 6 benchmarks.
Widest category
Agentic work, 49.5 points apart on average over 2 shared benchmarks.
Widest single gap
SWE-bench Verified (Vals AI): Command A 7.8% against GLM 5.3 95.4%, 87.6 points apart on a 0 to 100 scale.
Output price
Command A $10.00 against GLM 5.3 $4.40 per million output tokens, 2.3x apart.
Context window
Command A takes 256K tokens, GLM 5.3 1.05M.

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
CategoryCommand ABarsGLM 5.3Ties
Coding07
Intelligence01
Agentic work02
Reasoning06
Math02
Knowledge08
General021
All shared boards047
CodingLike-for-likeSix of these boards feed the Vibe Coding Index07
Coding benchmarks for Command A and GLM 5.3
BenchmarkCommand AGLM 5.3Leader
LiveBench codingNot measured82.8%Only GLM 5.3 measured
LMArena WebDevNot measured1621Only GLM 5.3 measured
AA Coding Index27.874.8GLM 5.3
LiveCodeBench (Vals AI)35.1%80.5%GLM 5.3
SWE-bench Verified (Vals AI)7.8%95.4%GLM 5.3
Terminal-Bench 4 (Vals AI)Not measured38.9%Only GLM 5.3 measured
Vibe Code Bench (Vals AI)Not measured78.1%Only GLM 5.3 measured
Code migration (Vals AI)Not measured44.2%Only GLM 5.3 measured
ProgramBench (Vals AI)Not measured1.5%Only GLM 5.3 measured
IOI (Vals AI)Not measured68.4%Only GLM 5.3 measured
Terminal-Bench 2.1 (Vals AI)17.6%71.5%GLM 5.3
Vibe Code Bench 1-100 (Vals AI)Not measured20.0%Only GLM 5.3 measured
SkillsBench (Vals AI)Not measured47.5%Only GLM 5.3 measured
CyberBench (Vals AI)Not measured72.1%Only GLM 5.3 measured
LiveCodeBench (Artificial Analysis run)28.7%Not measuredOnly Command A measured
SciCode38.5%59.0%GLM 5.3
Terminal-Bench Hard (Artificial Analysis run)25.0%Not measuredOnly Command A measured
FrontierCodeNot measured40.1%Only GLM 5.3 measured
FrontierSWENot measured30.2%Only GLM 5.3 measured
WeirdMLNot measured75.4%Only GLM 5.3 measured
ALE-BenchNot measured1317.4Only GLM 5.3 measured
LMArena Text: coding13301497GLM 5.3
LMArena Text: software and IT13361493GLM 5.3
LMArena WebDev: ReactNot measured1641Only GLM 5.3 measured
LMArena WebDev: HTMLNot measured1595Only GLM 5.3 measured
Neither model has a result on the other 14 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for Command A and GLM 5.3
BenchmarkCommand AGLM 5.3Leader
AA Intelligence Index13.144.8GLM 5.3
Epoch Capabilities IndexNot measured156.0Only GLM 5.3 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
Agentic work benchmarks for Command A and GLM 5.3
BenchmarkCommand AGLM 5.3Leader
AA Agentic Index1.053.1GLM 5.3
Design Arena: agents building mobile appsNot measured1194Only GLM 5.3 measured
Design Arena: HTML slidesNot measured1189Only GLM 5.3 measured
Design Arena: PowerPoint slidesNot measured1230Only GLM 5.3 measured
LiveBench researchNot measured73.2%Only GLM 5.3 measured
LiveBench browserNot measured0.0%Only GLM 5.3 measured
LiveBench long agenticNot measured35.9%Only GLM 5.3 measured
SREBench (Vals AI)Not measured9.2%Only GLM 5.3 measured
Finance Agent (Vals AI)9.0%55.8%GLM 5.3
Tax Agent Bench (Vals AI)Not measured39.7%Only GLM 5.3 measured
Legal Research Bench (Vals AI)Not measured49.0%Only GLM 5.3 measured
Harvey's Legal Agent Benchmark (Vals AI)Not measured14.6%Only GLM 5.3 measured
Excel Modeling Benchmark (Vals AI)Not measured56.3%Only GLM 5.3 measured
Terminal-Bench-Science (Vals AI)Not measured5.7%Only GLM 5.3 measured
τ²-Bench Telecom80.7%Not measuredOnly Command A measured
APEX-AgentsNot measured56.6%Only GLM 5.3 measured
Vending-Bench 2Not measured$8,164Only GLM 5.3 measured
Berkeley Function Calling Leaderboard (BFCL)46.5%Not measuredOnly Command A measured
Neither model has a result on the other 23 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
Reasoning benchmarks for Command A and GLM 5.3
BenchmarkCommand AGLM 5.3Leader
GPQA Diamond48.5%88.1%GLM 5.3
LiveBench reasoningNot measured85.8%Only GLM 5.3 measured
MysteryMechanism (Vals AI)Not measured23.0%Only GLM 5.3 measured
GPQA Diamond (Artificial Analysis run)76.1%91.7%GLM 5.3
Humanity's Last Exam (Artificial Analysis run)12.0%42.3%GLM 5.3
