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Command R (08-2024) vs GLM 5

Command R (08-2024) costs 5.3x less per output token than GLM 5 ($0.60 vs $3.20 per million). Updated October 22, 2026; 25 benchmarks have a result for both.

Replace Command R (08-2024) Replace GLM 5
Command R (08-2024)Cohere
Coding
No data
Overall
No data
Output $/M
$0.60
0
GLM 5Z.ai
Coding
41.7
Overall
54.3
Output $/M
$3.20
25
vs boards won of 25 shared

Command R (08-2024) or GLM 5: which to pick

Only GLM 5 has a coding score, 41.7 on the Vibe Coding Index; Command R (08-2024) has none, so the two cannot be ranked on coding.

Pick Command R (08-2024) for output tokens 5.3x cheaper ($0.60 vs $3.20 per million).

Model ACommand R (08-2024)CohereNot benchmarkedNo coding benchmarkNo cost per solved taskModel BGLM 5Z.ai41.7/1003 independent benchmarks$0.63 per solved task
Shared results
25
Command R (08-2024) only
0
GLM 5 only
51
Like-for-like categories
5 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 R (08-2024) not benchmarked, GLM 5 41.7 from 3 benchmarks.
Widest category
Reasoning, 45.1 points apart on average over 3 shared benchmarks.
Widest single gap
LMArena Text: Chinese: Command R (08-2024) 1227 against GLM 5 1511, 47.4 points apart on a 0 to 100 scale.
Output price
Command R (08-2024) $0.60 against GLM 5 $3.20 per million output tokens, 5.3x apart.
Context window
Command R (08-2024) takes 128K tokens, GLM 5 205K.

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 R (08-2024)BarsGLM 5Ties
Coding02
Reasoning03
Math02
Knowledge04
General014
All shared boards025
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
Coding benchmarks for Command R (08-2024) and GLM 5
BenchmarkCommand R (08-2024)GLM 5Leader
SWE-bench VerifiedNot measured72.8%Only GLM 5 measured
SWE-bench MultilingualNot measured69.7%Only GLM 5 measured
LMArena WebDevNot measured1435Only GLM 5 measured
LiveCodeBench (Vals AI)Not measured81.9%Only GLM 5 measured
SWE-bench Verified (Vals AI)Not measured71.4%Only GLM 5 measured
Vibe Code Bench (Vals AI)Not measured23.4%Only GLM 5 measured
Terminal-Bench Hard (Artificial Analysis run)Not measured43.2%Only GLM 5 measured
Terminal-BenchNot measured52.4%Only GLM 5 measured
WeirdMLNot measured48.2%Only GLM 5 measured
ALE-BenchNot measured765.6Only GLM 5 measured
LMArena Text: coding11871461GLM 5
LMArena Text: software and IT11971460GLM 5
LMArena WebDev: ReactNot measured1424Only GLM 5 measured
LMArena WebDev: HTMLNot measured1450Only GLM 5 measured
Neither model has a result on the other 25 boards here
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Intelligence benchmarks for Command R (08-2024) and GLM 5
BenchmarkCommand R (08-2024)GLM 5Leader
AA Intelligence IndexNot measured27.9Only GLM 5 measured
Epoch Capabilities IndexNot measured145.8Only GLM 5 measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Agentic work benchmarks for Command R (08-2024) and GLM 5
BenchmarkCommand R (08-2024)GLM 5Leader
Design Arena: agents building full-stack appsNot measured1123Only GLM 5 measured
Design Arena: agents building mobile appsNot measured1136Only GLM 5 measured
Design Arena: agents building Android appsNot measured1147Only GLM 5 measured
Design Arena: agents building Godot gamesNot measured1144Only GLM 5 measured
Design Arena: HTML slidesNot measured1098Only GLM 5 measured
τ²-Bench TelecomNot measured98.2%Only GLM 5 measured
Vending-Bench 2Not measured$4,432Only GLM 5 measured
Neither model has a result on the other 34 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
Reasoning benchmarks for Command R (08-2024) and GLM 5
BenchmarkCommand R (08-2024)GLM 5Leader
GPQA DiamondNot measured83.3%Only GLM 5 measured
GPQA Diamond (Artificial Analysis run)Not measured82.0%Only GLM 5 measured
Humanity's Last Exam (Artificial Analysis run)Not measured29.3%Only GLM 5 measured
GPQA Diamond (Epoch AI run)Not measured87.8%Only GLM 5 measured
ARC-AGINot measured44.7%Only GLM 5 measured
ARC-AGI-2Not measured4.9%Only GLM 5 measured
SimpleBenchNot measured53.2%Only GLM 5 measured
LMArena Text: hard prompts11861452GLM 5
LMArena Text: expert11731454GLM 5
LMArena Text: hard prompts in English11941460GLM 5
Kagi LLM BenchmarkNot measured75.0%Only GLM 5 measured
Neither model has a result on the other 11 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for Command R (08-2024) and GLM 5
BenchmarkCommand R (08-2024)GLM 5Leader
AIMENot measured91.7%Only GLM 5 measured
OTIS Mock AIMENot measured80.0%Only GLM 5 measured
LMArena Text: math11881440GLM 5
LMArena Text: mathematical work11981451GLM 5
Neither model has a result on the other 9 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for Command R (08-2024) and GLM 5
BenchmarkCommand R (08-2024)GLM 5Leader
MMLU ProNot measured86.0%Only GLM 5 measured
LegalBench (Vals AI)Not measured84.1%Only GLM 5 measured
CaseLaw (Vals AI)Not measured52.5%Only GLM 5 measured
CorpFin (Vals AI)Not measured62.9%Only GLM 5 measured
TaxEval (Vals AI)Not measured70.0%Only GLM 5 measured
MedQA (Vals AI)Not measured94.3%Only GLM 5 measured
LMArena Text: science12521460GLM 5
LMArena Text: medicine and healthcare12381455GLM 5
LMArena Text: legal and government12681453GLM 5
LMArena Text: business and finance12151430GLM 5
Neither model has a result on the other 8 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for Command R (08-2024) and GLM 5
BenchmarkCommand R (08-2024)GLM 5Leader
Design Arena: websitesNot measured1247Only GLM 5 measured
Design Arena: UI componentsNot measured1227Only GLM 5 measured
Design Arena: game devNot measured1234Only GLM 5 measured
Design Arena: data vizNot measured1228Only GLM 5 measured
Design Arena: 3DNot measured1228Only GLM 5 measured
Design Arena: all code categoriesNot measured1246Only GLM 5 measured
Design Arena: SVGNot measured1168Only GLM 5 measured
Design Arena: ASCII artNot measured1147Only GLM 5 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index014
General benchmarks for Command R (08-2024) and GLM 5
BenchmarkCommand R (08-2024)GLM 5Leader
IFBenchNot measured72.3%Only GLM 5 measured
Long context reasoning (AA-LCR)Not measured75.7%Only GLM 5 measured
CL-benchNot measured18.7%Only GLM 5 measured
LMArena Text12291446GLM 5
LMArena Text: instruction following11971428GLM 5
LMArena Text: creative writing12341438GLM 5
LMArena Text: English12381458GLM 5
LMArena Text: non-English12161430GLM 5
LMArena Text: Chinese12271511GLM 5
LMArena Text: German12161447GLM 5
LMArena Text: Japanese11661416GLM 5
LMArena Text: Korean11621423GLM 5
LMArena Text: FrenchNot measured1456Only GLM 5 measured
LMArena Text: SpanishNot measured1454Only GLM 5 measured
LMArena Text: Russian12271436GLM 5
LMArena Text: PolishNot measured1434Only GLM 5 measured
LMArena Text: multi-turn12131456GLM 5
LMArena Text: longer queries12301446GLM 5
LMArena Text: writing and language12411437GLM 5
LMArena Text: entertainment and media12231426GLM 5
Neither model has a result on the other 26 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Command R (08-2024) and GLM 5
BenchmarkCommand R (08-2024)GLM 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 R (08-2024)$0.0004Fits in one request
GLM 5$0.0026Fits in one request

