Gemma 3 12B vs Qwen2.5 7B Instruct
Gemma 3 12B costs 1.2x less per output token than Qwen2.5 7B Instruct ($0.1625 vs $0.20 per million). Updated October 22, 2026; 5 benchmarks have a result for both.
Gemma 3 12B or Qwen2.5 7B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Gemma 3 12B for output tokens 1.2x cheaper ($0.1625 vs $0.20 per million) and a larger context window (131K against 33K tokens).
- Shared results
- 5
- Gemma 3 12B only
- 38
- Qwen2.5 7B Instruct only
- 2
- Like-for-like categories
- 4 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 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Gemma 3 12B
Gemma 3 12B $0.0002, Qwen2.5 7B Instruct $0.0002 per turn.
List prices
At a glance
- Coding index
- Gemma 3 12B not benchmarked, Qwen2.5 7B Instruct not benchmarked.
- Widest category
- Math, 14.2 points apart on average over 1 shared benchmark.
- Widest single gap
- OTIS Mock AIME: Gemma 3 12B 16.7% against Qwen2.5 7B Instruct 2.5%, 14.2 points apart on a 0 to 100 scale.
- Output price
- Gemma 3 12B $0.1625 against Qwen2.5 7B Instruct $0.20 per million output tokens, 1.2x apart.
- Context window
- Gemma 3 12B takes 131K tokens, Qwen2.5 7B Instruct 33K.
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 | Gemma 3 12B | Bars | Qwen2.5 7B Instruct | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Reasoning | 1 | 0 | ||
| Math | 1 | 0 | ||
| General | 1 | 1 | ||
| All shared boards | 4 | 1 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index7 boards, none shared
| Benchmark | Gemma 3 12B | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| AA Coding Index | 5.8 | Not measured | Only Gemma 3 12B measured |
| LiveCodeBench (Artificial Analysis run) | 13.7% | Not measured | Only Gemma 3 12B measured |
| SciCode | 16.4% | Not measured | Only Gemma 3 12B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 0.8% | Not measured | Only Gemma 3 12B measured |
| LMArena Text: coding | 1281 | Not measured | Only Gemma 3 12B measured |
| LMArena Text: software and IT | 1313 | Not measured | Only Gemma 3 12B measured |
| SciCode | 0.0% | Not measured | Only Gemma 3 12B measured |
| Neither model has a result on the other 32 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Gemma 3 12B | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 3.8 | Not measured | Only Gemma 3 12B measured |
| Epoch Capabilities Index | 123.5 | 118.5 | Gemma 3 12B |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | Gemma 3 12B | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| AA Agentic Index | 0.1 | Not measured | Only Gemma 3 12B measured |
| τ²-Bench Telecom | 10.8% | Not measured | Only Gemma 3 12B measured |
| BALROG | Not measured | 7.8% | Only Qwen2.5 7B Instruct measured |
| Berkeley Function Calling Leaderboard (BFCL) | 30.4% | Not measured | Only Gemma 3 12B measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Gemma 3 12B | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 34.9% | Not measured | Only Gemma 3 12B measured |
| Humanity's Last Exam (Artificial Analysis run) | 4.2% | Not measured | Only Gemma 3 12B measured |
| GPQA Diamond (Epoch AI run) | 39.5% | 35.5% | Gemma 3 12B |
| LMArena Text: hard prompts | 1309 | Not measured | Only Gemma 3 12B measured |
| LMArena Text: expert | 1248 | Not measured | Only Gemma 3 12B measured |
| LMArena Text: hard prompts in English | 1318 | Not measured | Only Gemma 3 12B measured |
| Neither model has a result on the other 16 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Gemma 3 12B | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| AA Math Index | 18.3 | Not measured | Only Gemma 3 12B measured |
| AIME 2025 (Artificial Analysis run) | 18.3% | Not measured | Only Gemma 3 12B measured |
| OTIS Mock AIME | 16.7% | 2.5% | Gemma 3 12B |
| LMArena Text: math | 1307 | Not measured | Only Gemma 3 12B measured |
| LMArena Text: mathematical work | 1336 | Not measured | 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 index6 boards, none shared
| Benchmark | Gemma 3 12B | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 59.5% | Not measured | Only Gemma 3 12B measured |
| MMLU | Not measured | 72.9% | Only Qwen2.5 7B Instruct measured |
| LMArena Text: science | 1366 | Not measured | Only Gemma 3 12B measured |
| LMArena Text: medicine and healthcare | 1334 | Not measured | Only Gemma 3 12B measured |
| LMArena Text: legal and government | 1381 | Not measured | Only Gemma 3 12B measured |
| LMArena Text: business and finance | 1376 | Not measured | Only Gemma 3 12B measured |
| Neither model has a result on the other 12 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemma 3 12B | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | Gemma 3 12B | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| IFBench | 36.7% | Not measured | Only Gemma 3 12B measured |
| Long context reasoning (AA-LCR) | 8.3% | Not measured | Only Gemma 3 12B measured |
| DTBench | 48.8% | 47.7% | Gemma 3 12B |
| LMCA | 4.5% | 6.4% | Qwen2.5 7B Instruct |
| LMArena Text | 1334 | Not measured | Only Gemma 3 12B measured |
| LMArena Text: instruction following | 1299 | Not measured | Only Gemma 3 12B measured |
| LMArena Text: creative writing | 1331 | Not measured | Only Gemma 3 12B measured |
| LMArena Text: English | 1349 | Not measured | Only Gemma 3 12B measured |
| LMArena Text: non-English | 1318 | Not measured | Only Gemma 3 12B measured |
| LMArena Text: German | 1370 | Not measured | Only Gemma 3 12B measured |
| LMArena Text: Russian | 1335 | Not measured | Only Gemma 3 12B measured |
| LMArena Text: multi-turn | 1334 | Not measured | Only Gemma 3 12B measured |
| LMArena Text: longer queries | 1317 | Not measured | Only Gemma 3 12B measured |
| LMArena Text: writing and language | 1307 | Not measured | Only Gemma 3 12B measured |
| LMArena Text: entertainment and media | 1297 | Not measured | 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 | Gemma 3 12B | Qwen2.5 7B Instruct | 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
Gemma 3 12B costs less
Repository review
50K input + 3K output tokens
Gemma 3 12B costs less
Agent loop
200K input + 10K output tokens
Gemma 3 12B 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 | Google Gemma 3 12B | Qwen2.5 7B Instruct |
|---|---|---|
| Maker | Qwen | |
| Weights | Open weights | Open weights |
| Released | March 13, 2025 | October 16, 2024 |
| Context window | 131K | 33K |
| Input $/Mtok | $0.075 | $0.10 |
| Output $/Mtok | $0.1625 | $0.20 |
| 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 Gemma 3 12B better than Qwen2.5 7B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Gemma 3 12B for output tokens 1.2x cheaper ($0.1625 vs $0.20 per million) and a larger context window (131K against 33K tokens).
Which is cheaper, Gemma 3 12B or Qwen2.5 7B Instruct?
Gemma 3 12B costs $0.075 per million input tokens and $0.1625 per million output tokens; Qwen2.5 7B Instruct costs $0.10 and $0.20. Cost per solved task is known for neither of them.
Which has the bigger context window, Gemma 3 12B or Qwen2.5 7B Instruct?
Gemma 3 12B, with 131K tokens against 33K.
How many benchmarks is the Gemma 3 12B vs Qwen2.5 7B Instruct comparison based on?
5 benchmarks have a result for both models as of October 22, 2026. Gemma 3 12B's coding index rests on 0 independent benchmarks and Qwen2.5 7B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.