Gemini 3.7 Flash vs gpt-oss-120b
Updated October 21, 2026. 54 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
vs boards won of 54 shared
Decision reading
Pick Gemini 3.7 Flash for coding: it scores 69.9 on the Vibe Coding Index against 34.5 for gpt-oss-120b.
Pick gpt-oss-120b when output tokens cost 6.3x less, or when a solved task costs $0.51 against $1.78.
Model AGemini 3.7 FlashGoogle69.9/1002 independent benchmarks$1.78 per solved taskModel Bgpt-oss-120bOpenAI34.5/1002 independent benchmarks$0.51 per solved task
- Shared results
- 54
- Gemini 3.7 Flash only
- 58
- gpt-oss-120b only
- 24
- 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, four public coding benchmarks
Gemini 3.7 Flash
Gemini 3.7 Flash scores 69.9 against 34.5.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
Gemini 3.7 Flash
Gemini 3.7 Flash leads, 39.6 against 11.6.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only gpt-oss-120b has a AA Agentic Index result, 3.7.
Directional only
Reasoning
GPQA Diamond
Gemini 3.7 Flash
Gemini 3.7 Flash leads, 93.9% against 78.5%.
Same board, both measured
Math
AIME
No call
Only gpt-oss-120b has a AIME result, 92.6%.
Directional only
Knowledge
MMLU Pro
Gemini 3.7 Flash
Gemini 3.7 Flash leads, 90.1% against 79.2%.
Same board, both measured
Web design
Design Arena: websites
Gemini 3.7 Flash
Gemini 3.7 Flash leads, 1304 against 968.
Same board, both measured
Long documents
Listed context window
Gemini 3.7 Flash
Gemini 3.7 Flash takes 1.05M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
gpt-oss-120b
Gemini 3.7 Flash $0.0026, gpt-oss-120b $0.0004 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
gpt-oss-120b
Gemini 3.7 Flash $1.78, gpt-oss-120b $0.51.
Published attempt cost and pass rate
At a glance
- Coding index
- Gemini 3.7 Flash 69.9 from 2 benchmarks, gpt-oss-120b 34.5 from 2 benchmarks.
- Widest category
- Agentic work, 63.4 points apart on average over 1 shared benchmark.
- Widest single gap
- APEX-Agents: Gemini 3.7 Flash 67.8% against gpt-oss-120b 4.4%, 63.4 points apart on a 0 to 100 scale.
- Output price
- Gemini 3.7 Flash $3.75 against gpt-oss-120b $0.60 per million output tokens, 6.3x apart.
- Context window
- Gemini 3.7 Flash takes 1.05M tokens, gpt-oss-120b 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 3.7 Flash | Bars | gpt-oss-120b | Ties |
|---|---|---|---|---|
| Coding | 7 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 8 | 0 | ||
| Math | 3 | 0 | ||
| Knowledge | 7 | 0 | ||
| Web design | 6 | 0 | ||
| General | 20 | 0 | ||
| All shared boards | 54 | 0 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index70
| Benchmark | Gemini 3.7 Flash | gpt-oss-120b | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 41.8% | Only gpt-oss-120b measured |
| SWE-bench Verified | Not measured | 26.0% | Only gpt-oss-120b measured |
| LiveBench coding | 80.5% | Not measured | Only Gemini 3.7 Flash measured |
| LMArena WebDev | 1591 | Not measured | Only Gemini 3.7 Flash measured |
| AA Coding Index | 71.5 | 30.4 | Gemini 3.7 Flash |
| LiveCodeBench (Vals AI) | 88.7% | 83.2% | Gemini 3.7 Flash |
| SWE-bench Verified (Vals AI) | 80.8% | 33.6% | Gemini 3.7 Flash |
| Terminal-Bench 4 (Vals AI) | 12.1% | Not measured | Only Gemini 3.7 Flash measured |
| Vibe Code Bench (Vals AI) | 70.4% | Not measured | Only Gemini 3.7 Flash measured |
| Code migration (Vals AI) | 34.8% | Not measured | Only Gemini 3.7 Flash measured |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only Gemini 3.7 Flash measured |
| IOI (Vals AI) | 67.8% | Not measured | Only Gemini 3.7 Flash measured |
| Terminal-Bench 2.1 (Vals AI) | 77.5% | Not measured | Only Gemini 3.7 Flash measured |
| SkillsBench (Vals AI) | 65.9% | Not measured | Only Gemini 3.7 Flash measured |
| CyberBench (Vals AI) | 43.8% | Not measured | Only Gemini 3.7 Flash measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 87.8% | Only gpt-oss-120b measured |