Command R (08-2024) costs less

Repository review

50K input + 3K output tokens

Command R (08-2024)$0.0093Fits in one request
GLM 5$0.060Fits in one request

Command R (08-2024) costs less

Agent loop

200K input + 10K output tokens

Command R (08-2024)$0.036Exceeds the context window
GLM 5$0.232Exceeds the context window

Command R (08-2024) 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 R (08-2024) and GLM 5
SpecificationCohere Command R (08-2024)Z.ai GLM 5
MakerCohereZ.ai
WeightsNo public weights listedOpen weights
ReleasedAugust 30, 2024February 11, 2026
Context window128K205K
Input $/Mtok$0.15$1.00
Output $/Mtok$0.60$3.20
Coding indexNot benchmarked41.7
Independent measurements behind it03
Cost per solved taskNot computable$0.63
SourceOpenRouter listingOpenRouter listing

Questions

Is Command R (08-2024) better than GLM 5 for coding?

Only GLM 5 has a coding score, 41.7 on the Vibe Coding Index; Command R (08-2024) has none, so the two cannot be ranked on coding. Pick Command R (08-2024) for output tokens 5.3x cheaper ($0.60 vs $3.20 per million).

Which is cheaper, Command R (08-2024) or GLM 5?

Command R (08-2024) costs $0.15 per million input tokens and $0.60 per million output tokens; GLM 5 costs $1.00 and $3.20. Cost per solved task is known for GLM 5 only.

Which has the bigger context window, Command R (08-2024) or GLM 5?

GLM 5, with 205K tokens against 128K.

How many benchmarks is the Command R (08-2024) vs GLM 5 comparison based on?

25 benchmarks have a result for both models as of October 22, 2026. Command R (08-2024)'s coding index rests on 0 independent benchmarks and GLM 5's on 3. Where only one model has a result, the page says so instead of naming a winner.

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