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Command A vs GLM 4.5

Command A leads GLM 4.5 on the Artificial Analysis Intelligence Index, 13.1 to 12.8, and GLM 4.5 costs 4.5x less per output token ($2.20 vs $10.00 per million). Updated October 22, 2026; 50 benchmarks have a result for both.

Replace Command A Replace GLM 4.5
Command ACohere
Coding
No data
Overall
No data
Output $/M
$10.00
5
GLM 4.5Z.ai
Coding
No data
Overall
No data
Output $/M
$2.20
44
vs boards won of 50 shared, 1 tied

Command A or GLM 4.5: which to pick

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

Pick Command A for a larger context window (256K against 131K tokens) and work measured by LiveCodeBench (Vals AI) or LiveCodeBench (Artificial Analysis run), where it leads.

Model ACommand ACohereNot benchmarked1 independent benchmark$2.69 per solved taskModel BGLM 4.5Z.aiNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
50
Command A only
12
GLM 4.5 only
11
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 4.5 not benchmarked.
Widest category
Agentic work, 37.7 points apart on average over 1 shared benchmark.
Widest single gap
AIME: Command A 13.3% against GLM 4.5 86.7%, 73.3 points apart on a 0 to 100 scale.
Output price
Command A $10.00 against GLM 4.5 $2.20 per million output tokens, 4.5x apart.
Context window
Command A takes 256K tokens, GLM 4.5 131K.

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 4.5Ties
Coding14
Intelligence10
Agentic work10
Reasoning07
Math07
Knowledge19
General1171 tie
All shared boards5441 tie
CodingLike-for-likeSix of these boards feed the Vibe Coding Index14
Coding benchmarks for Command A and GLM 4.5
BenchmarkCommand AGLM 4.5Leader
SWE-bench VerifiedNot measured64.2%Only GLM 4.5 measured
AA Coding Index27.8Not measuredOnly Command A measured
LiveCodeBench (Vals AI)35.1%67.5%GLM 4.5
SWE-bench Verified (Vals AI)7.8%Not measuredOnly Command A measured
Terminal-Bench 2.1 (Vals AI)17.6%Not measuredOnly Command A measured
LiveCodeBench (Artificial Analysis run)28.7%73.8%GLM 4.5
SciCode38.5%Not measuredOnly Command A measured
Terminal-Bench Hard (Artificial Analysis run)25.0%22.0%Command A
WeirdMLNot measured40.6%Only GLM 4.5 measured
ALE-BenchNot measured344.8Only GLM 4.5 measured
LMArena Text: coding13301434GLM 4.5
LMArena Text: software and IT13361440GLM 4.5
Neither model has a result on the other 27 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for Command A and GLM 4.5
BenchmarkCommand AGLM 4.5Leader
AA Intelligence Index13.112.8Command A
Neither model has a result on the other 1 board here
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for Command A and GLM 4.5
BenchmarkCommand AGLM 4.5Leader
AA Agentic Index1.0Not measuredOnly Command A measured
Finance Agent (Vals AI)9.0%Not measuredOnly Command A measured
τ²-Bench Telecom80.7%43.0%Command A
Berkeley Function Calling Leaderboard (BFCL)46.5%Not measuredOnly Command A measured
Neither model has a result on the other 37 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index07
Reasoning benchmarks for Command A and GLM 4.5
BenchmarkCommand AGLM 4.5Leader
GPQA Diamond48.5%72.2%GLM 4.5
GPQA Diamond (Artificial Analysis run)76.1%78.2%GLM 4.5
Humanity's Last Exam (Artificial Analysis run)12.0%13.0%GLM 4.5
LMArena Text: hard prompts13261429GLM 4.5
LMArena Text: expert12961433GLM 4.5
LMArena Text: hard prompts in English13351429GLM 4.5
Kagi LLM Benchmark28.8%57.9%GLM 4.5
Neither model has a result on the other 15 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index07
Math benchmarks for Command A and GLM 4.5
BenchmarkCommand AGLM 4.5Leader
AIME13.3%86.7%GLM 4.5
MATH 500 (Vals AI)76.2%94.0%GLM 4.5
MGSM (Vals AI)85.7%90.8%GLM 4.5
AA Math Index13.073.7GLM 4.5
AIME 2025 (Artificial Analysis run)13.0%73.7%GLM 4.5
LMArena Text: math13001427GLM 4.5
LMArena Text: mathematical work13121430GLM 4.5
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index19
Knowledge benchmarks for Command A and GLM 4.5
BenchmarkCommand AGLM 4.5Leader
MMLU Pro69.2%81.2%GLM 4.5
LegalBench (Vals AI)79.7%75.6%Command A
CaseLaw (Vals AI)64.5%Not measuredOnly Command A measured
CorpFin (Vals AI)46.0%61.0%GLM 4.5
TaxEval (Vals AI)61.4%72.4%GLM 4.5
MedQA (Vals AI)80.5%90.0%GLM 4.5
MedCode (Vals AI)19.7%Not measuredOnly Command A measured
MMLU Pro (Artificial Analysis run)71.2%83.5%GLM 4.5
LMArena Text: science13391444GLM 4.5
LMArena Text: medicine and healthcare13341455GLM 4.5
LMArena Text: legal and government13481438GLM 4.5
LMArena Text: business and finance13151420GLM 4.5
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 Command A and GLM 4.5
BenchmarkCommand AGLM 4.5Leader
Design Arena: websitesNot measured1169Only GLM 4.5 measured
Design Arena: UI componentsNot measured1141Only GLM 4.5 measured
Design Arena: game devNot measured1144Only GLM 4.5 measured
Design Arena: data vizNot measured1159Only GLM 4.5 measured
Design Arena: 3DNot measured1170Only GLM 4.5 measured
Design Arena: all code categoriesNot measured1165Only GLM 4.5 measured
Design Arena: SVGNot measured1096Only GLM 4.5 measured
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index117
General benchmarks for Command A and GLM 4.5
BenchmarkCommand AGLM 4.5Leader
MedScribe (Vals AI)55.7%Not measuredOnly Command A measured
IFBench73.9%44.1%Command A
Long context reasoning (AA-LCR)52.7%52.7%Tie
DTBench61.3%Not measuredOnly Command A measured
LMCA10.3%Not measuredOnly Command A measured
Creative writing (Lech Mazur)Not measured73.4%Only GLM 4.5 measured
LMArena Text13311430GLM 4.5
LMArena Text: instruction following13091404GLM 4.5
LMArena Text: creative writing13191395GLM 4.5
LMArena Text: English13441437GLM 4.5
LMArena Text: non-English13131417GLM 4.5
LMArena Text: Chinese13271465GLM 4.5
LMArena Text: German13421407GLM 4.5
LMArena Text: Japanese12851415GLM 4.5
LMArena Text: Korean12851380GLM 4.5
LMArena Text: French13511418GLM 4.5
LMArena Text: Spanish13471454GLM 4.5
LMArena Text: Russian13141413GLM 4.5
LMArena Text: Polish13241422GLM 4.5
LMArena Text: multi-turn13381415GLM 4.5
LMArena Text: longer queries13341412GLM 4.5
LMArena Text: writing and language13281403GLM 4.5
LMArena Text: entertainment and media13061404GLM 4.5
Neither model has a result on the other 23 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 4.5
BenchmarkCommand AGLM 4.5Leader
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 4.5$0.0017Fits in one request