| SciCode | 59.8% | 34.0% | Gemini 3.7 Flash |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 23.5% | Only gpt-oss-120b measured |
| Terminal-Bench | Not measured | 18.7% | Only gpt-oss-120b measured |
| FrontierCode | 43.6% | Not measured | Only Gemini 3.7 Flash measured |
| FrontierSWE | 20.3% | Not measured | Only Gemini 3.7 Flash measured |
| WeirdML | Not measured | 48.2% | Only gpt-oss-120b measured |
| ALE-Bench | 904.3 | 575.6 | Gemini 3.7 Flash |
| LMArena Text: coding | 1499 | 1379 | Gemini 3.7 Flash |
| LMArena Text: software and IT | 1498 | 1387 | Gemini 3.7 Flash |
| LMArena WebDev: React | 1587 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena WebDev: HTML | 1593 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena WebDev: from an image | 1599 | Not measured | Only Gemini 3.7 Flash measured |
| Neither model has a result on the other 10 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Gemini 3.7 Flash | gpt-oss-120b | Leader |
|---|---|---|---|
| AA Intelligence Index | 39.6 | 11.6 | Gemini 3.7 Flash |
| Epoch Capabilities Index | 158.0 | 139.8 | Gemini 3.7 Flash |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Gemini 3.7 Flash | gpt-oss-120b | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 3.7 | Only gpt-oss-120b measured |
| Design Arena: agents building web apps | 1224 | Not measured | Only Gemini 3.7 Flash measured |
| Design Arena: agents building full-stack apps | 1208 | Not measured | Only Gemini 3.7 Flash measured |
| Design Arena: agents building mobile apps | 1230 | Not measured | Only Gemini 3.7 Flash measured |
| Design Arena: agents building Android apps | 1247 | Not measured | Only Gemini 3.7 Flash measured |
| Design Arena: agents building Godot games | 1264 | Not measured | Only Gemini 3.7 Flash measured |
| Design Arena: agents building games | 1235 | Not measured | Only Gemini 3.7 Flash measured |
| Design Arena: spreadsheets | 1028 | Not measured | Only Gemini 3.7 Flash measured |
| LiveBench research | 76.5% | Not measured | Only Gemini 3.7 Flash measured |
| LiveBench browser | 56.0% | Not measured | Only Gemini 3.7 Flash measured |
| LiveBench long agentic | 28.3% | Not measured | Only Gemini 3.7 Flash measured |
| SREBench (Vals AI) | 4.6% | Not measured | Only Gemini 3.7 Flash measured |
| Finance Agent (Vals AI) | 59.0% | Not measured | Only Gemini 3.7 Flash measured |
| Tax Agent Bench (Vals AI) | 22.8% | Not measured | Only Gemini 3.7 Flash measured |
| Legal Research Bench (Vals AI) | 34.6% | Not measured | Only Gemini 3.7 Flash measured |
| Excel Modeling Benchmark (Vals AI) | 71.3% | Not measured | Only Gemini 3.7 Flash measured |
| Terminal-Bench-Science (Vals AI) | 7.1% | Not measured | Only Gemini 3.7 Flash measured |
| τ²-Bench Telecom | Not measured | 65.8% | Only gpt-oss-120b measured |
| APEX-Agents | 67.8% | 4.4% | Gemini 3.7 Flash |
| METR time horizons | Not measured | 56.6% | Only gpt-oss-120b measured |
| Remote Labor Index | 5.0% | Not measured | Only Gemini 3.7 Flash measured |
| Vending-Bench 2 | Not measured | -$22 | Only gpt-oss-120b measured |
| Neither model has a result on the other 17 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | Gemini 3.7 Flash | gpt-oss-120b | Leader |
|---|---|---|---|
| GPQA Diamond | 93.9% | 78.5% | Gemini 3.7 Flash |
| LiveBench reasoning | 87.8% | Not measured | Only Gemini 3.7 Flash measured |
| MMMU Pro (Vals AI) | 89.0% | Not measured | Only Gemini 3.7 Flash measured |
| GPQA Diamond (Artificial Analysis run) | 94.5% | 78.2% | Gemini 3.7 Flash |
| Humanity's Last Exam (Artificial Analysis run) | 47.9% | 19.6% | Gemini 3.7 Flash |
| GPQA Diamond (Epoch AI run) | 94.8% | 75.8% | Gemini 3.7 Flash |
| ARC-AGI | 95.5% | Not measured | Only Gemini 3.7 Flash measured |
| ARC-AGI-2 | 84.6% | Not measured | Only Gemini 3.7 Flash measured |
