Command R7B (12-2024) vs DeepSeek V3.2
Command R7B (12-2024) costs 5.9x less per output token than DeepSeek V3.2 ($0.15 vs $0.88 per million). Updated October 21, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.15
- Coding
- 30.0
- Overall
- 37.9
- Output $/M
- $0.88
Command R7B (12-2024) or DeepSeek V3.2: which to pick
Only DeepSeek V3.2 has a coding score, 30.0 on the Vibe Coding Index; Command R7B (12-2024) has none, so the two cannot be ranked on coding.
Pick Command R7B (12-2024) for output tokens 5.9x cheaper ($0.15 vs $0.88 per million).
No benchmark has tested both yet. Command R7B (12-2024) has results on Berkeley Function Calling Leaderboard (BFCL); DeepSeek V3.2 on SWE-bench Verified, SWE-bench Multilingual and LMArena WebDev. 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
Only DeepSeek V3.2 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only DeepSeek V3.2 has a AA Intelligence Index result, 21.5.
Directional only
Reasoning
GPQA Diamond
No call
Only DeepSeek V3.2 has a GPQA Diamond result, 80.3%.
Directional only
Math
AIME
No call
Only DeepSeek V3.2 has a AIME result, 84.6%.
Directional only
Knowledge
MMLU Pro
No call
Only DeepSeek V3.2 has a MMLU Pro result, 84.9%.
Directional only
Web design
Design Arena: websites
No call
Only DeepSeek V3.2 has a Design Arena: websites result, 1175.
Directional only
Long documents
Listed context window
DeepSeek V3.2
DeepSeek V3.2 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 $0.0007 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only DeepSeek V3.2 has the number, $0.99.
Directional only
At a glance
- Coding index
- Command R7B (12-2024) not benchmarked, DeepSeek V3.2 30.0 from 3 benchmarks.
- 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 R7B (12-2024) $0.15 against DeepSeek V3.2 $0.88 per million output tokens, 5.9x apart.
- Context window
- Command R7B (12-2024) takes 128K tokens, DeepSeek V3.2 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 Index14 boards measured
| Benchmark | Command R7B (12-2024) | DeepSeek V3.2 | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 70.0% | Only DeepSeek V3.2 measured |
| SWE-bench Multilingual | Not measured | 59.0% | Only DeepSeek V3.2 measured |
| LMArena WebDev | Not measured | 1362 | Only DeepSeek V3.2 measured |
| AA Coding Index | Not measured | 44.2 | Only DeepSeek V3.2 measured |
| LiveCodeBench (Vals AI) | Not measured | 80.7% | Only DeepSeek V3.2 measured |
| SWE-bench Verified (Vals AI) | Not measured | 67.6% | Only DeepSeek V3.2 measured |
| Vibe Code Bench (Vals AI) | Not measured | 5.1% | Only DeepSeek V3.2 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 86.2% | Only DeepSeek V3.2 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 35.6% | Only DeepSeek V3.2 measured |
| Terminal-Bench | Not measured | 39.6% | Only DeepSeek V3.2 measured |
| LMArena Text: coding | Not measured | 1453 | Only DeepSeek V3.2 measured |
| LMArena Text: software and IT | Not measured | 1449 | Only DeepSeek V3.2 measured |
| LMArena WebDev: React | Not measured | 1350 | Only DeepSeek V3.2 measured |
| LMArena WebDev: HTML | Not measured | 1371 | Only DeepSeek V3.2 measured |
| Neither model has a result on the other 25 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Command R7B (12-2024) | DeepSeek V3.2 | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 21.5 | Only DeepSeek V3.2 measured |
| Epoch Capabilities Index | Not measured | 146.1 | Only DeepSeek V3.2 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | Command R7B (12-2024) | DeepSeek V3.2 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 90.6% | Only DeepSeek V3.2 measured |
| APEX-Agents | Not measured | 21.3% | Only DeepSeek V3.2 measured |
| Vending-Bench 2 | Not measured | $1,034 | Only DeepSeek V3.2 measured |
| Berkeley Function Calling Leaderboard (BFCL) | 32.1% | Not measured | Only Command R7B (12-2024) measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index8 boards measured
| Benchmark | Command R7B (12-2024) | DeepSeek V3.2 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 80.3% | Only DeepSeek V3.2 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 84.0% | Only DeepSeek V3.2 measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 24.6% | Only DeepSeek V3.2 measured |
| ARC-AGI | Not measured | 57.0% | Only DeepSeek V3.2 measured |
| ARC-AGI-2 | Not measured | 4.0% | Only DeepSeek V3.2 measured |
| LMArena Text: hard prompts | Not measured | 1434 | Only DeepSeek V3.2 measured |
| LMArena Text: expert | Not measured | 1436 | Only DeepSeek V3.2 measured |
| LMArena Text: hard prompts in English | Not measured | 1446 | Only DeepSeek V3.2 measured |
| Neither model has a result on the other 14 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | Command R7B (12-2024) | DeepSeek V3.2 | Leader |
|---|---|---|---|
| AIME | Not measured | 84.6% | Only DeepSeek V3.2 measured |
| MGSM (Vals AI) | Not measured | 86.0% | Only DeepSeek V3.2 measured |
| AA Math Index | Not measured | 92.0 | Only DeepSeek V3.2 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 92.0% | Only DeepSeek V3.2 measured |
| LMArena Text: math | Not measured | 1435 | Only DeepSeek V3.2 measured |
| LMArena Text: mathematical work | Not measured | 1440 | Only DeepSeek V3.2 measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index11 boards measured
