Gemma 3 12B vs Qwen2.5 VL 72B Instruct
Gemma 3 12B costs 6.2x less per output token than Qwen2.5 VL 72B Instruct ($0.1625 vs $1.00 per million). Updated October 23, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- 28.0
- Output $/M
- $0.1625
- Coding
- No data
- Overall
- No data
- Output $/M
- $1.00
Gemma 3 12B or Qwen2.5 VL 72B 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 6.2x cheaper ($0.1625 vs $1.00 per million) and a larger context window (131K against 128K tokens).
No benchmark has tested both yet. Gemma 3 12B has results on AA Coding Index, LiveCodeBench (Artificial Analysis run) and SciCode; Qwen2.5 VL 72B Instruct on OSWorld, Kagi LLM Benchmark and GeoBench. 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
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
Gemma 3 12B
Gemma 3 12B $0.0002, Qwen2.5 VL 72B Instruct $0.0013 per turn.
List prices
At a glance
- Coding index
- Gemma 3 12B not benchmarked, Qwen2.5 VL 72B Instruct not benchmarked.
- 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
- Gemma 3 12B $0.1625 against Qwen2.5 VL 72B Instruct $1.00 per million output tokens, 6.2x apart.
- Context window
- Gemma 3 12B takes 131K tokens, Qwen2.5 VL 72B Instruct 128K.
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 Index7 boards measured
| Benchmark | Gemma 3 12B | Qwen2.5 VL 72B 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 36 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Gemma 3 12B | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 3.8 | Not measured | Only Gemma 3 12B measured |
| Epoch Capabilities Index | 123.5 | Not measured | Only Gemma 3 12B measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | Gemma 3 12B | Qwen2.5 VL 72B 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 |
| OSWorld | Not measured | 5.0% | Only Qwen2.5 VL 72B Instruct measured |
| Berkeley Function Calling Leaderboard (BFCL) | 30.4% | Not measured | Only Gemma 3 12B measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | Gemma 3 12B | Qwen2.5 VL 72B 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% | Not measured | Only Gemma 3 12B measured |
| 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 |
| Kagi LLM Benchmark | Not measured | 36.0% | Only Qwen2.5 VL 72B Instruct measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | Gemma 3 12B | Qwen2.5 VL 72B 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% | Not measured | Only Gemma 3 12B measured |
| 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 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | Gemma 3 12B | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 59.5% | Not measured | Only Gemma 3 12B measured |
| GeoBench | Not measured | 62.0% | Only Qwen2.5 VL 72B 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 14 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index18 boards measured
| Benchmark | Gemma 3 12B | Qwen2.5 VL 72B 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% | Not measured | Only Gemma 3 12B measured |
| LMCA | 4.5% | Not measured | Only Gemma 3 12B measured |
| 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 Vision | Not measured | 1107 | Only Qwen2.5 VL 72B Instruct measured |
| LMArena Text: English | 1348 | 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 |
| LMArena Vision: English | Not measured | 1115 | Only Qwen2.5 VL 72B Instruct measured |
| LMArena Vision: Chinese | Not measured | 1107 | Only Qwen2.5 VL 72B Instruct measured |
| Neither model has a result on the other 30 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 VL 72B Instruct |
|---|---|---|
| Maker | Qwen | |
| Weights | Open weights | Open weights |
| Released | March 13, 2025 | February 1, 2025 |
| Context window | 131K | 128K |
| Input $/Mtok | $0.075 | $0.80 |
| Output $/Mtok | $0.1625 | $1.00 |
| 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 VL 72B 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 6.2x cheaper ($0.1625 vs $1.00 per million) and a larger context window (131K against 128K tokens).
Which is cheaper, Gemma 3 12B or Qwen2.5 VL 72B Instruct?
Gemma 3 12B costs $0.075 per million input tokens and $0.1625 per million output tokens; Qwen2.5 VL 72B Instruct costs $0.80 and $1.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Gemma 3 12B or Qwen2.5 VL 72B Instruct?
Gemma 3 12B, with 131K tokens against 128K.
Has any benchmark tested both Gemma 3 12B and Qwen2.5 VL 72B Instruct?
No benchmark has tested both yet. Gemma 3 12B has results on AA Coding Index, LiveCodeBench (Artificial Analysis run) and SciCode; Qwen2.5 VL 72B Instruct on OSWorld, Kagi LLM Benchmark and GeoBench. Compare them on price and context instead. Data as of October 23, 2026.