| SimpleBench | Not measured | 22.1% | Only gpt-oss-120b measured |
| Chess puzzles | 66.0% | Not measured | Only Gemini 3.7 Flash measured |
| Mystery game puzzles | 37.0% | 2.0% | Gemini 3.7 Flash |
| LMArena Text: hard prompts | 1495 | 1364 | Gemini 3.7 Flash |
| LMArena Text: expert | 1512 | 1356 | Gemini 3.7 Flash |
| LMArena Text: hard prompts in English | 1487 | 1368 | Gemini 3.7 Flash |
| LMArena Vision: diagrams | 1329 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Vision: homework | 1320 | Not measured | Only Gemini 3.7 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 3.7 Flash | gpt-oss-120b | Leader |
|---|---|---|---|
| AIME | Not measured | 92.6% | Only gpt-oss-120b measured |
| MATH 500 (Vals AI) | Not measured | 94.8% | Only gpt-oss-120b measured |
| MGSM (Vals AI) | Not measured | 92.0% | Only gpt-oss-120b measured |
| AA Math Index | Not measured | 93.4 | Only gpt-oss-120b measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 93.4% | Only gpt-oss-120b measured |
| OTIS Mock AIME | 97.2% | 88.9% | Gemini 3.7 Flash |
| FrontierMath, tiers 1 to 3 | 71.6% | Not measured | Only Gemini 3.7 Flash measured |
| FrontierMath, tier 4 | 36.6% | Not measured | Only Gemini 3.7 Flash measured |
| ProofBench | 58.0% | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: math | 1511 | 1389 | Gemini 3.7 Flash |
| LMArena Text: mathematical work | 1502 | 1386 | Gemini 3.7 Flash |
| Neither model has a result on the other 2 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index70
| Benchmark | Gemini 3.7 Flash | gpt-oss-120b | Leader |
|---|---|---|---|
| MMLU Pro | 90.1% | 79.2% | Gemini 3.7 Flash |
| LegalBench (Vals AI) | 87.3% | 75.9% | Gemini 3.7 Flash |
| CaseLaw (Vals AI) | Not measured | 48.8% | Only gpt-oss-120b measured |
| CorpFin (Vals AI) | Not measured | 58.2% | Only gpt-oss-120b measured |
| TaxEval (Vals AI) | 74.7% | 71.6% | Gemini 3.7 Flash |
| MortgageTax (Vals AI) | 66.7% | Not measured | Only Gemini 3.7 Flash measured |
| MedQA (Vals AI) | Not measured | 91.4% | Only gpt-oss-120b measured |
| MedCode (Vals AI) | 53.4% | Not measured | Only Gemini 3.7 Flash measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 80.8% | Only gpt-oss-120b measured |
| SimpleQA Verified | 69.2% | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: science | 1511 | 1382 | Gemini 3.7 Flash |
| LMArena Text: medicine and healthcare | 1494 | 1388 | Gemini 3.7 Flash |
| LMArena Text: legal and government | 1496 | 1355 | Gemini 3.7 Flash |
| LMArena Text: business and finance | 1479 | 1359 | Gemini 3.7 Flash |
| Neither model has a result on the other 3 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | Gemini 3.7 Flash | gpt-oss-120b | Leader |
|---|---|---|---|
| Design Arena: websites | 1304 | 968 | Gemini 3.7 Flash |
| Design Arena: UI components | 1299 | 927 | Gemini 3.7 Flash |
| Design Arena: game dev | 1318 | 992 | Gemini 3.7 Flash |
| Design Arena: data viz | 1315 | 987 | Gemini 3.7 Flash |
| Design Arena: 3D | 1308 | 901 | Gemini 3.7 Flash |
| Design Arena: all code categories | 1308 | 963 | Gemini 3.7 Flash |
| Design Arena: SVG | 1283 | Not measured | Only Gemini 3.7 Flash measured |
| Design Arena: ASCII art | 1276 | Not measured | Only Gemini 3.7 Flash measured |
| LiveBench design | 73.2% | Not measured | Only Gemini 3.7 Flash measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index200
| Benchmark | Gemini 3.7 Flash | gpt-oss-120b | Leader |
|---|---|---|---|
| LiveBench data analysis | 68.0% | Not measured | Only Gemini 3.7 Flash measured |
| SAGE (Vals AI) | 49.2% | Not measured | Only Gemini 3.7 Flash measured |
| MedScribe (Vals AI) | 83.9% | Not measured | Only Gemini 3.7 Flash measured |
| IFBench | Not measured | 69.0% | Only gpt-oss-120b measured |
| Long context reasoning (AA-LCR) | 83.0% | 52.0% | Gemini 3.7 Flash |
| DTBench | 96.8% | 76.3% | Gemini 3.7 Flash |
| LMCA | 50.4% | 22.1% | Gemini 3.7 Flash |
