Gemma 4 26B A4B vs GPT-4o (2024-08-06)
Gemma 4 26B A4B leads GPT-4o (2024-08-06) on the Artificial Analysis Intelligence Index, 16.7 to 7.7, and Gemma 4 26B A4B costs 16.7x less per output token ($0.60 vs $10.00 per million). Updated October 22, 2026; 36 benchmarks have a result for both.
Gemma 4 26B A4B or GPT-4o (2024-08-06): 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 16.7x cheaper ($0.60 vs $10.00 per million) and a larger context window (262K against 128K tokens).
- Shared results
- 36
- Gemma 4 26B A4B only
- 19
- GPT-4o (2024-08-06) only
- 22
- 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 7.7.
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
Math
AIME
No call
Only GPT-4o (2024-08-06) has a AIME result, 14.0%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-4o (2024-08-06) has a MMLU Pro result, 74.1%.
Directional only
Long documents
Listed context window
Gemma 4 26B A4B
Gemma 4 26B A4B takes 262K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Gemma 4 26B A4B
Gemma 4 26B A4B $0.0004, GPT-4o (2024-08-06) $0.0075 per turn.
List prices
At a glance
- Coding index
- Gemma 4 26B A4B not benchmarked, GPT-4o (2024-08-06) not benchmarked.
- Widest category
- Math, 46.2 points apart on average over 3 shared benchmarks.
- Widest single gap
- OTIS Mock AIME: Gemma 4 26B A4B 82.2% against GPT-4o (2024-08-06) 6.4%, 75.8 points apart on a 0 to 100 scale.
- Output price
- Gemma 4 26B A4B $0.60 against GPT-4o (2024-08-06) $10.00 per million output tokens, 16.7x apart.
- Context window
- Gemma 4 26B A4B takes 262K tokens, GPT-4o (2024-08-06) 128K.
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-4o (2024-08-06) | Ties |
|---|---|---|---|---|
| Coding | 3 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 6 | 0 | ||
| Math | 3 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 18 | 0 | ||
| All shared boards | 36 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
| Benchmark | Gemma 4 26B A4B | GPT-4o (2024-08-06) | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 23.1% | Only GPT-4o (2024-08-06) measured |
| SWE-bench Lite | Not measured | 21.7% | Only GPT-4o (2024-08-06) 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 | 31.7% | Only GPT-4o (2024-08-06) measured |
| SciCode | 40.0% | Not measured | Only Gemma 4 26B A4B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 25.0% | 8.3% | Gemma 4 26B A4B |
| 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 | 1283 | Gemma 4 26B A4B |
| LMArena Text: software and IT | 1454 | 1278 | 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 30 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Gemma 4 26B A4B | GPT-4o (2024-08-06) | Leader |
|---|---|---|---|
| AA Intelligence Index | 16.7 | 7.7 | 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 index10
| Benchmark | Gemma 4 26B A4B | GPT-4o (2024-08-06) | Leader |
|---|---|---|---|
| AA Agentic Index | 2.9 | Not measured | Only Gemma 4 26B A4B measured |
| τ²-Bench Telecom | 43.6% | 28.9% | Gemma 4 26B A4B |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | Gemma 4 26B A4B | GPT-4o (2024-08-06) | Leader |
|---|---|---|---|
| MMMU Pro (Vals AI) | Not measured | 64.0% | Only GPT-4o (2024-08-06) measured |
| GPQA Diamond (Artificial Analysis run) | 79.2% | 52.1% | Gemma 4 26B A4B |
| Humanity's Last Exam (Artificial Analysis run) | 19.3% | 2.3% | Gemma 4 26B A4B |
| GPQA Diamond (Epoch AI run) | 73.2% | 49.2% | Gemma 4 26B A4B |
| SimpleBench | Not measured | 17.8% | Only GPT-4o (2024-08-06) measured |
| Chess puzzles | Not measured | 6.0% | Only GPT-4o (2024-08-06) measured |
| LMArena Text: hard prompts | 1438 | 1264 | Gemma 4 26B A4B |
| LMArena Text: expert | 1447 | 1241 | Gemma 4 26B A4B |
| LMArena Text: hard prompts in English | 1448 | 1277 | 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 12 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | Gemma 4 26B A4B | GPT-4o (2024-08-06) | Leader |
|---|---|---|---|
| AIME | Not measured | 14.0% | Only GPT-4o (2024-08-06) measured |
| MATH 500 (Vals AI) | Not measured | 75.2% | Only GPT-4o (2024-08-06) measured |
| MGSM (Vals AI) | Not measured | 90.7% | Only GPT-4o (2024-08-06) measured |
| OTIS Mock AIME | 82.2% | 6.4% | Gemma 4 26B A4B |
| FrontierMath, tiers 1 to 3 | Not measured | 0.4% | Only GPT-4o (2024-08-06) measured |
