Gemma 4 26B A4B vs Qwen3 235B A22B Instruct 2507
Gemma 4 26B A4B leads Qwen3 235B A22B Instruct 2507 on the Artificial Analysis Intelligence Index, 16.7 to 12.0, and Gemma 4 26B A4B costs 1.3x less per output token ($0.60 vs $0.775 per million). Updated October 22, 2026; 11 benchmarks have a result for both.
Gemma 4 26B A4B or Qwen3 235B A22B Instruct 2507: 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 1.3x cheaper ($0.60 vs $0.775 per million) and work measured by SciCode or WeirdML, where it leads.
- Shared results
- 11
- Gemma 4 26B A4B only
- 44
- Qwen3 235B A22B Instruct 2507 only
- 13
- Like-for-like categories
- 5 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 12.0.
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
Web design
Design Arena: websites
No call
Only Qwen3 235B A22B Instruct 2507 has a Design Arena: websites result, 1058.
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 235B A22B Instruct 2507 $0.0005 per turn.
List prices
At a glance
- Coding index
- Gemma 4 26B A4B not benchmarked, Qwen3 235B A22B Instruct 2507 not benchmarked.
- Widest category
- Agentic work, 9.6 points apart on average over 1 shared benchmark.
- Widest single gap
- IFBench: Gemma 4 26B A4B 72.4% against Qwen3 235B A22B Instruct 2507 51.2%, 21.2 points apart on a 0 to 100 scale.
- Output price
- Gemma 4 26B A4B $0.60 against Qwen3 235B A22B Instruct 2507 $0.775 per million output tokens, 1.3x 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 235B A22B Instruct 2507 | Ties |
|---|---|---|---|---|
| Coding | 1 | 2 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 2 | 0 | ||
| General | 2 | 2 | ||
| All shared boards | 6 | 5 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index12
| Benchmark | Gemma 4 26B A4B | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| 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 | 78.8% | Only Qwen3 235B A22B Instruct 2507 measured |
| SciCode | 40.0% | 41.4% | Qwen3 235B A22B Instruct 2507 |
| Terminal-Bench Hard (Artificial Analysis run) | 25.0% | 15.2% | Gemma 4 26B A4B |
| WeirdML | 35.2% | 38.7% | Qwen3 235B A22B Instruct 2507 |
| ALE-Bench | 927.2 | Not measured | Only Gemma 4 26B A4B measured |
| LMArena Text: coding | 1446 | Not measured | Only Gemma 4 26B A4B measured |
| LMArena Text: software and IT | 1454 | Not measured | Only Gemma 4 26B A4B measured |
| 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 32 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Gemma 4 26B A4B | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| AA Intelligence Index | 16.7 | 12.0 | Gemma 4 26B A4B |
| 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 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| AA Agentic Index | 2.9 | Not measured | Only Gemma 4 26B A4B measured |
| τ²-Bench Telecom | 43.6% | 53.2% | Qwen3 235B A22B Instruct 2507 |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 52.1% | Only Qwen3 235B A22B Instruct 2507 measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Gemma 4 26B A4B | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 79.2% | 79.0% | Gemma 4 26B A4B |
| Humanity's Last Exam (Artificial Analysis run) | 19.3% | 15.9% | Gemma 4 26B A4B |
| GPQA Diamond (Epoch AI run) | 73.2% | Not measured | Only Gemma 4 26B A4B measured |
| ARC-AGI | Not measured | 11.0% | Only Qwen3 235B A22B Instruct 2507 measured |
| ARC-AGI-2 | Not measured | 1.3% | Only Qwen3 235B A22B Instruct 2507 measured |
| LMArena Text: hard prompts | 1438 | Not measured | Only Gemma 4 26B A4B measured |
| LMArena Text: expert | 1447 | Not measured | Only Gemma 4 26B A4B measured |
| LMArena Text: hard prompts in English | 1448 | Not measured | Only Gemma 4 26B A4B measured |
| 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 13 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Gemma 4 26B A4B | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 91.0 | Only Qwen3 235B A22B Instruct 2507 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 91.0% | Only Qwen3 235B A22B Instruct 2507 measured |
| OTIS Mock AIME | 82.2% | Not measured | Only Gemma 4 26B A4B measured |
| LMArena Text: math | 1470 | Not measured | Only Gemma 4 26B A4B measured |
