Gemma 4 26B A4B vs Qwen3.5-27B
Qwen3.5-27B leads Gemma 4 26B A4B on the Artificial Analysis Intelligence Index, 22.9 to 16.7, and Gemma 4 26B A4B costs 2.6x less per output token ($0.60 vs $1.56 per million). Updated October 22, 2026; 48 benchmarks have a result for both.
Gemma 4 26B A4B or Qwen3.5-27B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Gemma 4 26B A4B for output tokens 2.6x cheaper ($0.60 vs $1.56 per million) and work measured by Terminal-Bench Hard (Artificial Analysis run) or WeirdML, where it leads.
- Shared results
- 48
- Gemma 4 26B A4B only
- 7
- Qwen3.5-27B only
- 6
- Like-for-like categories
- 7 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
Qwen3.5-27B
Qwen3.5-27B leads, 22.9 against 16.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Gemma 4 26B A4B has a AA Agentic Index result, 2.9.
Directional only
Long documents
Listed context window
No call
Both take 262K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Gemma 4 26B A4B
Gemma 4 26B A4B $0.0004, Qwen3.5-27B $0.0010 per turn.
List prices
At a glance
- Coding index
- Gemma 4 26B A4B not benchmarked, Qwen3.5-27B not benchmarked.
- Widest category
- Agentic work, 50.3 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: Gemma 4 26B A4B 43.6% against Qwen3.5-27B 93.9%, 50.3 points apart on a 0 to 100 scale.
- Output price
- Gemma 4 26B A4B $0.60 against Qwen3.5-27B $1.56 per million output tokens, 2.6x apart.
- Context window
- Both take 262K tokens.
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 4 26B A4B | Bars | Qwen3.5-27B | Ties |
|---|---|---|---|---|
| Coding | 5 | 3 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 5 | 2 | ||
| Math | 2 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 20 | 5 | ||
| All shared boards | 36 | 12 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index53
| Benchmark | Gemma 4 26B A4B | Qwen3.5-27B | Leader |
|---|---|---|---|
| LMArena WebDev | 1360 | 1358 | Gemma 4 26B A4B |
| AA Coding Index | 39.3 | Not measured | Only Gemma 4 26B A4B measured |
| SciCode | 40.0% | Not measured | Only Gemma 4 26B A4B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 25.0% | 32.6% | Qwen3.5-27B |
| WeirdML | 35.2% | 39.5% | Qwen3.5-27B |
| ALE-Bench | 927.2 | 349.4 | Gemma 4 26B A4B |
| LMArena Text: coding | 1446 | 1427 | Gemma 4 26B A4B |
| LMArena Text: software and IT | 1454 | 1431 | Gemma 4 26B A4B |
| LMArena WebDev: React | 1351 | 1346 | Gemma 4 26B A4B |
| LMArena WebDev: HTML | 1362 | 1386 | Qwen3.5-27B |
| Neither model has a result on the other 33 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Gemma 4 26B A4B | Qwen3.5-27B | Leader |
|---|---|---|---|
| AA Intelligence Index | 16.7 | 22.9 | Qwen3.5-27B |
| Epoch Capabilities Index | 141.9 | Not measured | Only Gemma 4 26B A4B measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Gemma 4 26B A4B | Qwen3.5-27B | Leader |
|---|---|---|---|
| AA Agentic Index | 2.9 | Not measured | Only Gemma 4 26B A4B measured |
| τ²-Bench Telecom | 43.6% | 93.9% | Qwen3.5-27B |
| Vending-Bench 2 | Not measured | $202 | Only Qwen3.5-27B measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index52
| Benchmark | Gemma 4 26B A4B | Qwen3.5-27B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 79.2% | 85.8% | Qwen3.5-27B |
| Humanity's Last Exam (Artificial Analysis run) | 19.3% | 23.9% | Qwen3.5-27B |
| GPQA Diamond (Epoch AI run) | 73.2% | Not measured | Only Gemma 4 26B A4B measured |
| LMArena Text: hard prompts | 1438 | 1415 | Gemma 4 26B A4B |
| LMArena Text: expert | 1447 | 1427 | Gemma 4 26B A4B |
| LMArena Text: hard prompts in English | 1448 | 1429 | Gemma 4 26B A4B |
| LMArena Vision: diagrams | 1264 | 1249 | Gemma 4 26B A4B |
| LMArena Vision: homework | 1293 | 1256 | Gemma 4 26B A4B |
| Neither model has a result on the other 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Gemma 4 26B A4B | Qwen3.5-27B | Leader |
|---|---|---|---|
| OTIS Mock AIME | 82.2% | Not measured | Only Gemma 4 26B A4B measured |
| LMArena Text: math | 1470 | 1429 | Gemma 4 26B A4B |
| LMArena Text: mathematical work | 1475 | 1429 | Gemma 4 26B A4B |
