Command R (08-2024) vs DeepSeek V3.2 Exp
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.
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
Command R (08-2024) has the edge where what matters is LMArena Text: coding or LMArena Text: software and IT, where it leads.
- Shared results
- 25
- Command R (08-2024) only
- 0
- DeepSeek V3.2 Exp only
- 32
- 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.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only DeepSeek V3.2 Exp has a AA Intelligence Index result, 16.6.
Directional only
Web design
Design Arena: websites
No call
Only DeepSeek V3.2 Exp has a Design Arena: websites result, 1179.
Directional only
Long documents
Listed context window
DeepSeek V3.2 Exp
DeepSeek V3.2 Exp takes 164K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Command R (08-2024)
Command R (08-2024) $0.0004, DeepSeek V3.2 Exp $0.0005 per turn.
List prices
At a glance
- Coding index
- Command R (08-2024) not benchmarked, DeepSeek V3.2 Exp not benchmarked.
- Widest category
- Reasoning, 41.2 points apart on average over 3 shared benchmarks.
- Widest single gap
- LMArena Text: coding: Command R (08-2024) 1187 against DeepSeek V3.2 Exp 1438, 41.9 points apart on a 0 to 100 scale.
- Output price
- Command R (08-2024) $0.60 against DeepSeek V3.2 Exp $0.41 per million output tokens, 1.5x apart.
- Context window
- Command R (08-2024) takes 128K tokens, DeepSeek V3.2 Exp 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.
| Category | Command R (08-2024) | Bars | DeepSeek V3.2 Exp | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Reasoning | 0 | 3 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| General | 0 | 14 | ||
| All shared boards | 0 | 25 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | Command R (08-2024) | DeepSeek V3.2 Exp | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 74.2% | Only DeepSeek V3.2 Exp measured |
| LMArena WebDev | Not measured | 1272 | Only DeepSeek V3.2 Exp measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 78.9% | Only DeepSeek V3.2 Exp measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 31.1% | Only DeepSeek V3.2 Exp measured |
| WeirdML | Not measured | 39.5% | Only DeepSeek V3.2 Exp measured |
| LMArena Text: coding | 1187 | 1438 | DeepSeek V3.2 Exp |
| LMArena Text: software and IT | 1197 | 1438 | DeepSeek V3.2 Exp |
| LMArena WebDev: HTML | Not measured | 1281 | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 30 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Command R (08-2024) | DeepSeek V3.2 Exp | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 16.6 | Only DeepSeek V3.2 Exp measured |
| Epoch Capabilities Index | Not measured | 144.9 | Only DeepSeek V3.2 Exp measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Command R (08-2024) | DeepSeek V3.2 Exp | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 33.9% | Only DeepSeek V3.2 Exp measured |
| TheAgentCompany | Not measured | 42.9% | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Command R (08-2024) | DeepSeek V3.2 Exp | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 79.7% | Only DeepSeek V3.2 Exp measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 14.9% | Only DeepSeek V3.2 Exp measured |
| LMArena Text: hard prompts | 1186 | 1429 | DeepSeek V3.2 Exp |
| LMArena Text: expert | 1173 | 1422 | DeepSeek V3.2 Exp |
| LMArena Text: hard prompts in English | 1194 | 1444 | DeepSeek V3.2 Exp |
| Neither model has a result on the other 16 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Command R (08-2024) | DeepSeek V3.2 Exp | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 87.7 | Only DeepSeek V3.2 Exp measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 87.7% | Only DeepSeek V3.2 Exp measured |
| LMArena Text: math | 1188 | 1422 | DeepSeek V3.2 Exp |
| LMArena Text: mathematical work | 1198 | 1433 | DeepSeek V3.2 Exp |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Command R (08-2024) | DeepSeek V3.2 Exp | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 85.0% | Only DeepSeek V3.2 Exp measured |
| LMArena Text: science | 1252 | 1443 | DeepSeek V3.2 Exp |
| LMArena Text: medicine and healthcare | 1238 | 1468 | DeepSeek V3.2 Exp |