GPQA Diamond (Epoch AI run)Not measured90.9%Only GLM 5.3 measured
Chess puzzlesNot measured37.0%Only GLM 5.3 measured
Mystery game puzzlesNot measured33.0%Only GLM 5.3 measured
LMArena Text: hard prompts13261490GLM 5.3
LMArena Text: expert12961514GLM 5.3
LMArena Text: hard prompts in English13351496GLM 5.3
Kagi LLM Benchmark28.8%Not measuredOnly Command A measured
Neither model has a result on the other 10 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for Command A and GLM 5.3
BenchmarkCommand AGLM 5.3Leader
AIME13.3%Not measuredOnly Command A measured
MATH 500 (Vals AI)76.2%Not measuredOnly Command A measured
MGSM (Vals AI)85.7%Not measuredOnly Command A measured
AA Math Index13.0Not measuredOnly Command A measured
AIME 2025 (Artificial Analysis run)13.0%Not measuredOnly Command A measured
OTIS Mock AIMENot measured91.1%Only GLM 5.3 measured
FrontierMath, tiers 1 to 3Not measured68.8%Only GLM 5.3 measured
FrontierMath, tier 4Not measured29.3%Only GLM 5.3 measured
ProofBenchNot measured49.0%Only GLM 5.3 measured
LMArena Text: math13001495GLM 5.3
LMArena Text: mathematical work13121497GLM 5.3
Neither model has a result on the other 2 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index08
Knowledge benchmarks for Command A and GLM 5.3
BenchmarkCommand AGLM 5.3Leader
MMLU Pro69.2%86.8%GLM 5.3
LegalBench (Vals AI)79.7%84.8%GLM 5.3
CaseLaw (Vals AI)64.5%Not measuredOnly Command A measured
CorpFin (Vals AI)46.0%Not measuredOnly Command A measured
TaxEval (Vals AI)61.4%72.4%GLM 5.3
MedQA (Vals AI)80.5%Not measuredOnly Command A measured
MedCode (Vals AI)19.7%42.9%GLM 5.3
MMLU Pro (Artificial Analysis run)71.2%Not measuredOnly Command A measured
SimpleQA VerifiedNot measured41.0%Only GLM 5.3 measured
LMArena Text: science13391498GLM 5.3
LMArena Text: medicine and healthcare13341481GLM 5.3
LMArena Text: legal and government13481473GLM 5.3
LMArena Text: business and finance13151459GLM 5.3
Neither model has a result on the other 5 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
Web design benchmarks for Command A and GLM 5.3
BenchmarkCommand AGLM 5.3Leader
Design Arena: websitesNot measured1295Only GLM 5.3 measured
Design Arena: UI componentsNot measured1314Only GLM 5.3 measured
Design Arena: game devNot measured1311Only GLM 5.3 measured
Design Arena: data vizNot measured1260Only GLM 5.3 measured
Design Arena: 3DNot measured1332Only GLM 5.3 measured
Design Arena: all code categoriesNot measured1304Only GLM 5.3 measured
Design Arena: SVGNot measured1294Only GLM 5.3 measured
Design Arena: ASCII artNot measured1208Only GLM 5.3 measured
LiveBench designNot measured77.3%Only GLM 5.3 measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index021
General benchmarks for Command A and GLM 5.3
BenchmarkCommand AGLM 5.3Leader
Vals IndexNot measured59.7%Only GLM 5.3 measured
LiveBench data analysisNot measured70.2%Only GLM 5.3 measured
Public Benefits Bench (Vals AI)Not measured68.5%Only GLM 5.3 measured
MedScribe (Vals AI)55.7%88.8%GLM 5.3
IFBench73.9%Not measuredOnly Command A measured
Long context reasoning (AA-LCR)52.7%79.7%GLM 5.3
DTBench61.3%87.7%GLM 5.3
LMCA10.3%55.5%GLM 5.3
LMArena Text13311474GLM 5.3
LMArena Text: instruction following13091479GLM 5.3
LMArena Text: creative writing13191458GLM 5.3
LMArena Text: English13441482GLM 5.3
LMArena Text: non-English13131459GLM 5.3
LMArena Text: Chinese13271532GLM 5.3
LMArena Text: German13421488GLM 5.3
LMArena Text: Japanese12851460GLM 5.3
LMArena Text: Korean12851460GLM 5.3
LMArena Text: French13511503GLM 5.3
LMArena Text: Spanish13471462GLM 5.3
LMArena Text: Russian13141469GLM 5.3
LMArena Text: Polish13241466GLM 5.3
LMArena Text: multi-turn13381472GLM 5.3
LMArena Text: longer queries13341483GLM 5.3
LMArena Text: writing and language13281469GLM 5.3
LMArena Text: entertainment and media13061447GLM 5.3
Neither model has a result on the other 21 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Command A and GLM 5.3
BenchmarkCommand AGLM 5.3Leader
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

