Command R7B (12-2024) vs Gemma 3 12B
Command R7B (12-2024) and Gemma 3 12B cost about the same per output token ($0.15 vs $0.1625 per million). Updated October 21, 2026; 1 benchmarks have a result for both.
Command R7B (12-2024) or Gemma 3 12B: 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 1.1x cheaper ($0.15 vs $0.1625 per million).
- Shared results
- 1
- Command R7B (12-2024) only
- 0
- Gemma 3 12B only
- 42
- 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 Gemma 3 12B has a AA Intelligence Index result, 3.8.
Directional only
Agentic work
AA Agentic Index
No call
Only Gemma 3 12B has a AA Agentic Index result, 0.1.
Directional only
Long documents
Listed context window
Gemma 3 12B
Gemma 3 12B takes 131K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Command R7B (12-2024)
Command R7B (12-2024) $0.0001, Gemma 3 12B $0.0002 per turn.
List prices
At a glance
- Coding index
- Command R7B (12-2024) not benchmarked, Gemma 3 12B not benchmarked.
- Widest category
- Agentic work, 1.6 points apart on average over 1 shared benchmark.
- Widest single gap
- Berkeley Function Calling Leaderboard (BFCL): Command R7B (12-2024) 32.1% against Gemma 3 12B 30.4%, 1.6 points apart on a 0 to 100 scale.
- Output price
- Command R7B (12-2024) $0.15 against Gemma 3 12B $0.1625 per million output tokens, 1.1x apart.
- Context window
- Command R7B (12-2024) takes 128K tokens, Gemma 3 12B 131K.
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 | Gemma 3 12B | Ties |
|---|---|---|---|---|
| Agentic work | 1 | 0 | ||
| All shared boards | 1 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index7 boards, none shared
| Benchmark | Command R7B (12-2024) | Gemma 3 12B | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 5.8 | Only Gemma 3 12B measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 13.7% | Only Gemma 3 12B measured |
| SciCode | Not measured | 16.4% | Only Gemma 3 12B measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 0.8% | Only Gemma 3 12B measured |
| LMArena Text: coding | Not measured | 1281 | Only Gemma 3 12B measured |
| LMArena Text: software and IT | Not measured | 1313 | Only Gemma 3 12B measured |
| SciCode | Not measured | 0.0% | Only Gemma 3 12B measured |
| Neither model has a result on the other 32 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Command R7B (12-2024) | Gemma 3 12B | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 3.8 | Only Gemma 3 12B measured |
| Epoch Capabilities Index | Not measured | 123.5 | Only Gemma 3 12B measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Command R7B (12-2024) | Gemma 3 12B | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 0.1 | Only Gemma 3 12B measured |
| τ²-Bench Telecom | Not measured | 10.8% | Only Gemma 3 12B measured |
| Berkeley Function Calling Leaderboard (BFCL) | 32.1% | 30.4% | Command R7B (12-2024) |
| Neither model has a result on the other 38 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Command R7B (12-2024) | Gemma 3 12B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 34.9% | Only Gemma 3 12B measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 4.2% | Only Gemma 3 12B measured |
| GPQA Diamond (Epoch AI run) | Not measured | 39.5% | Only Gemma 3 12B measured |
| LMArena Text: hard prompts | Not measured | 1309 | Only Gemma 3 12B measured |
| LMArena Text: expert | Not measured | 1248 | Only Gemma 3 12B measured |
| LMArena Text: hard prompts in English | Not measured | 1318 | Only Gemma 3 12B measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Command R7B (12-2024) | Gemma 3 12B | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 18.3 | Only Gemma 3 12B measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 18.3% | Only Gemma 3 12B measured |
| OTIS Mock AIME | Not measured | 16.7% | Only Gemma 3 12B measured |
| LMArena Text: math | Not measured | 1307 | Only Gemma 3 12B measured |
| LMArena Text: mathematical work | Not measured | 1336 | Only Gemma 3 12B measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Command R7B (12-2024) | Gemma 3 12B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 59.5% | Only Gemma 3 12B measured |
| LMArena Text: science | Not measured | 1366 | Only Gemma 3 12B measured |
| LMArena Text: medicine and healthcare | Not measured | 1334 | Only Gemma 3 12B measured |
| LMArena Text: legal and government | Not measured | 1381 | Only Gemma 3 12B measured |
| LMArena Text: business and finance | Not measured | 1376 | Only Gemma 3 12B measured |
| Neither model has a result on the other 13 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Command R7B (12-2024) | Gemma 3 12B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index15 boards, none shared
| Benchmark | Command R7B (12-2024) | Gemma 3 12B | Leader |
|---|---|---|---|
| IFBench | Not measured | 36.7% | Only Gemma 3 12B measured |
| Long context reasoning (AA-LCR) | Not measured | 8.3% | Only Gemma 3 12B measured |
| DTBench | Not measured | 48.8% | Only Gemma 3 12B measured |
| LMCA | Not measured | 4.5% | Only Gemma 3 12B measured |
| LMArena Text | Not measured | 1334 | Only Gemma 3 12B measured |
| LMArena Text: instruction following | Not measured | 1299 | Only Gemma 3 12B measured |
| LMArena Text: creative writing | Not measured | 1331 | Only Gemma 3 12B measured |
| LMArena Text: English | Not measured | 1349 | Only Gemma 3 12B measured |
| LMArena Text: non-English | Not measured | 1318 | Only Gemma 3 12B measured |
| LMArena Text: German | Not measured | 1370 | Only Gemma 3 12B measured |
| LMArena Text: Russian | Not measured | 1335 | Only Gemma 3 12B measured |
| LMArena Text: multi-turn | Not measured | 1334 | Only Gemma 3 12B measured |
| LMArena Text: longer queries | Not measured | 1317 | Only Gemma 3 12B measured |
| LMArena Text: writing and language | Not measured | 1307 | Only Gemma 3 12B measured |
| LMArena Text: entertainment and media | Not measured | 1297 | Only Gemma 3 12B measured |
| Neither model has a result on the other 31 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Command R7B (12-2024) | Gemma 3 12B | 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) | Google Gemma 3 12B |
|---|---|---|
| Maker | Cohere | |
| Weights | No public weights listed | Open weights |
| Released | December 14, 2024 | March 13, 2025 |
| Context window | 128K | 131K |
| Input $/Mtok | $0.0375 | $0.075 |
| Output $/Mtok | $0.15 | $0.1625 |
| 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 R7B (12-2024) better than Gemma 3 12B 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 1.1x cheaper ($0.15 vs $0.1625 per million).
Which is cheaper, Command R7B (12-2024) or Gemma 3 12B?
Command R7B (12-2024) costs $0.0375 per million input tokens and $0.15 per million output tokens; Gemma 3 12B costs $0.075 and $0.1625. Cost per solved task is known for neither of them.
Which has the bigger context window, Command R7B (12-2024) or Gemma 3 12B?
Gemma 3 12B, with 131K tokens against 128K.
How many benchmarks is the Command R7B (12-2024) vs Gemma 3 12B comparison based on?
1 benchmarks have a result for both models as of October 21, 2026. Command R7B (12-2024)'s coding index rests on 0 independent benchmarks and Gemma 3 12B's on 0. Where only one model has a result, the page says so instead of naming a winner.