Gemma 3 27B vs Qwen2.5 Coder 32B Instruct
Qwen2.5 Coder 32B Instruct leads Gemma 3 27B on the Artificial Analysis Intelligence Index, 6.7 to 4.9, and Gemma 3 27B costs 3.3x less per output token ($0.30 vs $1.00 per million). Updated October 22, 2026; 29 benchmarks have a result for both.
Gemma 3 27B or Qwen2.5 Coder 32B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Gemma 3 27B for output tokens 3.3x cheaper ($0.30 vs $1.00 per million) and a larger context window (131K against 33K tokens).
- Shared results
- 29
- Gemma 3 27B only
- 37
- Qwen2.5 Coder 32B Instruct only
- 6
- Like-for-like categories
- 6 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
Qwen2.5 Coder 32B Instruct
Qwen2.5 Coder 32B Instruct leads, 6.7 against 4.9.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Gemma 3 27B has a AA Agentic Index result, 0.1.
Directional only
Long documents
Listed context window
Gemma 3 27B
Gemma 3 27B takes 131K tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Gemma 3 27B
Gemma 3 27B $0.0002, Qwen2.5 Coder 32B Instruct $0.0012 per turn.
List prices
At a glance
- Coding index
- Gemma 3 27B not benchmarked, Qwen2.5 Coder 32B Instruct not benchmarked.
- Widest category
- General, 21.7 points apart on average over 11 shared benchmarks.
- Widest single gap
- LMArena Text: medicine and healthcare: Gemma 3 27B 1391 against Qwen2.5 Coder 32B Instruct 1187, 34.1 points apart on a 0 to 100 scale.
- Output price
- Gemma 3 27B $0.30 against Qwen2.5 Coder 32B Instruct $1.00 per million output tokens, 3.3x apart.
- Context window
- Gemma 3 27B takes 131K tokens, Qwen2.5 Coder 32B 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 27B | Bars | Qwen2.5 Coder 32B Instruct | Ties |
|---|---|---|---|---|
| Coding | 2 | 2 | ||
| Intelligence | 1 | 1 | ||
| Reasoning | 5 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 5 | 0 | ||
| General | 11 | 0 | ||
| All shared boards | 26 | 3 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index22
| Benchmark | Gemma 3 27B | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| Aider polyglot | 4.9% | 16.4% | Qwen2.5 Coder 32B Instruct |
| SWE-bench Verified | Not measured | 47.0% | Only Qwen2.5 Coder 32B Instruct measured |
| AA Coding Index | 10.1 | Not measured | Only Gemma 3 27B measured |
| LiveCodeBench (Artificial Analysis run) | 13.7% | 29.5% | Qwen2.5 Coder 32B Instruct |
| SciCode | 23.3% | Not measured | Only Gemma 3 27B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 3.8% | Not measured | Only Gemma 3 27B measured |
| LMArena Text: coding | 1322 | 1276 | Gemma 3 27B |
| LMArena Text: software and IT | 1354 | 1261 | Gemma 3 27B |
| SciCode | 0.0% | Not measured | Only Gemma 3 27B measured |
| Neither model has a result on the other 30 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | Gemma 3 27B | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 4.9 | 6.7 | Qwen2.5 Coder 32B Instruct |
| Epoch Capabilities Index | 130.0 | 119.5 | Gemma 3 27B |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | Gemma 3 27B | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| AA Agentic Index | 0.1 | Not measured | Only Gemma 3 27B measured |
| τ²-Bench Telecom | 10.5% | Not measured | Only Gemma 3 27B measured |
| Berkeley Function Calling Leaderboard (BFCL) | 29.5% | Not measured | Only Gemma 3 27B measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | Gemma 3 27B | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 42.8% | 41.7% | Gemma 3 27B |
| Humanity's Last Exam (Artificial Analysis run) | 4.4% | 3.5% | Gemma 3 27B |
| GPQA Diamond (Epoch AI run) | 47.7% | Not measured | Only Gemma 3 27B measured |
| LMArena Text: hard prompts | 1340 | 1251 | Gemma 3 27B |
| LMArena Text: expert | 1304 | 1221 | Gemma 3 27B |
| LMArena Text: hard prompts in English | 1353 | 1269 | Gemma 3 27B |
| LMArena Vision: diagrams | 1156 | Not measured | Only Gemma 3 27B measured |
| LMArena Vision: homework | 1160 | Not measured | Only Gemma 3 27B measured |
| Kagi LLM Benchmark | 35.1% | Not measured | Only Gemma 3 27B measured |
| Neither model has a result on the other 13 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Gemma 3 27B | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| AA Math Index | 20.7 | Not measured | Only Gemma 3 27B measured |
| AIME 2025 (Artificial Analysis run) | 20.7% | Not measured | Only Gemma 3 27B measured |
| OTIS Mock AIME | 22.5% | Not measured | Only Gemma 3 27B measured |
| MATH level 5 | 74.0% | Not measured | Only Gemma 3 27B measured |
| GSM8K | Not measured | 93.0% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: math | 1312 | 1251 | Gemma 3 27B |
| LMArena Text: mathematical work | 1327 | 1243 | Gemma 3 27B |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | Gemma 3 27B | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 66.9% | 63.5% | Gemma 3 27B |