| MATH level 5 | Not measured | 53.3% | Only GPT-4o (2024-08-06) measured |
| LMArena Text: math | 1470 | 1285 | Gemma 4 26B A4B |
| LMArena Text: mathematical work | 1475 | 1283 | Gemma 4 26B A4B |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | Gemma 4 26B A4B | GPT-4o (2024-08-06) | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 74.1% | Only GPT-4o (2024-08-06) measured |
| LegalBench (Vals AI) | Not measured | 80.1% | Only GPT-4o (2024-08-06) measured |
| CorpFin (Vals AI) | Not measured | 39.4% | Only GPT-4o (2024-08-06) measured |
| TaxEval (Vals AI) | Not measured | 71.1% | Only GPT-4o (2024-08-06) measured |
| MortgageTax (Vals AI) | Not measured | 61.0% | Only GPT-4o (2024-08-06) measured |
| MedQA (Vals AI) | Not measured | 88.2% | Only GPT-4o (2024-08-06) measured |
| SimpleQA Verified | Not measured | 26.0% | Only GPT-4o (2024-08-06) measured |
| MMLU | Not measured | 84.3% | Only GPT-4o (2024-08-06) measured |
| LMArena Text: science | 1446 | 1274 | Gemma 4 26B A4B |
| LMArena Text: medicine and healthcare | 1428 | 1252 | Gemma 4 26B A4B |
| LMArena Text: legal and government | 1457 | 1275 | Gemma 4 26B A4B |
| LMArena Text: business and finance | 1429 | 1232 | 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 7 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemma 4 26B A4B | GPT-4o (2024-08-06) | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index180
| Benchmark | Gemma 4 26B A4B | GPT-4o (2024-08-06) | Leader |
|---|---|---|---|
| IFBench | 72.4% | 36.0% | Gemma 4 26B A4B |
| Long context reasoning (AA-LCR) | 65.7% | 41.0% | Gemma 4 26B A4B |
| DTBench | 74.9% | Not measured | Only Gemma 4 26B A4B measured |
| LMCA | 29.7% | Not measured | Only Gemma 4 26B A4B measured |
| LMArena Text | 1434 | 1283 | Gemma 4 26B A4B |
| LMArena Text: instruction following | 1420 | 1267 | Gemma 4 26B A4B |
| LMArena Text: creative writing | 1400 | 1274 | Gemma 4 26B A4B |
| LMArena Vision | 1260 | 1065 | Gemma 4 26B A4B |
| LMArena Text: English | 1444 | 1297 | Gemma 4 26B A4B |
| LMArena Text: non-English | 1421 | 1262 | Gemma 4 26B A4B |
| LMArena Text: Chinese | 1495 | 1254 | Gemma 4 26B A4B |
| LMArena Text: German | Not measured | 1257 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: Japanese | Not measured | 1234 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: Korean | Not measured | 1218 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: French | 1460 | 1263 | Gemma 4 26B A4B |
| LMArena Text: Spanish | 1415 | 1269 | Gemma 4 26B A4B |
| LMArena Text: Russian | 1434 | 1272 | Gemma 4 26B A4B |
| LMArena Text: multi-turn | 1441 | 1279 | Gemma 4 26B A4B |
| LMArena Text: longer queries | 1427 | 1283 | Gemma 4 26B A4B |
| LMArena Text: writing and language | 1405 | 1290 | Gemma 4 26B A4B |
| LMArena Text: entertainment and media | 1403 | 1267 | Gemma 4 26B A4B |
| LMArena Vision: English | 1256 | 1072 | Gemma 4 26B A4B |
| LMArena Vision: Chinese | 1312 | 1009 | Gemma 4 26B A4B |
| 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 20 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemma 4 26B A4B | GPT-4o (2024-08-06) | 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 | OpenAI GPT-4o (2024-08-06) |
|---|---|---|
| Maker | OpenAI | |
| Weights | Open weights | No public weights listed |
| Released | April 3, 2026 | August 6, 2024 |
| Context window | 262K | 128K |
| Input $/Mtok | $0.15 | $2.50 |
| Output $/Mtok | $0.60 | $10.00 |
| 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 GPT-4o (2024-08-06) 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 16.7x cheaper ($0.60 vs $10.00 per million) and a larger context window (262K against 128K tokens).
Which is cheaper, Gemma 4 26B A4B or GPT-4o (2024-08-06)?
Gemma 4 26B A4B costs $0.15 per million input tokens and $0.60 per million output tokens; GPT-4o (2024-08-06) costs $2.50 and $10.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Gemma 4 26B A4B or GPT-4o (2024-08-06)?
Gemma 4 26B A4B, with 262K tokens against 128K.
How many benchmarks is the Gemma 4 26B A4B vs GPT-4o (2024-08-06) comparison based on?
36 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-4o (2024-08-06)'s on 0. Where only one model has a result, the page says so instead of naming a winner.