| LMArena Text: legal and government | 1268 | 1419 | DeepSeek V3.2 Exp |
| LMArena Text: business and finance | 1215 | 1412 | DeepSeek V3.2 Exp |
| Neither model has a result on the other 12 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | Command R (08-2024) | DeepSeek V3.2 Exp | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1179 | Only DeepSeek V3.2 Exp measured |
| Design Arena: UI components | Not measured | 1167 | Only DeepSeek V3.2 Exp measured |
| Design Arena: game dev | Not measured | 1144 | Only DeepSeek V3.2 Exp measured |
| Design Arena: data viz | Not measured | 1152 | Only DeepSeek V3.2 Exp measured |
| Design Arena: 3D | Not measured | 1148 | Only DeepSeek V3.2 Exp measured |
| Design Arena: all code categories | Not measured | 1171 | Only DeepSeek V3.2 Exp measured |
| Design Arena: SVG | Not measured | 1032 | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index014
| Benchmark | Command R (08-2024) | DeepSeek V3.2 Exp | Leader |
|---|---|---|---|
| IFBench | Not measured | 54.1% | Only DeepSeek V3.2 Exp measured |
| Long context reasoning (AA-LCR) | Not measured | 72.3% | Only DeepSeek V3.2 Exp measured |
| DTBench | Not measured | 87.7% | Only DeepSeek V3.2 Exp measured |
| LMCA | Not measured | 29.1% | Only DeepSeek V3.2 Exp measured |
| Fiction.LiveBench | Not measured | 83.3% | Only DeepSeek V3.2 Exp measured |
| CL-bench | Not measured | 13.2% | Only DeepSeek V3.2 Exp measured |
| CL-bench Life | Not measured | 9.5% | Only DeepSeek V3.2 Exp measured |
| LMArena Text | 1229 | 1425 | DeepSeek V3.2 Exp |
| LMArena Text: instruction following | 1197 | 1401 | DeepSeek V3.2 Exp |
| LMArena Text: creative writing | 1234 | 1404 | DeepSeek V3.2 Exp |
| LMArena Text: English | 1238 | 1441 | DeepSeek V3.2 Exp |
| LMArena Text: non-English | 1216 | 1404 | DeepSeek V3.2 Exp |
| LMArena Text: Chinese | 1227 | 1461 | DeepSeek V3.2 Exp |
| LMArena Text: German | 1216 | 1440 | DeepSeek V3.2 Exp |
| LMArena Text: Japanese | 1166 | 1331 | DeepSeek V3.2 Exp |
| LMArena Text: Korean | 1162 | 1372 | DeepSeek V3.2 Exp |
| LMArena Text: French | Not measured | 1434 | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Spanish | Not measured | 1441 | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Russian | 1227 | 1412 | DeepSeek V3.2 Exp |
| LMArena Text: Polish | Not measured | 1435 | Only DeepSeek V3.2 Exp measured |
| LMArena Text: multi-turn | 1213 | 1422 | DeepSeek V3.2 Exp |
| LMArena Text: longer queries | 1230 | 1418 | DeepSeek V3.2 Exp |
| LMArena Text: writing and language | 1241 | 1407 | DeepSeek V3.2 Exp |
| LMArena Text: entertainment and media | 1223 | 1407 | DeepSeek V3.2 Exp |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Command R (08-2024) | DeepSeek V3.2 Exp | Leader |
|---|---|---|---|
| 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) costs less
Repository review
50K input + 3K output tokens
Command R (08-2024) costs less
Agent loop
200K input + 10K output tokens
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.
| Specification | Cohere Command R (08-2024) | DeepSeek V3.2 Exp |
|---|---|---|
| Maker | Cohere | DeepSeek |
| Weights | No public weights listed | Open weights |
| Released | August 30, 2024 | September 29, 2025 |
| Context window | 128K | 164K |
| Input $/Mtok | $0.15 | $0.27 |
| Output $/Mtok | $0.60 | $0.41 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Command R (08-2024) better than DeepSeek V3.2 Exp for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Command R (08-2024) has the edge where what matters is LMArena Text: coding or LMArena Text: software and IT, where it leads.
Which is cheaper, Command R (08-2024) or DeepSeek V3.2 Exp?
Command R (08-2024) costs $0.15 per million input tokens and $0.60 per million output tokens; DeepSeek V3.2 Exp costs $0.27 and $0.41. Cost per solved task is known for neither of them.
Which has the bigger context window, Command R (08-2024) or DeepSeek V3.2 Exp?
DeepSeek V3.2 Exp, with 164K tokens against 128K.
How many benchmarks is the Command R (08-2024) vs DeepSeek V3.2 Exp 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 Exp's on 1. Where only one model has a result, the page says so instead of naming a winner.