| MathArena | Not measured | 15.8% | Only Qwen3.5-27B measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | Gemma 4 26B A4B | Qwen3.5-27B | Leader |
|---|---|---|---|
| LMArena Text: science | 1446 | 1437 | Gemma 4 26B A4B |
| LMArena Text: medicine and healthcare | 1428 | 1425 | Gemma 4 26B A4B |
| LMArena Text: legal and government | 1457 | 1405 | Gemma 4 26B A4B |
| LMArena Text: business and finance | 1429 | 1399 | Gemma 4 26B A4B |
| FACTS Benchmark Suite | 42.7% | Not measured | Only Gemma 4 26B A4B measured |
| Neither model has a result on the other 15 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemma 4 26B A4B | Qwen3.5-27B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index205
| Benchmark | Gemma 4 26B A4B | Qwen3.5-27B | Leader |
|---|---|---|---|
| IFBench | 72.4% | 75.6% | Qwen3.5-27B |
| Long context reasoning (AA-LCR) | 65.7% | 77.7% | Qwen3.5-27B |
| DTBench | 74.9% | 82.4% | Qwen3.5-27B |
| LMCA | 29.7% | 34.0% | Qwen3.5-27B |
| LMArena Text | 1434 | 1409 | Gemma 4 26B A4B |
| LMArena Text: instruction following | 1420 | 1393 | Gemma 4 26B A4B |
| LMArena Text: creative writing | 1400 | 1362 | Gemma 4 26B A4B |
| LMArena Vision | 1260 | 1241 | Gemma 4 26B A4B |
| LMArena Text: English | 1444 | 1425 | Gemma 4 26B A4B |
| LMArena Text: non-English | 1421 | 1390 | Gemma 4 26B A4B |
| LMArena Text: Chinese | 1495 | 1478 | Gemma 4 26B A4B |
| LMArena Text: German | Not measured | 1392 | Only Qwen3.5-27B measured |
| LMArena Text: Japanese | Not measured | 1346 | Only Qwen3.5-27B measured |
| LMArena Text: Korean | Not measured | 1359 | Only Qwen3.5-27B measured |
| LMArena Text: French | 1460 | 1410 | Gemma 4 26B A4B |
| LMArena Text: Spanish | 1415 | 1407 | Gemma 4 26B A4B |
| LMArena Text: Russian | 1434 | 1390 | Gemma 4 26B A4B |
| LMArena Text: Polish | Not measured | 1375 | Only Qwen3.5-27B measured |
| LMArena Text: multi-turn | 1441 | 1410 | Gemma 4 26B A4B |
| LMArena Text: longer queries | 1427 | 1413 | Gemma 4 26B A4B |
| LMArena Text: writing and language | 1405 | 1380 | Gemma 4 26B A4B |
| LMArena Text: entertainment and media | 1403 | 1363 | Gemma 4 26B A4B |
| LMArena Vision: English | 1256 | 1246 | Gemma 4 26B A4B |
| LMArena Vision: Chinese | 1312 | 1301 | Gemma 4 26B A4B |
| LMArena Vision: reading text | 1268 | 1246 | Gemma 4 26B A4B |
| LMArena Vision: humor | 1272 | 1227 | Gemma 4 26B A4B |
| LMArena Vision: captioning | 1297 | 1170 | Gemma 4 26B A4B |
| LMArena Vision: recognizing things | 1191 | 1210 | Qwen3.5-27B |
| LMArena Vision: creative writing | 1256 | 1224 | Gemma 4 26B A4B |
| Neither model has a result on the other 19 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemma 4 26B A4B | Qwen3.5-27B | 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 4 26B A4B costs less
Repository review
50K input + 3K output tokens
Gemma 4 26B A4B costs less
Agent loop
200K input + 10K output tokens
Gemma 4 26B A4B 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 4 26B A4B | Qwen3.5-27B |
|---|---|---|
| Maker | Qwen | |
| Weights | Open weights | Open weights |
| Released | April 3, 2026 | February 25, 2026 |
| Context window | 262K | 262K |
| Input $/Mtok | $0.15 | $0.195 |
| Output $/Mtok | $0.60 | $1.56 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Gemma 4 26B A4B better than Qwen3.5-27B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Gemma 4 26B A4B for output tokens 2.6x cheaper ($0.60 vs $1.56 per million) and work measured by Terminal-Bench Hard (Artificial Analysis run) or WeirdML, where it leads.
Which is cheaper, Gemma 4 26B A4B or Qwen3.5-27B?
Gemma 4 26B A4B costs $0.15 per million input tokens and $0.60 per million output tokens; Qwen3.5-27B costs $0.195 and $1.56. Cost per solved task is known for neither of them.
Which has the bigger context window, Gemma 4 26B A4B or Qwen3.5-27B?
Neither: both take 262K tokens.
How many benchmarks is the Gemma 4 26B A4B vs Qwen3.5-27B comparison based on?
48 benchmarks have a result for both models as of October 22, 2026. Gemma 4 26B A4B's coding index rests on 1 independent benchmark and Qwen3.5-27B's on 1. Where only one model has a result, the page says so instead of naming a winner.