Command R (08-2024) vs DeepSeek V3
Updated October 21, 2026. No benchmark has tested both models yet. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.60
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.9594
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 output tokens cost 1.6x less.
No benchmark has tested both yet. Command R (08-2024) has results on LMArena Text: coding, LMArena Text: software and IT and LMArena Text: hard prompts; DeepSeek V3 on Aider polyglot, SWE-bench Lite and GPQA Diamond (Epoch AI run). Compare them on price and context instead.
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
Web design
Design Arena: websites
No call
Only DeepSeek V3 has a Design Arena: websites result, 1120.
Directional only
Long documents
Listed context window
DeepSeek V3
DeepSeek V3 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 $0.0008 per turn.
List prices
At a glance
- Coding index
- Command R (08-2024) not benchmarked, DeepSeek V3 not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- Command R (08-2024) $0.60 against DeepSeek V3 $0.9594 per million output tokens, 1.6x apart.
- Context window
- Command R (08-2024) takes 128K tokens, DeepSeek V3 164K.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index4 boards measured
| Benchmark | Command R (08-2024) | DeepSeek V3 | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 70.2% | Only DeepSeek V3 measured |
| SWE-bench Lite | Not measured | 30.7% | Only DeepSeek V3 measured |
| LMArena Text: coding | 1187 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: software and IT | 1197 | Not measured | Only Command R (08-2024) measured |
| Neither model has a result on the other 34 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | Command R (08-2024) | DeepSeek V3 | Leader |
|---|---|---|---|
| GPQA Diamond (Epoch AI run) | Not measured | 71.2% | Only DeepSeek V3 measured |
| LMArena Text: hard prompts | 1186 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: expert | 1173 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: hard prompts in English | 1194 | Not measured | Only Command R (08-2024) measured |
| Neither model has a result on the other 17 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | Command R (08-2024) | DeepSeek V3 | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 48.9% | Only DeepSeek V3 measured |
| LMArena Text: math | 1188 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: mathematical work | 1198 | Not measured | Only Command R (08-2024) measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | Command R (08-2024) | DeepSeek V3 | Leader |
|---|---|---|---|
| LMArena Text: science | 1252 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: medicine and healthcare | 1238 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: legal and government | 1268 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: business and finance | 1215 | Not measured | Only Command R (08-2024) measured |
| Neither model has a result on the other 13 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | Command R (08-2024) | DeepSeek V3 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1120 | Only DeepSeek V3 measured |
| Design Arena: UI components | Not measured | 1093 | Only DeepSeek V3 measured |
| Design Arena: game dev | Not measured | 1053 | Only DeepSeek V3 measured |
| Design Arena: data viz | Not measured | 1089 | Only DeepSeek V3 measured |
| Design Arena: 3D | Not measured | 1087 | Only DeepSeek V3 measured |
| Design Arena: all code categories | Not measured | 1108 | Only DeepSeek V3 measured |
| Design Arena: SVG | Not measured | 977 | Only DeepSeek V3 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index16 boards measured
| Benchmark | Command R (08-2024) | DeepSeek V3 | Leader |
|---|---|---|---|
| DTBench | Not measured | 62.7% | Only DeepSeek V3 measured |
| LMCA | Not measured | 15.2% | Only DeepSeek V3 measured |
| LMArena Text | 1229 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: instruction following | 1197 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: creative writing | 1234 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: English | 1238 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: non-English | 1216 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: Chinese | 1227 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: German | 1216 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: Japanese | 1166 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: Korean | 1162 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: Russian | 1227 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: multi-turn | 1213 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: longer queries | 1230 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: writing and language | 1241 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: entertainment and media | 1223 | Not measured | Only Command R (08-2024) measured |
| Neither model has a result on the other 30 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 |
|---|---|---|
| Maker | Cohere | DeepSeek |
| Weights | No public weights listed | Open weights |
| Released | August 30, 2024 | December 26, 2024 |
| Context window | 128K | 164K |
| Input $/Mtok | $0.15 | $0.2887 |
| Output $/Mtok | $0.60 | $0.9594 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Command R (08-2024) better than DeepSeek V3 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 output tokens cost 1.6x less.
Which is cheaper, Command R (08-2024) or DeepSeek V3?
Command R (08-2024) costs $0.15 per million input tokens and $0.60 per million output tokens; DeepSeek V3 costs $0.2887 and $0.9594. Cost per solved task is known for neither of them.
Which has the bigger context window, Command R (08-2024) or DeepSeek V3?
DeepSeek V3, with 164K tokens against 128K.
Has any benchmark tested both Command R (08-2024) and DeepSeek V3?
No benchmark has tested both yet. Command R (08-2024) has results on LMArena Text: coding, LMArena Text: software and IT and LMArena Text: hard prompts; DeepSeek V3 on Aider polyglot, SWE-bench Lite and GPQA Diamond (Epoch AI run). Compare them on price and context instead. Data as of October 21, 2026.