GLM 4.5 costs less

Repository review

50K input + 3K output tokens

Command A$0.155Fits in one request
GLM 4.5$0.037Fits in one request

GLM 4.5 costs less

Agent loop

200K input + 10K output tokens

Command A$0.600Fits in one request
GLM 4.5$0.142Exceeds the context window

GLM 4.5 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 4.5
SpecificationCohere Command AZ.ai GLM 4.5
MakerCohereZ.ai
WeightsOpen weightsOpen weights
ReleasedMarch 13, 2025July 25, 2025
Context window256K131K
Input $/Mtok$2.50$0.60
Output $/Mtok$10.00$2.20
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it10
Cost per solved task$2.69Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Command A better than GLM 4.5 for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Command A for a larger context window (256K against 131K tokens) and work measured by LiveCodeBench (Vals AI) or LiveCodeBench (Artificial Analysis run), where it leads.

Which is cheaper, Command A or GLM 4.5?

Command A costs $2.50 per million input tokens and $10.00 per million output tokens; GLM 4.5 costs $0.60 and $2.20. Cost per solved task is known for Command A only.

Which has the bigger context window, Command A or GLM 4.5?

Command A, with 256K tokens against 131K.

How many benchmarks is the Command A vs GLM 4.5 comparison based on?

50 benchmarks have a result for both models as of October 22, 2026. Command A's coding index rests on 1 independent benchmark and GLM 4.5's on 0. Where only one model has a result, the page says so instead of naming a winner.

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