Gemma 4 26B A4B vs gpt-oss-20b
Gemma 4 26B A4B leads gpt-oss-20b on the Artificial Analysis Intelligence Index, 16.7 to 10.0, and gpt-oss-20b costs 4.0x less per output token ($0.15 vs $0.60 per million). Updated October 22, 2026; 38 benchmarks have a result for both.
Gemma 4 26B A4B or gpt-oss-20b: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Gemma 4 26B A4B for a larger context window (262K against 131K tokens) and work measured by WeirdML or τ²-Bench Telecom, where it leads.
- Shared results
- 38
- Gemma 4 26B A4B only
- 17
- gpt-oss-20b only
- 24
- 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
Gemma 4 26B A4B
Gemma 4 26B A4B leads, 16.7 against 10.0.
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
Reasoning
GPQA Diamond
No call
Only gpt-oss-20b has a GPQA Diamond result, 68.9%.
Directional only
Math
AIME
No call
Only gpt-oss-20b has a AIME result, 86.0%.
Directional only
Knowledge
MMLU Pro
No call
Only gpt-oss-20b has a MMLU Pro result, 71.6%.
Directional only
Web design
Design Arena: websites
No call
Only gpt-oss-20b has a Design Arena: websites result, 852.
Directional only
Long documents
Listed context window
Gemma 4 26B A4B
Gemma 4 26B A4B takes 262K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
gpt-oss-20b
Gemma 4 26B A4B $0.0004, gpt-oss-20b $0.0001 per turn.
List prices
At a glance
- Coding index
- Gemma 4 26B A4B not benchmarked, gpt-oss-20b not benchmarked.
- Widest category
- General, 24.7 points apart on average over 16 shared benchmarks.
- Widest single gap
- LMArena Text: creative writing: Gemma 4 26B A4B 1400 against gpt-oss-20b 1201, 33.2 points apart on a 0 to 100 scale.
- Output price
- Gemma 4 26B A4B $0.60 against gpt-oss-20b $0.15 per million output tokens, 4.0x apart.
- Context window
- Gemma 4 26B A4B takes 262K tokens, gpt-oss-20b 131K.
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 | gpt-oss-20b | Ties |
|---|---|---|---|---|
| Coding | 5 | 1 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 6 | 0 | ||
| Math | 3 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 16 | 0 | ||
| All shared boards | 36 | 2 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index51
| Benchmark | Gemma 4 26B A4B | gpt-oss-20b | 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 (Vals AI) | Not measured | 80.4% | Only gpt-oss-20b measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 77.7% | Only gpt-oss-20b measured |
| SciCode | 40.0% | 38.9% | Gemma 4 26B A4B |
| Terminal-Bench Hard (Artificial Analysis run) | 25.0% | 10.6% | Gemma 4 26B A4B |
| Terminal-Bench | Not measured | 3.4% | Only gpt-oss-20b measured |
| WeirdML | 35.2% | 40.9% | gpt-oss-20b |
| ALE-Bench | 927.2 | 566.0 | Gemma 4 26B A4B |
| LMArena Text: coding | 1446 | 1307 | Gemma 4 26B A4B |
| LMArena Text: software and IT | 1454 | 1311 | 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 | gpt-oss-20b | Leader |
|---|---|---|---|
| AA Intelligence Index | 16.7 | 10.0 | Gemma 4 26B A4B |
| Epoch Capabilities Index | 141.9 | 137.7 | Gemma 4 26B A4B |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Gemma 4 26B A4B | gpt-oss-20b | Leader |
|---|---|---|---|
| AA Agentic Index | 2.9 | Not measured | Only Gemma 4 26B A4B measured |
| τ²-Bench Telecom | 43.6% | 60.2% | gpt-oss-20b |
| BrowseComp-Plus | Not measured | 50.6% | Only gpt-oss-20b measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | Gemma 4 26B A4B | gpt-oss-20b | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 68.9% | Only gpt-oss-20b measured |
| GPQA Diamond (Artificial Analysis run) | 79.2% | 68.8% | Gemma 4 26B A4B |
| Humanity's Last Exam (Artificial Analysis run) | 19.3% | 11.0% | Gemma 4 26B A4B |
| GPQA Diamond (Epoch AI run) | 73.2% | 60.8% | Gemma 4 26B A4B |
| LMArena Text: hard prompts | 1438 | 1274 | Gemma 4 26B A4B |
| LMArena Text: expert | 1447 | 1258 | Gemma 4 26B A4B |
| LMArena Text: hard prompts in English | 1448 | 1292 | 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 |
| Kagi LLM Benchmark | Not measured | 53.2% | Only gpt-oss-20b measured |
| Neither model has a result on the other 12 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | Gemma 4 26B A4B | gpt-oss-20b | Leader |
|---|---|---|---|
| AIME | Not measured | 86.0% | Only gpt-oss-20b measured |
| MATH 500 (Vals AI) | Not measured | 94.2% | Only gpt-oss-20b measured |
| MGSM (Vals AI) | Not measured | 89.0% | Only gpt-oss-20b measured |
| AA Math Index | Not measured | 89.3 | Only gpt-oss-20b measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 89.3% | Only gpt-oss-20b measured |
