Gemini 3.5 Flash vs gpt-oss-20b
Updated October 21, 2026. 50 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Only Gemini 3.5 Flash has a coding score, 39.8 on the Vibe Coding Index; gpt-oss-20b has none, so the two cannot be ranked on coding.
gpt-oss-20b has the edge where output tokens cost 60.0x less.
- Shared results
- 50
- Gemini 3.5 Flash only
- 60
- gpt-oss-20b only
- 10
- Like-for-like categories
- 8 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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
Only Gemini 3.5 Flash has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Gemini 3.5 Flash
Gemini 3.5 Flash leads, 33.6 against 10.0.
Same board, both measured
Reasoning
GPQA Diamond
Gemini 3.5 Flash
Gemini 3.5 Flash leads, 92.7% against 68.9%.
Same board, both measured
Math
AIME
No call
Only gpt-oss-20b has a AIME result, 86.0%.
Directional only
Knowledge
MMLU Pro
Gemini 3.5 Flash
Gemini 3.5 Flash leads, 89.5% against 71.6%.
Same board, both measured
Web design
Design Arena: websites
Gemini 3.5 Flash
Gemini 3.5 Flash leads, 1268 against 853.
Same board, both measured
Long documents
Listed context window
Gemini 3.5 Flash
Gemini 3.5 Flash takes 1.05M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
gpt-oss-20b
Gemini 3.5 Flash $0.0060, gpt-oss-20b $0.0001 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Gemini 3.5 Flash has the number, $1.21.
Directional only
At a glance
- Coding index
- Gemini 3.5 Flash 39.8 from 4 benchmarks, gpt-oss-20b not benchmarked.
- Widest category
- Web design, 44.9 points apart on average over 2 shared benchmarks.
- Widest single gap
- Design Arena: websites: Gemini 3.5 Flash 1268 against gpt-oss-20b 853, 51.9 points apart on a 0 to 100 scale.
- Output price
- Gemini 3.5 Flash $9.00 against gpt-oss-20b $0.15 per million output tokens, 60.0x apart.
- Context window
- Gemini 3.5 Flash takes 1.05M 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 | Gemini 3.5 Flash | Bars | gpt-oss-20b | Ties |
|---|---|---|---|---|
| Coding | 7 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 7 | 0 | ||
| Math | 3 | 0 | ||
| Knowledge | 8 | 0 | ||
| Web design | 2 | 0 | ||
| General | 20 | 0 | ||
| All shared boards | 50 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index70
| Benchmark | Gemini 3.5 Flash | gpt-oss-20b | Leader |
|---|---|---|---|
| LMArena WebDev | 1499 | Not measured | Only Gemini 3.5 Flash measured |
| LiveCodeBench (Vals AI) | 87.6% | 80.4% | Gemini 3.5 Flash |
| SWE-bench Verified (Vals AI) | 78.8% | Not measured | Only Gemini 3.5 Flash measured |
| Terminal-Bench 4 (Vals AI) | 6.1% | Not measured | Only Gemini 3.5 Flash measured |
| Vibe Code Bench (Vals AI) | 48.7% | Not measured | Only Gemini 3.5 Flash measured |
| Code migration (Vals AI) | 26.8% | Not measured | Only Gemini 3.5 Flash measured |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only Gemini 3.5 Flash measured |
| Terminal-Bench 2.1 (Vals AI) | 74.2% | Not measured | Only Gemini 3.5 Flash measured |
| SkillsBench (Vals AI) | 52.7% | Not measured | Only Gemini 3.5 Flash measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 77.7% | Only gpt-oss-20b measured |
| SciCode | 53.9% | 38.9% | Gemini 3.5 Flash |
| Terminal-Bench Hard (Artificial Analysis run) | 46.2% | 10.6% | Gemini 3.5 Flash |
| Terminal-Bench | Not measured | 3.4% | Only gpt-oss-20b measured |
| WeirdML | 62.6% | 40.9% | Gemini 3.5 Flash |
| ALE-Bench | 911.0 | 566.0 | Gemini 3.5 Flash |
| LMArena Text: coding | 1493 | 1307 | Gemini 3.5 Flash |
| LMArena Text: software and IT | 1494 | 1311 | Gemini 3.5 Flash |
| LMArena WebDev: React | 1495 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena WebDev: HTML | 1522 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena WebDev: from an image | 1504 | Not measured | Only Gemini 3.5 Flash measured |
| Neither model has a result on the other 18 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Gemini 3.5 Flash | gpt-oss-20b | Leader |
|---|---|---|---|
| AA Intelligence Index | 33.6 | 10.0 | Gemini 3.5 Flash |