Command A$0.0075Fits in one request
GLM 5.3$0.0036Fits in one request

GLM 5.3 costs less

Repository review

50K input + 3K output tokens

Command A$0.155Fits in one request
GLM 5.3$0.083Fits in one request

GLM 5.3 costs less

Agent loop

200K input + 10K output tokens

Command A$0.600Fits in one request
GLM 5.3$0.324Fits in one request

GLM 5.3 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 Command A and GLM 5.3
SpecificationCohere Command AZ.ai GLM 5.3
MakerCohereZ.ai
WeightsOpen weightsOpen weights
ReleasedMarch 13, 2025August 18, 2026
Context window256K1.05M
Input $/Mtok$2.50$1.40
Output $/Mtok$10.00$4.40
Coding indexNot benchmarked70.6
Independent measurements behind it16
Cost per solved task$2.69$0.36
SourceOpenRouter listingOpenRouter listing

Questions

Is Command A better than GLM 5.3 for coding?

Only GLM 5.3 has a coding score, 70.6 on the Vibe Coding Index; Command A has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.

Which is cheaper, Command A or GLM 5.3?

Command A costs $2.50 per million input tokens and $10.00 per million output tokens; GLM 5.3 costs $1.40 and $4.40. Per solved SWE-bench Verified task, Command A costs $2.69 and GLM 5.3 $0.36.

Which has the bigger context window, Command A or GLM 5.3?

GLM 5.3, with 1.05M tokens against 256K.

How many benchmarks is the Command A vs GLM 5.3 comparison based on?

47 benchmarks have a result for both models as of October 22, 2026. Command A's coding index rests on 1 independent benchmark and GLM 5.3's on 6. Where only one model has a result, the page says so instead of naming a winner.

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