| MMLU | Not measured | 79.1% | Only Qwen2.5 Coder 32B Instruct measured |
| GeoBench | 52.0% | Not measured | Only Gemma 3 27B measured |
| AI2 Reasoning Challenge | Not measured | 70.5% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: science | 1383 | 1221 | Gemma 3 27B |
| LMArena Text: medicine and healthcare | 1391 | 1187 | Gemma 3 27B |
| LMArena Text: legal and government | 1375 | 1257 | Gemma 3 27B |
| LMArena Text: business and finance | 1360 | 1224 | Gemma 3 27B |
| FACTS Benchmark Suite | 29.1% | Not measured | Only Gemma 3 27B measured |
| Neither model has a result on the other 9 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemma 3 27B | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index110
| Benchmark | Gemma 3 27B | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| IFBench | 31.8% | Not measured | Only Gemma 3 27B measured |
| Long context reasoning (AA-LCR) | 7.3% | Not measured | Only Gemma 3 27B measured |
| DTBench | 52.5% | Not measured | Only Gemma 3 27B measured |
| LMCA | 12.3% | Not measured | Only Gemma 3 27B measured |
| Fiction.LiveBench | 33.3% | Not measured | Only Gemma 3 27B measured |
| Creative writing (Lech Mazur) | 79.9% | Not measured | Only Gemma 3 27B measured |
| HellaSwag | Not measured | 83.0% | Only Qwen2.5 Coder 32B Instruct measured |
| WinoGrande | Not measured | 80.8% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text | 1358 | 1230 | Gemma 3 27B |
| LMArena Text: instruction following | 1321 | 1223 | Gemma 3 27B |
| LMArena Text: creative writing | 1345 | 1174 | Gemma 3 27B |
| LMArena Vision | 1165 | Not measured | Only Gemma 3 27B measured |
| LMArena Text: English | 1377 | 1248 | Gemma 3 27B |
| LMArena Text: non-English | 1334 | 1205 | Gemma 3 27B |
| LMArena Text: Chinese | 1346 | 1222 | Gemma 3 27B |
| LMArena Text: German | 1362 | Not measured | Only Gemma 3 27B measured |
| LMArena Text: Japanese | 1287 | Not measured | Only Gemma 3 27B measured |
| LMArena Text: Korean | 1308 | Not measured | Only Gemma 3 27B measured |
| LMArena Text: French | 1368 | Not measured | Only Gemma 3 27B measured |
| LMArena Text: Spanish | 1349 | Not measured | Only Gemma 3 27B measured |
| LMArena Text: Russian | 1349 | 1228 | Gemma 3 27B |
| LMArena Text: Polish | 1345 | Not measured | Only Gemma 3 27B measured |
| LMArena Text: multi-turn | 1345 | 1222 | Gemma 3 27B |
| LMArena Text: longer queries | 1333 | 1251 | Gemma 3 27B |
| LMArena Text: writing and language | 1347 | 1191 | Gemma 3 27B |
| LMArena Text: entertainment and media | 1320 | 1150 | Gemma 3 27B |
| LMArena Vision: English | 1188 | Not measured | Only Gemma 3 27B measured |
| LMArena Vision: Chinese | 1164 | Not measured | Only Gemma 3 27B measured |
| LMArena Vision: reading text | 1156 | Not measured | Only Gemma 3 27B measured |
| LMArena Vision: humor | 1166 | Not measured | Only Gemma 3 27B measured |
| LMArena Vision: captioning | 1151 | Not measured | Only Gemma 3 27B measured |
| LMArena Vision: recognizing things | 1196 | Not measured | Only Gemma 3 27B measured |
| LMArena Vision: creative writing | 1177 | Not measured | Only Gemma 3 27B measured |
| Neither model has a result on the other 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemma 3 27B | Qwen2.5 Coder 32B 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 27B costs less
Repository review
50K input + 3K output tokens
Gemma 3 27B costs less
Agent loop
200K input + 10K output tokens
Gemma 3 27B 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 27B | Qwen2.5 Coder 32B Instruct |
|---|---|---|
| Maker | Qwen | |
| Weights | Open weights | Open weights |
| Released | March 12, 2025 | November 11, 2024 |
| Context window | 131K | 33K |
| Input $/Mtok | $0.08 | $0.66 |
| Output $/Mtok | $0.30 | $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 27B better than Qwen2.5 Coder 32B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Gemma 3 27B for output tokens 3.3x cheaper ($0.30 vs $1.00 per million) and a larger context window (131K against 33K tokens).
Which is cheaper, Gemma 3 27B or Qwen2.5 Coder 32B Instruct?
Gemma 3 27B costs $0.08 per million input tokens and $0.30 per million output tokens; Qwen2.5 Coder 32B Instruct costs $0.66 and $1.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Gemma 3 27B or Qwen2.5 Coder 32B Instruct?
Gemma 3 27B, with 131K tokens against 33K.
How many benchmarks is the Gemma 3 27B vs Qwen2.5 Coder 32B Instruct comparison based on?
29 benchmarks have a result for both models as of October 22, 2026. Gemma 3 27B's coding index rests on 0 independent benchmarks and Qwen2.5 Coder 32B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.