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Command R (08-2024) vs DeepSeek V3.2

Updated October 21, 2026. 25 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace Command R (08-2024) Replace DeepSeek V3.2
Command R (08-2024)Cohere
Coding
No data
Overall
No data
Output $/M
$0.60
0
DeepSeek V3.2DeepSeek
Coding
30.0
Overall
21.5
Output $/M
$0.88
25
vs boards won of 25 shared

Decision reading

Only DeepSeek V3.2 has a coding score, 30.0 on the Vibe Coding Index; Command R (08-2024) has none, so the two cannot be ranked on coding.

Command R (08-2024) has the edge where output tokens cost 1.5x less.

Model ACommand R (08-2024)CohereNot benchmarkedNo coding benchmarkNo cost per solved taskModel BDeepSeek V3.2DeepSeek30.0/1003 independent benchmarks$0.99 per solved task
Shared results
25
Command R (08-2024) only
0
DeepSeek V3.2 only
50
Like-for-like categories
5 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
Command R (08-2024) not benchmarked, DeepSeek V3.2 30.0 from 3 benchmarks.
Widest category
Coding, 43.2 points apart on average over 2 shared benchmarks.
Widest single gap
LMArena Text: coding: Command R (08-2024) 1187 against DeepSeek V3.2 1453, 44.4 points apart on a 0 to 100 scale.
Output price
Command R (08-2024) $0.60 against DeepSeek V3.2 $0.88 per million output tokens, 1.5x apart.
Context window
Command R (08-2024) takes 128K tokens, DeepSeek V3.2 164K.