| Benchmark | Command R7B (12-2024) | DeepSeek V3.2 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 84.9% | Only DeepSeek V3.2 measured |
| LegalBench (Vals AI) | Not measured | 76.1% | Only DeepSeek V3.2 measured |
| CaseLaw (Vals AI) | Not measured | 55.4% | Only DeepSeek V3.2 measured |
| CorpFin (Vals AI) | Not measured | 51.0% | Only DeepSeek V3.2 measured |
| TaxEval (Vals AI) | Not measured | 68.2% | Only DeepSeek V3.2 measured |
| MedQA (Vals AI) | Not measured | 93.9% | Only DeepSeek V3.2 measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 86.2% | Only DeepSeek V3.2 measured |
| LMArena Text: science | Not measured | 1446 | Only DeepSeek V3.2 measured |
| LMArena Text: medicine and healthcare | Not measured | 1440 | Only DeepSeek V3.2 measured |
| LMArena Text: legal and government | Not measured | 1425 | Only DeepSeek V3.2 measured |
| LMArena Text: business and finance | Not measured | 1410 | Only DeepSeek V3.2 measured |
| Neither model has a result on the other 7 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards measured
| Benchmark | Command R7B (12-2024) | DeepSeek V3.2 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1175 | Only DeepSeek V3.2 measured |
| Design Arena: UI components | Not measured | 1149 | Only DeepSeek V3.2 measured |
| Design Arena: game dev | Not measured | 1132 | Only DeepSeek V3.2 measured |
| Design Arena: data viz | Not measured | 1158 | Only DeepSeek V3.2 measured |
| Design Arena: 3D | Not measured | 1133 | Only DeepSeek V3.2 measured |
| Design Arena: all code categories | Not measured | 1164 | Only DeepSeek V3.2 measured |
| Design Arena: SVG | Not measured | 1032 | Only DeepSeek V3.2 measured |
| Design Arena: ASCII art | Not measured | 1083 | Only DeepSeek V3.2 measured |
| Neither model has a result on the other 1 board here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index23 boards measured
| Benchmark | Command R7B (12-2024) | DeepSeek V3.2 | Leader |
|---|---|---|---|
| IFBench | Not measured | 60.7% | Only DeepSeek V3.2 measured |
| Long context reasoning (AA-LCR) | Not measured | 73.3% | Only DeepSeek V3.2 measured |
| DTBench | Not measured | 85.6% | Only DeepSeek V3.2 measured |
| LMCA | Not measured | 28.8% | Only DeepSeek V3.2 measured |
| CL-bench | Not measured | 12.4% | Only DeepSeek V3.2 measured |
| CL-bench Life | Not measured | 7.4% | Only DeepSeek V3.2 measured |
| LMArena Text | Not measured | 1425 | Only DeepSeek V3.2 measured |
| LMArena Text: instruction following | Not measured | 1413 | Only DeepSeek V3.2 measured |
| LMArena Text: creative writing | Not measured | 1400 | Only DeepSeek V3.2 measured |
| LMArena Text: English | Not measured | 1439 | Only DeepSeek V3.2 measured |
| LMArena Text: non-English | Not measured | 1409 | Only DeepSeek V3.2 measured |
| LMArena Text: Chinese | Not measured | 1455 | Only DeepSeek V3.2 measured |
| LMArena Text: German | Not measured | 1413 | Only DeepSeek V3.2 measured |
| LMArena Text: Japanese | Not measured | 1374 | Only DeepSeek V3.2 measured |
| LMArena Text: Korean | Not measured | 1370 | Only DeepSeek V3.2 measured |
| LMArena Text: French | Not measured | 1429 | Only DeepSeek V3.2 measured |
| LMArena Text: Spanish | Not measured | 1424 | Only DeepSeek V3.2 measured |
| LMArena Text: Russian | Not measured | 1424 | Only DeepSeek V3.2 measured |
| LMArena Text: Polish | Not measured | 1419 | Only DeepSeek V3.2 measured |
| LMArena Text: multi-turn | Not measured | 1427 | Only DeepSeek V3.2 measured |
| LMArena Text: longer queries | Not measured | 1428 | Only DeepSeek V3.2 measured |
| LMArena Text: writing and language | Not measured | 1407 | Only DeepSeek V3.2 measured |
| LMArena Text: entertainment and media | Not measured | 1395 | Only DeepSeek V3.2 measured |
| Neither model has a result on the other 23 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 |
|---|---|---|
| Maker | Cohere | DeepSeek |
| Weights | No public weights listed | Open weights |
| Released | December 14, 2024 | December 1, 2025 |
| Context window | 128K | 164K |
| Input $/Mtok | $0.0375 | $0.29 |
| Output $/Mtok | $0.15 | $0.88 |
| Coding index | Not benchmarked | 30.0 |
| Independent measurements behind it | 0 | 3 |
| Cost per solved task | Not computable | $0.99 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Command R7B (12-2024) better than DeepSeek V3.2 for coding?
Only DeepSeek V3.2 has a coding score, 30.0 on the Vibe Coding Index; Command R7B (12-2024) has none, so the two cannot be ranked on coding. Pick Command R7B (12-2024) for output tokens 5.9x cheaper ($0.15 vs $0.88 per million).
Which is cheaper, Command R7B (12-2024) or DeepSeek V3.2?
Command R7B (12-2024) costs $0.0375 per million input tokens and $0.15 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 R7B (12-2024) or DeepSeek V3.2?
DeepSeek V3.2, with 164K tokens against 128K.
Has any benchmark tested both Command R7B (12-2024) and DeepSeek V3.2?
No benchmark has tested both yet. Command R7B (12-2024) has results on Berkeley Function Calling Leaderboard (BFCL); DeepSeek V3.2 on SWE-bench Verified, SWE-bench Multilingual and LMArena WebDev. Compare them on price and context instead. Data as of October 21, 2026.