| OTIS Mock AIME | 82.2% | 65.3% | Gemma 4 26B A4B |
| LMArena Text: math | 1470 | 1317 | Gemma 4 26B A4B |
| LMArena Text: mathematical work | 1475 | 1305 | Gemma 4 26B A4B |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | Gemma 4 26B A4B | gpt-oss-20b | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 71.6% | Only gpt-oss-20b measured |
| LegalBench (Vals AI) | Not measured | 70.8% | Only gpt-oss-20b measured |
| CaseLaw (Vals AI) | Not measured | 43.8% | Only gpt-oss-20b measured |
| CorpFin (Vals AI) | Not measured | 53.1% | Only gpt-oss-20b measured |
| TaxEval (Vals AI) | Not measured | 63.7% | Only gpt-oss-20b measured |
| MedQA (Vals AI) | Not measured | 82.9% | Only gpt-oss-20b measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 74.8% | Only gpt-oss-20b measured |
| LMArena Text: science | 1446 | 1296 | Gemma 4 26B A4B |
| LMArena Text: medicine and healthcare | 1428 | 1310 | Gemma 4 26B A4B |
| LMArena Text: legal and government | 1457 | 1280 | Gemma 4 26B A4B |
| LMArena Text: business and finance | 1429 | 1284 | 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 6 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Gemma 4 26B A4B | gpt-oss-20b | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 852 | Only gpt-oss-20b measured |
| Design Arena: data viz | Not measured | 933 | Only gpt-oss-20b measured |
| Neither model has a result on the other 7 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index160
| Benchmark | Gemma 4 26B A4B | gpt-oss-20b | Leader |
|---|---|---|---|
| IFBench | 72.4% | 65.1% | Gemma 4 26B A4B |
| Long context reasoning (AA-LCR) | 65.7% | 34.7% | Gemma 4 26B A4B |
| DTBench | 74.9% | 68.0% | Gemma 4 26B A4B |
| LMCA | 29.7% | 14.5% | Gemma 4 26B A4B |
| LMArena Text | 1434 | 1288 | Gemma 4 26B A4B |
| LMArena Text: instruction following | 1420 | 1236 | Gemma 4 26B A4B |
| LMArena Text: creative writing | 1400 | 1201 | Gemma 4 26B A4B |
| LMArena Vision | 1260 | Not measured | Only Gemma 4 26B A4B measured |
| LMArena Text: English | 1444 | 1303 | Gemma 4 26B A4B |
| LMArena Text: non-English | 1421 | 1269 | Gemma 4 26B A4B |
| LMArena Text: Chinese | 1495 | 1315 | Gemma 4 26B A4B |
| LMArena Text: German | Not measured | 1255 | Only gpt-oss-20b measured |
| LMArena Text: Japanese | Not measured | 1244 | Only gpt-oss-20b measured |
| LMArena Text: Korean | Not measured | 1236 | Only gpt-oss-20b measured |
| LMArena Text: French | 1460 | Not measured | Only Gemma 4 26B A4B measured |
| LMArena Text: Spanish | 1415 | 1267 | Gemma 4 26B A4B |
| LMArena Text: Russian | 1434 | 1278 | Gemma 4 26B A4B |
| LMArena Text: Polish | Not measured | 1252 | Only gpt-oss-20b measured |
| LMArena Text: multi-turn | 1441 | 1268 | Gemma 4 26B A4B |
| LMArena Text: longer queries | 1427 | 1250 | Gemma 4 26B A4B |
| LMArena Text: writing and language | 1405 | 1238 | Gemma 4 26B A4B |
| LMArena Text: entertainment and media | 1403 | 1216 | 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 17 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemma 4 26B A4B | gpt-oss-20b | 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
gpt-oss-20b costs less
Repository review
50K input + 3K output tokens
gpt-oss-20b costs less
Agent loop
200K input + 10K output tokens
gpt-oss-20b 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 | OpenAI gpt-oss-20b |
|---|---|---|
| Maker | OpenAI | |
| Weights | Open weights | Open weights |
| Released | April 3, 2026 | August 5, 2025 |
| Context window | 262K | 131K |
| Input $/Mtok | $0.15 | $0.03 |
| Output $/Mtok | $0.60 | $0.15 |
| 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 gpt-oss-20b for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Gemma 4 26B A4B for a larger context window (262K against 131K tokens) and work measured by WeirdML or τ²-Bench Telecom, where it leads.
Which is cheaper, Gemma 4 26B A4B or gpt-oss-20b?
Gemma 4 26B A4B costs $0.15 per million input tokens and $0.60 per million output tokens; gpt-oss-20b costs $0.03 and $0.15. Cost per solved task is known for neither of them.
Which has the bigger context window, Gemma 4 26B A4B or gpt-oss-20b?
Gemma 4 26B A4B, with 262K tokens against 131K.
How many benchmarks is the Gemma 4 26B A4B vs gpt-oss-20b comparison based on?
38 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 gpt-oss-20b's on 0. Where only one model has a result, the page says so instead of naming a winner.