Gemma 4 26B A4B vs Qwen2.5 Coder 32B Instruct
Gemma 4 26B A4B leads Qwen2.5 Coder 32B Instruct on the Artificial Analysis Intelligence Index, 16.7 to 6.7, and Gemma 4 26B A4B costs 1.7x less per output token ($0.60 vs $1.00 per million). Updated October 22, 2026; 26 benchmarks have a result for both.
Gemma 4 26B A4B 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 4 26B A4B for output tokens 1.7x cheaper ($0.60 vs $1.00 per million) and a larger context window (262K against 33K tokens).
- Shared results
- 26
- Gemma 4 26B A4B only
- 29
- Qwen2.5 Coder 32B Instruct only
- 9
- 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
Gemma 4 26B A4B
Gemma 4 26B A4B leads, 16.7 against 6.7.
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
Gemma 4 26B A4B
Gemma 4 26B A4B takes 262K tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Gemma 4 26B A4B
Gemma 4 26B A4B $0.0004, Qwen2.5 Coder 32B Instruct $0.0012 per turn.
List prices
At a glance
- Coding index
- Gemma 4 26B A4B not benchmarked, Qwen2.5 Coder 32B Instruct not benchmarked.
- Widest category
- Math, 37.6 points apart on average over 2 shared benchmarks.
- Widest single gap
- LMArena Text: Chinese: Gemma 4 26B A4B 1495 against Qwen2.5 Coder 32B Instruct 1222, 45.6 points apart on a 0 to 100 scale.
- Output price
- Gemma 4 26B A4B $0.60 against Qwen2.5 Coder 32B Instruct $1.00 per million output tokens, 1.7x apart.
- Context window
- Gemma 4 26B A4B takes 262K 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 4 26B A4B | Bars | Qwen2.5 Coder 32B Instruct | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Intelligence | 2 | 0 | ||
| Reasoning | 5 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 11 | 0 | ||
| All shared boards | 26 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | Gemma 4 26B A4B | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 16.4% | Only Qwen2.5 Coder 32B Instruct measured |
| SWE-bench Verified | Not measured | 47.0% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena WebDev | 1360 | Not measured | Only Gemma 4 26B A4B measured |
| AA Coding Index | 39.3 | Not measured | Only Gemma 4 26B A4B measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 29.5% | Only Qwen2.5 Coder 32B Instruct measured |
| SciCode | 40.0% | Not measured | Only Gemma 4 26B A4B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 25.0% | Not measured | Only Gemma 4 26B A4B measured |
| WeirdML | 35.2% | Not measured | Only Gemma 4 26B A4B measured |
| ALE-Bench | 927.2 | Not measured | Only Gemma 4 26B A4B measured |
| LMArena Text: coding | 1446 | 1276 | Gemma 4 26B A4B |
| LMArena Text: software and IT | 1454 | 1261 | Gemma 4 26B A4B |
| LMArena WebDev: React | 1351 | Not measured | Only Gemma 4 26B A4B measured |
| LMArena WebDev: HTML | 1362 | Not measured | Only Gemma 4 26B A4B measured |
| Neither model has a result on the other 26 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Gemma 4 26B A4B | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 16.7 | 6.7 | Gemma 4 26B A4B |
| Epoch Capabilities Index | 141.9 | 119.5 | Gemma 4 26B A4B |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Gemma 4 26B A4B | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| AA Agentic Index | 2.9 | Not measured | Only Gemma 4 26B A4B measured |
| τ²-Bench Telecom | 43.6% | Not measured | Only Gemma 4 26B A4B measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | Gemma 4 26B A4B | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 79.2% | 41.7% | Gemma 4 26B A4B |
| Humanity's Last Exam (Artificial Analysis run) | 19.3% | 3.5% | Gemma 4 26B A4B |
| GPQA Diamond (Epoch AI run) | 73.2% | Not measured | Only Gemma 4 26B A4B measured |
| LMArena Text: hard prompts | 1438 | 1251 | Gemma 4 26B A4B |
| LMArena Text: expert | 1447 | 1221 | Gemma 4 26B A4B |
| LMArena Text: hard prompts in English | 1448 | 1269 | Gemma 4 26B A4B |
| LMArena Vision: diagrams | 1264 | Not measured | Only Gemma 4 26B A4B measured |
| LMArena Vision: homework | 1293 | Not measured | Only Gemma 4 26B A4B measured |
