Command R (08-2024) vs DeepSeek V3.1 Terminus
Command R (08-2024) costs 1.7x less per output token than DeepSeek V3.1 Terminus ($0.60 vs $1.0142 per million). Updated October 21, 2026; 19 benchmarks have a result for both.
Command R (08-2024) or DeepSeek V3.1 Terminus: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Command R (08-2024) for output tokens 1.7x cheaper ($0.60 vs $1.0142 per million) and work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
- Shared results
- 19
- Command R (08-2024) only
- 6
- DeepSeek V3.1 Terminus only
- 26
- 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.
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 V3.1 Terminus has a AA Intelligence Index result, 14.8.
Directional only
Agentic work
AA Agentic Index
No call
Only DeepSeek V3.1 Terminus has a AA Agentic Index result, 6.8.
Directional only
Web design
Design Arena: websites
No call
Only DeepSeek V3.1 Terminus has a Design Arena: websites result, 1187.
Directional only
Long documents
Listed context window
DeepSeek V3.1 Terminus
DeepSeek V3.1 Terminus 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 V3.1 Terminus $0.0008 per turn.
List prices
At a glance
- Coding index
- Command R (08-2024) not benchmarked, DeepSeek V3.1 Terminus not benchmarked.
- Widest category
- Reasoning, 39.8 points apart on average over 2 shared benchmarks.
- Widest single gap
- LMArena Text: hard prompts: Command R (08-2024) 1186 against DeepSeek V3.1 Terminus 1426, 40.0 points apart on a 0 to 100 scale.
- Output price
- Command R (08-2024) $0.60 against DeepSeek V3.1 Terminus $1.0142 per million output tokens, 1.7x apart.
- Context window
- Command R (08-2024) takes 128K tokens, DeepSeek V3.1 Terminus 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 V3.1 Terminus | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Reasoning | 0 | 2 | ||
| Math | 0 | 1 | ||
| Knowledge | 0 | 4 | ||
| General | 0 | 10 | ||
| All shared boards | 0 | 19 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | Command R (08-2024) | DeepSeek V3.1 Terminus | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 43.5 | Only DeepSeek V3.1 Terminus measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 79.8% | Only DeepSeek V3.1 Terminus measured |
| SciCode | Not measured | 38.0% | Only DeepSeek V3.1 Terminus measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 31.8% | Only DeepSeek V3.1 Terminus measured |
| ALE-Bench | Not measured | 745.2 | Only DeepSeek V3.1 Terminus measured |
| LMArena Text: coding | 1187 | 1425 | DeepSeek V3.1 Terminus |
| LMArena Text: software and IT | 1197 | 1426 | DeepSeek V3.1 Terminus |
| Neither model has a result on the other 32 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Command R (08-2024) | DeepSeek V3.1 Terminus | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 14.8 | Only DeepSeek V3.1 Terminus measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Command R (08-2024) | DeepSeek V3.1 Terminus | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 6.8 | Only DeepSeek V3.1 Terminus measured |
| τ²-Bench Telecom | Not measured | 37.1% | Only DeepSeek V3.1 Terminus measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Command R (08-2024) | DeepSeek V3.1 Terminus | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 79.2% | Only DeepSeek V3.1 Terminus measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 16.4% | Only DeepSeek V3.1 Terminus measured |
| LMArena Text: hard prompts | 1186 | 1426 | DeepSeek V3.1 Terminus |
| LMArena Text: expert | 1173 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: hard prompts in English | 1194 | 1432 | DeepSeek V3.1 Terminus |
| Kagi LLM Benchmark | Not measured | 57.4% | Only DeepSeek V3.1 Terminus measured |
| Neither model has a result on the other 16 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Command R (08-2024) | DeepSeek V3.1 Terminus | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 89.7 | Only DeepSeek V3.1 Terminus measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 89.7% | Only DeepSeek V3.1 Terminus measured |
