Command R7B (12-2024) vs DeepSeek V3.2 Exp
Command R7B (12-2024) costs 2.7x less per output token than DeepSeek V3.2 Exp ($0.15 vs $0.41 per million). Updated October 22, 2026; 1 benchmarks have a result for both.
Command R7B (12-2024) or DeepSeek V3.2 Exp: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Command R7B (12-2024) for output tokens 2.7x cheaper ($0.15 vs $0.41 per million) and work measured by Berkeley Function Calling Leaderboard (BFCL), where it leads.
- Shared results
- 1
- Command R7B (12-2024) only
- 0
- DeepSeek V3.2 Exp only
- 59
- Like-for-like categories
- 1 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 V3.2 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 R7B (12-2024)
Command R7B (12-2024) $0.0001, DeepSeek V3.2 Exp $0.0005 per turn.
List prices
At a glance
- Coding index
- Command R7B (12-2024) not benchmarked, DeepSeek V3.2 Exp not benchmarked.
- Widest category
- Agentic work, 24.7 points apart on average over 1 shared benchmark.
- Widest single gap
- Berkeley Function Calling Leaderboard (BFCL): Command R7B (12-2024) 32.1% against DeepSeek V3.2 Exp 56.7%, 24.7 points apart on a 0 to 100 scale.
- Output price
- Command R7B (12-2024) $0.15 against DeepSeek V3.2 Exp $0.41 per million output tokens, 2.7x apart.
- Context window
- Command R7B (12-2024) 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 R7B (12-2024) | Bars | DeepSeek V3.2 Exp | Ties |
|---|---|---|---|---|
| Agentic work | 0 | 1 | ||
| All shared boards | 0 | 1 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index8 boards, none shared
| Benchmark | Command R7B (12-2024) | 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 | Not measured | 1438 | Only DeepSeek V3.2 Exp measured |
| LMArena Text: software and IT | Not measured | 1438 | Only DeepSeek V3.2 Exp measured |
| LMArena WebDev: HTML | Not measured | 1281 | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 35 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Command R7B (12-2024) | 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 workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Command R7B (12-2024) | 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 |
| Berkeley Function Calling Leaderboard (BFCL) | 32.1% | 56.7% | DeepSeek V3.2 Exp |
| Neither model has a result on the other 39 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Command R7B (12-2024) | 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 | Not measured | 1429 | Only DeepSeek V3.2 Exp measured |
| LMArena Text: expert | Not measured | 1422 | Only DeepSeek V3.2 Exp measured |
| LMArena Text: hard prompts in English | Not measured | 1444 | Only DeepSeek V3.2 Exp measured |
| Kagi LLM Benchmark | Not measured | 52.2% | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 17 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Command R7B (12-2024) | 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 | Not measured | 1422 | Only DeepSeek V3.2 Exp measured |
| LMArena Text: mathematical work | Not measured | 1433 | Only DeepSeek V3.2 Exp measured |
| MathArena | Not measured | 14.4% | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Command R7B (12-2024) | DeepSeek V3.2 Exp | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 85.0% | Only DeepSeek V3.2 Exp measured |
| LMArena Text: science | Not measured | 1443 | Only DeepSeek V3.2 Exp measured |
| LMArena Text: medicine and healthcare | Not measured | 1468 | Only DeepSeek V3.2 Exp measured |
| LMArena Text: legal and government | Not measured | 1419 | Only DeepSeek V3.2 Exp measured |
| LMArena Text: business and finance | Not measured | 1412 | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 15 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | Command R7B (12-2024) | DeepSeek V3.2 Exp | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1179 | Only DeepSeek V3.2 Exp measured |
| Design Arena: UI components | Not measured | 1164 | Only DeepSeek V3.2 Exp measured |
| Design Arena: game dev | Not measured | 1143 | 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 | 1170 | Only DeepSeek V3.2 Exp measured |
| Design Arena: SVG | Not measured | 1031 | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 2 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index24 boards, none shared
| Benchmark | Command R7B (12-2024) | 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 | Not measured | 1425 | Only DeepSeek V3.2 Exp measured |
| LMArena Text: instruction following | Not measured | 1401 | Only DeepSeek V3.2 Exp measured |
| LMArena Text: creative writing | Not measured | 1404 | Only DeepSeek V3.2 Exp measured |
| LMArena Text: English | Not measured | 1441 | Only DeepSeek V3.2 Exp measured |
| LMArena Text: non-English | Not measured | 1404 | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Chinese | Not measured | 1461 | Only DeepSeek V3.2 Exp measured |
| LMArena Text: German | Not measured | 1440 | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Japanese | Not measured | 1331 | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Korean | Not measured | 1372 | Only DeepSeek V3.2 Exp measured |
| 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 | Not measured | 1412 | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Polish | Not measured | 1435 | Only DeepSeek V3.2 Exp measured |
| LMArena Text: multi-turn | Not measured | 1422 | Only DeepSeek V3.2 Exp measured |
| LMArena Text: longer queries | Not measured | 1418 | Only DeepSeek V3.2 Exp measured |
| LMArena Text: writing and language | Not measured | 1407 | Only DeepSeek V3.2 Exp measured |
| LMArena Text: entertainment and media | Not measured | 1407 | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 24 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Command R7B (12-2024) | 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 R7B (12-2024) costs less
Repository review
50K input + 3K output tokens
Command R7B (12-2024) costs less
Agent loop
200K input + 10K output tokens
Command R7B (12-2024) 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 R7B (12-2024) | DeepSeek V3.2 Exp |
|---|---|---|
| Maker | Cohere | DeepSeek |
| Weights | No public weights listed | Open weights |
| Released | December 14, 2024 | September 29, 2025 |
| Context window | 128K | 164K |
| Input $/Mtok | $0.0375 | $0.27 |
| Output $/Mtok | $0.15 | $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 R7B (12-2024) better than DeepSeek V3.2 Exp for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Command R7B (12-2024) for output tokens 2.7x cheaper ($0.15 vs $0.41 per million) and work measured by Berkeley Function Calling Leaderboard (BFCL), where it leads.
Which is cheaper, Command R7B (12-2024) or DeepSeek V3.2 Exp?
Command R7B (12-2024) costs $0.0375 per million input tokens and $0.15 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 R7B (12-2024) or DeepSeek V3.2 Exp?
DeepSeek V3.2 Exp, with 164K tokens against 128K.
How many benchmarks is the Command R7B (12-2024) vs DeepSeek V3.2 Exp comparison based on?
1 benchmarks have a result for both models as of October 22, 2026. Command R7B (12-2024)'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.