| Neither model has a result on the other 14 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Gemma 4 26B A4B | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| OTIS Mock AIME | 82.2% | Not measured | Only Gemma 4 26B A4B measured |
| GSM8K | Not measured | 93.0% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: math | 1470 | 1251 | Gemma 4 26B A4B |
| LMArena Text: mathematical work | 1475 | 1243 | Gemma 4 26B A4B |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | Gemma 4 26B A4B | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 63.5% | Only Qwen2.5 Coder 32B Instruct measured |
| MMLU | Not measured | 79.1% | Only Qwen2.5 Coder 32B Instruct measured |
| AI2 Reasoning Challenge | Not measured | 70.5% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: science | 1446 | 1221 | Gemma 4 26B A4B |
| LMArena Text: medicine and healthcare | 1428 | 1187 | Gemma 4 26B A4B |
| LMArena Text: legal and government | 1457 | 1257 | Gemma 4 26B A4B |
| LMArena Text: business and finance | 1429 | 1224 | 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 10 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemma 4 26B A4B | 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 4 26B A4B | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| IFBench | 72.4% | Not measured | Only Gemma 4 26B A4B measured |
| Long context reasoning (AA-LCR) | 65.7% | Not measured | Only Gemma 4 26B A4B measured |
| DTBench | 74.9% | Not measured | Only Gemma 4 26B A4B measured |
| LMCA | 29.7% | Not measured | Only Gemma 4 26B A4B 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 | 1434 | 1230 | Gemma 4 26B A4B |
| LMArena Text: instruction following | 1420 | 1223 | Gemma 4 26B A4B |
| LMArena Text: creative writing | 1400 | 1174 | Gemma 4 26B A4B |
| LMArena Vision | 1260 | Not measured | Only Gemma 4 26B A4B measured |
| LMArena Text: English | 1444 | 1248 | Gemma 4 26B A4B |
| LMArena Text: non-English | 1421 | 1205 | Gemma 4 26B A4B |
| LMArena Text: Chinese | 1495 | 1222 | Gemma 4 26B A4B |
| LMArena Text: French | 1460 | Not measured | Only Gemma 4 26B A4B measured |
| LMArena Text: Spanish | 1415 | Not measured | Only Gemma 4 26B A4B measured |
| LMArena Text: Russian | 1434 | 1228 | Gemma 4 26B A4B |
| LMArena Text: multi-turn | 1441 | 1222 | Gemma 4 26B A4B |
| LMArena Text: longer queries | 1427 | 1251 | Gemma 4 26B A4B |
| LMArena Text: writing and language | 1405 | 1191 | Gemma 4 26B A4B |
| LMArena Text: entertainment and media | 1403 | 1150 | Gemma 4 26B A4B |
| LMArena Vision: English | 1256 | Not measured | Only Gemma 4 26B A4B measured |
| LMArena Vision: Chinese | 1312 | Not measured | Only Gemma 4 26B A4B measured |
| LMArena Vision: reading text | 1268 | Not measured | Only Gemma 4 26B A4B measured |
| LMArena Vision: humor | 1272 | Not measured | Only Gemma 4 26B A4B measured |
| LMArena Vision: captioning | 1297 | Not measured | Only Gemma 4 26B A4B measured |
| LMArena Vision: recognizing things | 1191 | Not measured | Only Gemma 4 26B A4B measured |
| LMArena Vision: creative writing | 1256 | Not measured | Only Gemma 4 26B A4B measured |
| 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 | 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 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 | Qwen2.5 Coder 32B Instruct |
|---|---|---|
| Maker | Qwen | |
| Weights | Open weights | Open weights |
| Released | April 3, 2026 | November 11, 2024 |
| Context window | 262K | 33K |
| Input $/Mtok | $0.15 | $0.66 |
| Output $/Mtok | $0.60 | $1.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Gemma 4 26B A4B 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 4 26B A4B for output tokens 1.7x cheaper ($0.60 vs $1.00 per million) and a larger context window (262K against 33K tokens).
Which is cheaper, Gemma 4 26B A4B or Qwen2.5 Coder 32B Instruct?
Gemma 4 26B A4B costs $0.15 per million input tokens and $0.60 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 4 26B A4B or Qwen2.5 Coder 32B Instruct?
Gemma 4 26B A4B, with 262K tokens against 33K.
How many benchmarks is the Gemma 4 26B A4B vs Qwen2.5 Coder 32B Instruct comparison based on?
26 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 Qwen2.5 Coder 32B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.