| Epoch Capabilities Index | 154.9 | 137.7 | Gemini 3.5 Flash |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Gemini 3.5 Flash | gpt-oss-20b | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1202 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: agents building full-stack apps | 1197 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: agents building mobile apps | 1175 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: agents building Android apps | 1190 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: agents building Godot games | 1098 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: agents building games | 1180 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: HTML slides | 1142 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: PowerPoint slides | 1179 | Not measured | Only Gemini 3.5 Flash measured |
| Finance Agent (Vals AI) | 57.9% | Not measured | Only Gemini 3.5 Flash measured |
| Tax Agent Bench (Vals AI) | 21.6% | Not measured | Only Gemini 3.5 Flash measured |
| Legal Research Bench (Vals AI) | 30.8% | Not measured | Only Gemini 3.5 Flash measured |
| Excel Modeling Benchmark (Vals AI) | 63.5% | Not measured | Only Gemini 3.5 Flash measured |
| Terminal-Bench-Science (Vals AI) | 5.7% | Not measured | Only Gemini 3.5 Flash measured |
| τ²-Bench Telecom | 95.6% | 60.2% | Gemini 3.5 Flash |
| APEX-Agents | 27.5% | Not measured | Only Gemini 3.5 Flash measured |
| Vending-Bench 2 | $5,396 | Not measured | Only Gemini 3.5 Flash measured |
| Neither model has a result on the other 23 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index70
| Benchmark | Gemini 3.5 Flash | gpt-oss-20b | Leader |
|---|---|---|---|
| GPQA Diamond | 92.7% | 68.9% | Gemini 3.5 Flash |
| MMMU Pro (Vals AI) | 88.3% | Not measured | Only Gemini 3.5 Flash measured |
| GPQA Diamond (Artificial Analysis run) | 92.2% | 68.8% | Gemini 3.5 Flash |
| Humanity's Last Exam (Artificial Analysis run) | 42.7% | 11.0% | Gemini 3.5 Flash |
| GPQA Diamond (Epoch AI run) | 92.8% | 60.8% | Gemini 3.5 Flash |
| ARC-AGI | 92.5% | Not measured | Only Gemini 3.5 Flash measured |
| ARC-AGI-2 | 72.1% | Not measured | Only Gemini 3.5 Flash measured |
| SimpleBench | 76.7% | Not measured | Only Gemini 3.5 Flash measured |
| Chess puzzles | 45.0% | Not measured | Only Gemini 3.5 Flash measured |
| Mystery game puzzles | 32.0% | Not measured | Only Gemini 3.5 Flash measured |
| EBR-bench | 4.8% | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: hard prompts | 1488 | 1274 | Gemini 3.5 Flash |
| LMArena Text: expert | 1495 | 1258 | Gemini 3.5 Flash |
| LMArena Text: hard prompts in English | 1483 | 1292 | Gemini 3.5 Flash |
| LMArena Vision: diagrams | 1323 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Vision: homework | 1330 | Not measured | Only Gemini 3.5 Flash measured |
| Neither model has a result on the other 5 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | Gemini 3.5 Flash | 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 | 95.6% | 65.3% | Gemini 3.5 Flash |
| FrontierMath, tiers 1 to 3 | 62.8% | Not measured | Only Gemini 3.5 Flash measured |
| FrontierMath, tier 4 | 26.8% | Not measured | Only Gemini 3.5 Flash measured |
| ProofBench | 31.0% | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: math | 1504 | 1317 | Gemini 3.5 Flash |
| LMArena Text: mathematical work | 1495 | 1305 | Gemini 3.5 Flash |
| Neither model has a result on the other 2 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | Gemini 3.5 Flash | gpt-oss-20b | Leader |
|---|---|---|---|
| MMLU Pro | 89.5% | 71.6% | Gemini 3.5 Flash |
| LegalBench (Vals AI) | 83.6% | 70.8% | Gemini 3.5 Flash |
| CaseLaw (Vals AI) | Not measured | 43.8% | Only gpt-oss-20b measured |
| CorpFin (Vals AI) | 64.7% | 53.1% | Gemini 3.5 Flash |
| TaxEval (Vals AI) | 74.4% | 63.7% | Gemini 3.5 Flash |
| MortgageTax (Vals AI) | 68.1% | Not measured | Only Gemini 3.5 Flash measured |
| MedQA (Vals AI) | Not measured | 82.9% | Only gpt-oss-20b measured |
| MedCode (Vals AI) | 55.8% | Not measured | Only Gemini 3.5 Flash measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 74.8% | Only gpt-oss-20b measured |
| SimpleQA Verified | 66.2% | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: science | 1495 | 1296 | Gemini 3.5 Flash |
| LMArena Text: medicine and healthcare | 1483 | 1310 | Gemini 3.5 Flash |
| LMArena Text: legal and government | 1488 | 1280 | Gemini 3.5 Flash |
| LMArena Text: business and finance | 1467 | 1284 | Gemini 3.5 Flash |
| Neither model has a result on the other 3 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Gemini 3.5 Flash | gpt-oss-20b | Leader |
|---|---|---|---|
| Design Arena: websites | 1268 | 853 | Gemini 3.5 Flash |