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)BarsDeepSeek V3.2Ties
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 DeepSeek V3.2
BenchmarkCommand R (08-2024)DeepSeek V3.2Leader
SWE-bench VerifiedNot measured70.0%Only DeepSeek V3.2 measured
SWE-bench MultilingualNot measured59.0%Only DeepSeek V3.2 measured
LMArena WebDevNot measured1362Only DeepSeek V3.2 measured
AA Coding IndexNot measured44.2Only DeepSeek V3.2 measured
LiveCodeBench (Vals AI)Not measured80.7%Only DeepSeek V3.2 measured
SWE-bench Verified (Vals AI)Not measured67.6%Only DeepSeek V3.2 measured
Vibe Code Bench (Vals AI)Not measured5.1%Only DeepSeek V3.2 measured
LiveCodeBench (Artificial Analysis run)Not measured86.2%Only DeepSeek V3.2 measured
Terminal-Bench Hard (Artificial Analysis run)Not measured35.6%Only DeepSeek V3.2 measured
Terminal-BenchNot measured39.6%Only DeepSeek V3.2 measured
LMArena Text: coding11871453DeepSeek V3.2
LMArena Text: software and IT11971449DeepSeek V3.2
LMArena WebDev: ReactNot measured1350Only DeepSeek V3.2 measured
LMArena WebDev: HTMLNot measured1371Only DeepSeek V3.2 measured
Neither model has a result on the other 24 boards here
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Intelligence benchmarks for Command R (08-2024) and DeepSeek V3.2
BenchmarkCommand R (08-2024)DeepSeek V3.2Leader
AA Intelligence IndexNot measured21.5Only DeepSeek V3.2 measured
Epoch Capabilities IndexNot measured146.1Only DeepSeek V3.2 measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
Agentic work benchmarks for Command R (08-2024) and DeepSeek V3.2
BenchmarkCommand R (08-2024)DeepSeek V3.2Leader
τ²-Bench TelecomNot measured90.6%Only DeepSeek V3.2 measured
APEX-AgentsNot measured21.3%Only DeepSeek V3.2 measured
Vending-Bench 2Not measured$1,034Only DeepSeek V3.2 measured
Neither model has a result on the other 36 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
Reasoning benchmarks for Command R (08-2024) and DeepSeek V3.2
BenchmarkCommand R (08-2024)DeepSeek V3.2Leader
GPQA DiamondNot measured80.3%Only DeepSeek V3.2 measured
GPQA Diamond (Artificial Analysis run)Not measured84.0%Only DeepSeek V3.2 measured
Humanity's Last Exam (Artificial Analysis run)Not measured24.6%Only DeepSeek V3.2 measured
ARC-AGINot measured57.0%Only DeepSeek V3.2 measured
ARC-AGI-2Not measured4.0%Only DeepSeek V3.2 measured
LMArena Text: hard prompts11861434DeepSeek V3.2
LMArena Text: expert11731436DeepSeek V3.2
LMArena Text: hard prompts in English11941446DeepSeek V3.2
Neither model has a result on the other 13 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for Command R (08-2024) and DeepSeek V3.2
BenchmarkCommand R (08-2024)DeepSeek V3.2Leader
AIMENot measured84.6%Only DeepSeek V3.2 measured
MGSM (Vals AI)Not measured86.0%Only DeepSeek V3.2 measured
AA Math IndexNot measured92.0Only DeepSeek V3.2 measured
AIME 2025 (Artificial Analysis run)Not measured92.0%Only DeepSeek V3.2 measured
LMArena Text: math11881435DeepSeek V3.2
LMArena Text: mathematical work11981440DeepSeek V3.2
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for Command R (08-2024) and DeepSeek V3.2
BenchmarkCommand R (08-2024)DeepSeek V3.2Leader
MMLU ProNot measured84.9%Only DeepSeek V3.2 measured
LegalBench (Vals AI)Not measured76.1%Only DeepSeek V3.2 measured
CaseLaw (Vals AI)Not measured55.4%Only DeepSeek V3.2 measured
CorpFin (Vals AI)Not measured51.0%Only DeepSeek V3.2 measured
TaxEval (Vals AI)Not measured68.2%Only DeepSeek V3.2 measured
MedQA (Vals AI)Not measured93.9%Only DeepSeek V3.2 measured
MMLU Pro (Artificial Analysis run)Not measured86.2%Only DeepSeek V3.2 measured
LMArena Text: science12521446DeepSeek V3.2
LMArena Text: medicine and healthcare12381440DeepSeek V3.2
LMArena Text: legal and government12681425DeepSeek V3.2
LMArena Text: business and finance12151410DeepSeek V3.2
Neither model has a result on the other 6 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 DeepSeek V3.2
BenchmarkCommand R (08-2024)DeepSeek V3.2Leader
Design Arena: websitesNot measured1175Only DeepSeek V3.2 measured
Design Arena: UI componentsNot measured1149Only DeepSeek V3.2 measured
Design Arena: game devNot measured1131Only DeepSeek V3.2 measured
Design Arena: data vizNot measured1158Only DeepSeek V3.2 measured
Design Arena: 3DNot measured1133Only DeepSeek V3.2 measured
Design Arena: all code categoriesNot measured1164Only DeepSeek V3.2 measured
Design Arena: SVGNot measured1032Only DeepSeek V3.2 measured
Design Arena: ASCII artNot measured1082Only DeepSeek V3.2 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 DeepSeek V3.2
BenchmarkCommand R (08-2024)DeepSeek V3.2Leader
IFBenchNot measured60.7%Only DeepSeek V3.2 measured
Long context reasoning (AA-LCR)Not measured73.3%Only DeepSeek V3.2 measured
DTBenchNot measured85.6%Only DeepSeek V3.2 measured
LMCANot measured28.8%Only DeepSeek V3.2 measured
CL-benchNot measured12.4%Only DeepSeek V3.2 measured
CL-bench LifeNot measured7.4%Only DeepSeek V3.2 measured
LMArena Text12291425DeepSeek V3.2
LMArena Text: instruction following11971413DeepSeek V3.2
LMArena Text: creative writing12341400DeepSeek V3.2
LMArena Text: English12381439DeepSeek V3.2
LMArena Text: non-English12161409DeepSeek V3.2
LMArena Text: Chinese12271455DeepSeek V3.2
LMArena Text: German12161413DeepSeek V3.2
LMArena Text: Japanese11661374DeepSeek V3.2
LMArena Text: Korean11621370DeepSeek V3.2
LMArena Text: FrenchNot measured1429Only DeepSeek V3.2 measured
LMArena Text: SpanishNot measured1424Only DeepSeek V3.2 measured
LMArena Text: Russian12271424DeepSeek V3.2
LMArena Text: PolishNot measured1419Only DeepSeek V3.2 measured
LMArena Text: multi-turn12131427DeepSeek V3.2
LMArena Text: longer queries12301428DeepSeek V3.2
LMArena Text: writing and language12411407DeepSeek V3.2
LMArena Text: entertainment and media12231395DeepSeek V3.2
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 R (08-2024) and DeepSeek V3.2
BenchmarkCommand R (08-2024)DeepSeek V3.2Leader
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
DeepSeek V3.2$0.0007Fits 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
DeepSeek V3.2$0.017Fits 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
DeepSeek V3.2$0.067Exceeds 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 DeepSeek V3.2
SpecificationCohere Command R (08-2024)DeepSeek V3.2
MakerCohereDeepSeek
WeightsNo public weights listedOpen weights
ReleasedAugust 30, 2024December 1, 2025
Context window128K164K
Input $/Mtok$0.15$0.29
Output $/Mtok$0.60$0.88
Coding indexNot benchmarked30.0
Independent measurements behind it03
Cost per solved taskNot computable$0.99
SourceOpenRouter listingOpenRouter listing

Questions

Is Command R (08-2024) better than DeepSeek V3.2 for coding?

Only DeepSeek V3.2 has a coding score, 30.0 on the Vibe Coding Index; Command R (08-2024) has none, so the two cannot be ranked on coding. Command R (08-2024) has the edge where output tokens cost 1.5x less.

Which is cheaper, Command R (08-2024) or DeepSeek V3.2?

Command R (08-2024) costs $0.15 per million input tokens and $0.60 per million output tokens; DeepSeek V3.2 costs $0.29 and $0.88. Cost per solved task is known for DeepSeek V3.2 only.

Which has the bigger context window, Command R (08-2024) or DeepSeek V3.2?

DeepSeek V3.2, with 164K tokens against 128K.

How many benchmarks is the Command R (08-2024) vs DeepSeek V3.2 comparison based on?

25 benchmarks have a result for both models as of October 21, 2026. Command R (08-2024)'s coding index rests on 0 independent benchmarks and DeepSeek V3.2's on 3. Where only one model has a result, the page says so instead of naming a winner.

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