| Fiction.LiveBench | Not measured | 44.4% | Only gpt-oss-120b measured |
| Creative writing (Lech Mazur) | Not measured | 77.1% | Only gpt-oss-120b measured |
| Surface Evolver Bench | Not measured | 25.0% | Only gpt-oss-120b measured |
| Furniture assembly | 26.7% | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text | 1489 | 1366 | Gemini 3.7 Flash |
| LMArena Text: instruction following | 1484 | 1318 | Gemini 3.7 Flash |
| LMArena Text: creative writing | 1488 | 1274 | Gemini 3.7 Flash |
| LMArena Vision | 1314 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: English | 1483 | 1375 | Gemini 3.7 Flash |
| LMArena Text: non-English | 1487 | 1351 | Gemini 3.7 Flash |
| LMArena Text: Chinese | 1552 | 1385 | Gemini 3.7 Flash |
| LMArena Text: German | 1506 | 1353 | Gemini 3.7 Flash |
| LMArena Text: Japanese | 1511 | 1331 | Gemini 3.7 Flash |
| LMArena Text: Korean | 1481 | 1282 | Gemini 3.7 Flash |
| LMArena Text: French | 1500 | 1370 | Gemini 3.7 Flash |
| LMArena Text: Spanish | 1495 | 1389 | Gemini 3.7 Flash |
| LMArena Text: Russian | 1515 | 1343 | Gemini 3.7 Flash |
| LMArena Text: Polish | 1503 | 1353 | Gemini 3.7 Flash |
| LMArena Text: multi-turn | 1485 | 1340 | Gemini 3.7 Flash |
| LMArena Text: longer queries | 1492 | 1319 | Gemini 3.7 Flash |
| LMArena Text: writing and language | 1491 | 1306 | Gemini 3.7 Flash |
| LMArena Text: entertainment and media | 1477 | 1292 | Gemini 3.7 Flash |
| LMArena Vision: English | 1309 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Vision: Chinese | 1394 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Vision: reading text | 1322 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Vision: humor | 1299 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Vision: creative writing | 1312 | Not measured | Only Gemini 3.7 Flash measured |
| Neither model has a result on the other 12 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemini 3.7 Flash | gpt-oss-120b | 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
Gemini 3.7 Flash$0.0026Fits in one request
gpt-oss-120b$0.0004Fits in one request
gpt-oss-120b costs less
Repository review
50K input + 3K output tokens
Gemini 3.7 Flash$0.049Fits in one request
gpt-oss-120b$0.0093Fits in one request
gpt-oss-120b costs less
Agent loop
200K input + 10K output tokens
Gemini 3.7 Flash$0.188Fits in one request
gpt-oss-120b$0.036Exceeds the context window
gpt-oss-120b 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 3.7 Flash | OpenAI gpt-oss-120b |
|---|---|---|
| Maker | OpenAI | |
| Weights | No public weights listed | Open weights |
| Released | August 13, 2026 | August 5, 2025 |
| Context window | 1.05M | 131K |
| Input $/Mtok | $0.75 | $0.15 |
| Output $/Mtok | $3.75 | $0.60 |
| Coding index | 69.9 | 34.5 |
| Independent measurements behind it | 2 | 2 |
| Cost per solved task | $1.78 | $0.51 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Gemini 3.7 Flash better than gpt-oss-120b for coding?
Pick Gemini 3.7 Flash for coding: it scores 69.9 on the Vibe Coding Index against 34.5 for gpt-oss-120b. Pick gpt-oss-120b when output tokens cost 6.3x less, or when a solved task costs $0.51 against $1.78.
Which is cheaper, Gemini 3.7 Flash or gpt-oss-120b?
Gemini 3.7 Flash costs $0.75 per million input tokens and $3.75 per million output tokens; gpt-oss-120b costs $0.15 and $0.60. Per solved SWE-bench Verified task, Gemini 3.7 Flash costs $1.78 and gpt-oss-120b $0.51.
Which has the bigger context window, Gemini 3.7 Flash or gpt-oss-120b?
Gemini 3.7 Flash, with 1.05M tokens against 131K.
How many benchmarks is the Gemini 3.7 Flash vs gpt-oss-120b comparison based on?
54 benchmarks have a result for both models as of October 21, 2026. Gemini 3.7 Flash's coding index rests on 2 independent benchmarks and gpt-oss-120b's on 2. Where only one model has a result, the page says so instead of naming a winner.