| LMArena Text: mathematical work | 1475 | Not measured | Only Gemma 4 26B A4B measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Gemma 4 26B A4B | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 84.3% | Only Qwen3 235B A22B Instruct 2507 measured |
| LMArena Text: science | 1446 | Not measured | Only Gemma 4 26B A4B measured |
| LMArena Text: medicine and healthcare | 1428 | Not measured | Only Gemma 4 26B A4B measured |
| LMArena Text: legal and government | 1457 | Not measured | Only Gemma 4 26B A4B measured |
| LMArena Text: business and finance | 1429 | Not measured | Only Gemma 4 26B A4B measured |
| FACTS Benchmark Suite | 42.7% | Not measured | Only Gemma 4 26B A4B measured |
| Neither model has a result on the other 14 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Gemma 4 26B A4B | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1058 | Only Qwen3 235B A22B Instruct 2507 measured |
| Design Arena: UI components | Not measured | 963 | Only Qwen3 235B A22B Instruct 2507 measured |
| Design Arena: game dev | Not measured | 951 | Only Qwen3 235B A22B Instruct 2507 measured |
| Design Arena: data viz | Not measured | 1070 | Only Qwen3 235B A22B Instruct 2507 measured |
| Design Arena: 3D | Not measured | 994 | Only Qwen3 235B A22B Instruct 2507 measured |
| Design Arena: all code categories | Not measured | 1038 | Only Qwen3 235B A22B Instruct 2507 measured |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index22
| Benchmark | Gemma 4 26B A4B | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| IFBench | 72.4% | 51.2% | Gemma 4 26B A4B |
| Long context reasoning (AA-LCR) | 65.7% | 72.0% | Qwen3 235B A22B Instruct 2507 |
| DTBench | 74.9% | 78.4% | Qwen3 235B A22B Instruct 2507 |
| LMCA | 29.7% | 23.5% | Gemma 4 26B A4B |
| LMArena Text | 1434 | Not measured | Only Gemma 4 26B A4B measured |
| LMArena Text: instruction following | 1420 | Not measured | Only Gemma 4 26B A4B measured |
| LMArena Text: creative writing | 1400 | Not measured | Only Gemma 4 26B A4B measured |
| LMArena Vision | 1260 | Not measured | Only Gemma 4 26B A4B measured |
| LMArena Text: English | 1444 | Not measured | Only Gemma 4 26B A4B measured |
| LMArena Text: non-English | 1421 | Not measured | Only Gemma 4 26B A4B measured |
| LMArena Text: Chinese | 1495 | Not measured | Only Gemma 4 26B A4B measured |
| 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 | Not measured | Only Gemma 4 26B A4B measured |
| LMArena Text: multi-turn | 1441 | Not measured | Only Gemma 4 26B A4B measured |
| LMArena Text: longer queries | 1427 | Not measured | Only Gemma 4 26B A4B measured |
| LMArena Text: writing and language | 1405 | Not measured | Only Gemma 4 26B A4B measured |
| LMArena Text: entertainment and media | 1403 | Not measured | Only Gemma 4 26B A4B measured |
| 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 23 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemma 4 26B A4B | Qwen3 235B A22B Instruct 2507 | 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 235B A22B Instruct 2507 |
|---|---|---|
| Maker | Qwen | |
| Weights | Open weights | Open weights |
| Released | April 3, 2026 | July 21, 2025 |
| Context window | 262K | 262K |
| Input $/Mtok | $0.15 | $0.145 |
| Output $/Mtok | $0.60 | $0.775 |
| 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 Qwen3 235B A22B Instruct 2507 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 1.3x cheaper ($0.60 vs $0.775 per million) and work measured by SciCode or WeirdML, where it leads.
Which is cheaper, Gemma 4 26B A4B or Qwen3 235B A22B Instruct 2507?
Gemma 4 26B A4B costs $0.15 per million input tokens and $0.60 per million output tokens; Qwen3 235B A22B Instruct 2507 costs $0.145 and $0.775. Cost per solved task is known for neither of them.
Which has the bigger context window, Gemma 4 26B A4B or Qwen3 235B A22B Instruct 2507?
Neither: both take 262K tokens.
How many benchmarks is the Gemma 4 26B A4B vs Qwen3 235B A22B Instruct 2507 comparison based on?
11 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 235B A22B Instruct 2507's on 0. Where only one model has a result, the page says so instead of naming a winner.