| Design Arena: UI components | 1273 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: game dev | 1263 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: data viz | 1236 | 933 | Gemini 3.5 Flash |
| Design Arena: 3D | 1244 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: all code categories | 1266 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: SVG | 1254 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: ASCII art | 1254 | Not measured | Only Gemini 3.5 Flash measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index200
| Benchmark | Gemini 3.5 Flash | gpt-oss-20b | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | 59.5% | Not measured | Only Gemini 3.5 Flash measured |
| SAGE (Vals AI) | 49.9% | Not measured | Only Gemini 3.5 Flash measured |
| MedScribe (Vals AI) | 76.6% | Not measured | Only Gemini 3.5 Flash measured |
| Vals Multimodal Index | 62.9% | Not measured | Only Gemini 3.5 Flash measured |
| IFBench | 76.3% | 65.1% | Gemini 3.5 Flash |
| Long context reasoning (AA-LCR) | 74.3% | 34.7% | Gemini 3.5 Flash |
| DTBench | 94.7% | 68.0% | Gemini 3.5 Flash |
| LMCA | 47.1% | 14.5% | Gemini 3.5 Flash |
| Surface Evolver Bench | 58.1% | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text | 1482 | 1288 | Gemini 3.5 Flash |
| LMArena Text: instruction following | 1467 | 1236 | Gemini 3.5 Flash |
| LMArena Text: creative writing | 1470 | 1201 | Gemini 3.5 Flash |
| LMArena Vision | 1308 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: English | 1481 | 1303 | Gemini 3.5 Flash |
| LMArena Text: non-English | 1475 | 1269 | Gemini 3.5 Flash |
| LMArena Text: Chinese | 1527 | 1315 | Gemini 3.5 Flash |
| LMArena Text: German | 1494 | 1255 | Gemini 3.5 Flash |
| LMArena Text: Japanese | 1486 | 1244 | Gemini 3.5 Flash |
| LMArena Text: Korean | 1448 | 1236 | Gemini 3.5 Flash |
| LMArena Text: French | 1490 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: Spanish | 1480 | 1267 | Gemini 3.5 Flash |
| LMArena Text: Russian | 1493 | 1278 | Gemini 3.5 Flash |
| LMArena Text: Polish | 1516 | 1252 | Gemini 3.5 Flash |
| LMArena Text: multi-turn | 1481 | 1268 | Gemini 3.5 Flash |
| LMArena Text: longer queries | 1483 | 1250 | Gemini 3.5 Flash |
| LMArena Text: writing and language | 1478 | 1238 | Gemini 3.5 Flash |
| LMArena Text: entertainment and media | 1459 | 1216 | Gemini 3.5 Flash |
| LMArena Vision: English | 1305 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Vision: Chinese | 1377 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Vision: reading text | 1317 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Vision: humor | 1321 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Vision: creative writing | 1301 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Document | 1463 | Not measured | Only Gemini 3.5 Flash measured |
| Neither model has a result on the other 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemini 3.5 Flash | 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 Gemini 3.5 Flash | OpenAI gpt-oss-20b |
|---|---|---|
| Maker | OpenAI | |
| Weights | No public weights listed | Open weights |
| Released | May 19, 2026 | August 5, 2025 |
| Context window | 1.05M | 131K |
| Input $/Mtok | $1.50 | $0.03 |
| Output $/Mtok | $9.00 | $0.15 |
| Coding index | 39.8 | Not benchmarked |
| Independent measurements behind it | 4 | 0 |
| Cost per solved task | $1.21 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Gemini 3.5 Flash better than gpt-oss-20b for coding?
Only Gemini 3.5 Flash has a coding score, 39.8 on the Vibe Coding Index; gpt-oss-20b has none, so the two cannot be ranked on coding. gpt-oss-20b has the edge where output tokens cost 60.0x less.
Which is cheaper, Gemini 3.5 Flash or gpt-oss-20b?
Gemini 3.5 Flash costs $1.50 per million input tokens and $9.00 per million output tokens; gpt-oss-20b costs $0.03 and $0.15. Cost per solved task is known for Gemini 3.5 Flash only.
Which has the bigger context window, Gemini 3.5 Flash or gpt-oss-20b?
Gemini 3.5 Flash, with 1.05M tokens against 131K.
How many benchmarks is the Gemini 3.5 Flash vs gpt-oss-20b comparison based on?
50 benchmarks have a result for both models as of October 21, 2026. Gemini 3.5 Flash's coding index rests on 4 independent benchmarks and gpt-oss-20b's on 0. Where only one model has a result, the page says so instead of naming a winner.