| LMArena Text: math | 1188 | 1402 | DeepSeek V3.1 Terminus |
| LMArena Text: mathematical work | 1198 | Not measured | Only Command R (08-2024) measured |
| 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 V3.1 Terminus | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 85.1% | Only DeepSeek V3.1 Terminus measured |
| LMArena Text: science | 1252 | 1451 | DeepSeek V3.1 Terminus |
| LMArena Text: medicine and healthcare | 1238 | 1468 | DeepSeek V3.1 Terminus |
| LMArena Text: legal and government | 1268 | 1457 | DeepSeek V3.1 Terminus |
| LMArena Text: business and finance | 1215 | 1414 | DeepSeek V3.1 Terminus |
| Neither model has a result on the other 13 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | Command R (08-2024) | DeepSeek V3.1 Terminus | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1187 | Only DeepSeek V3.1 Terminus measured |
| Design Arena: UI components | Not measured | 1182 | Only DeepSeek V3.1 Terminus measured |
| Design Arena: game dev | Not measured | 1131 | Only DeepSeek V3.1 Terminus measured |
| Design Arena: data viz | Not measured | 1167 | Only DeepSeek V3.1 Terminus measured |
| Design Arena: 3D | Not measured | 1141 | Only DeepSeek V3.1 Terminus measured |
| Design Arena: all code categories | Not measured | 1175 | Only DeepSeek V3.1 Terminus measured |
| Design Arena: SVG | Not measured | 1065 | Only DeepSeek V3.1 Terminus measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index010
| Benchmark | Command R (08-2024) | DeepSeek V3.1 Terminus | Leader |
|---|---|---|---|
| IFBench | Not measured | 57.0% | Only DeepSeek V3.1 Terminus measured |
| Long context reasoning (AA-LCR) | Not measured | 69.3% | Only DeepSeek V3.1 Terminus measured |
| DTBench | Not measured | 81.3% | Only DeepSeek V3.1 Terminus measured |
| LMCA | Not measured | 28.6% | Only DeepSeek V3.1 Terminus measured |
| LMArena Text | 1229 | 1419 | DeepSeek V3.1 Terminus |
| LMArena Text: instruction following | 1197 | 1404 | DeepSeek V3.1 Terminus |
| LMArena Text: creative writing | 1234 | 1403 | DeepSeek V3.1 Terminus |
| LMArena Text: English | 1238 | 1429 | DeepSeek V3.1 Terminus |
| LMArena Text: non-English | 1216 | 1408 | DeepSeek V3.1 Terminus |
| 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 | 1435 | DeepSeek V3.1 Terminus |
| LMArena Text: Polish | Not measured | 1427 | Only DeepSeek V3.1 Terminus measured |
| LMArena Text: multi-turn | 1213 | 1411 | DeepSeek V3.1 Terminus |
| LMArena Text: longer queries | 1230 | 1422 | DeepSeek V3.1 Terminus |
| LMArena Text: writing and language | 1241 | 1409 | DeepSeek V3.1 Terminus |
| LMArena Text: entertainment and media | 1223 | 1391 | DeepSeek V3.1 Terminus |
| Neither model has a result on the other 27 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Command R (08-2024) | DeepSeek V3.1 Terminus | 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 V3.1 Terminus |
|---|---|---|
| Maker | Cohere | DeepSeek |
| Weights | No public weights listed | Open weights |
| Released | August 30, 2024 | September 22, 2025 |
| Context window | 128K | 164K |
| Input $/Mtok | $0.15 | $0.3063 |
| Output $/Mtok | $0.60 | $1.0142 |
| 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 V3.1 Terminus for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Command R (08-2024) for output tokens 1.7x cheaper ($0.60 vs $1.0142 per million) and work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
Which is cheaper, Command R (08-2024) or DeepSeek V3.1 Terminus?
Command R (08-2024) costs $0.15 per million input tokens and $0.60 per million output tokens; DeepSeek V3.1 Terminus costs $0.3063 and $1.0142. Cost per solved task is known for neither of them.
Which has the bigger context window, Command R (08-2024) or DeepSeek V3.1 Terminus?
DeepSeek V3.1 Terminus, with 164K tokens against 128K.
How many benchmarks is the Command R (08-2024) vs DeepSeek V3.1 Terminus comparison based on?
19 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 V3.1 Terminus's on 0. Where only one model has a result, the page says so instead of naming a winner.