Trinity Large Thinking vs gpt-oss-120b
gpt-oss-120b leads Trinity Large Thinking on the Artificial Analysis Intelligence Index, 11.6 to 10.8, and gpt-oss-120b costs 1.6x less per output token ($0.50 vs $0.80 per million). Updated October 22, 2026; 47 benchmarks have a result for both.
Trinity Large Thinking or gpt-oss-120b: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Trinity Large Thinking for a larger context window (262K against 131K tokens) and work measured by AA Coding Index or Terminal-Bench Hard (Artificial Analysis run), where it leads.
- Shared results
- 47
- Trinity Large Thinking only
- 5
- gpt-oss-120b only
- 34
- Like-for-like categories
- 8 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
gpt-oss-120b
gpt-oss-120b leads, 11.6 against 10.8.
Same board, both measured
Agentic work
AA Agentic Index
gpt-oss-120b
gpt-oss-120b leads, 3.7 against 1.0.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only gpt-oss-120b has a GPQA Diamond result, 78.5%.
Directional only
Math
AIME
No call
Only gpt-oss-120b has a AIME result, 92.6%.
Directional only
Knowledge
MMLU Pro
No call
Only gpt-oss-120b has a MMLU Pro result, 79.2%.
Directional only
Web design
Design Arena: websites
Trinity Large Thinking
Trinity Large Thinking leads, 1137 against 967.
Same board, both measured
Long documents
Listed context window
Trinity Large Thinking
Trinity Large Thinking takes 262K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
gpt-oss-120b
Trinity Large Thinking $0.0006, gpt-oss-120b $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only gpt-oss-120b has the number, $0.51.
Directional only
At a glance
- Coding index
- Trinity Large Thinking not benchmarked, gpt-oss-120b not benchmarked.
- Widest category
- Web design, 17.1 points apart on average over 6 shared benchmarks.
- Widest single gap
- τ²-Bench Telecom: Trinity Large Thinking 90.1% against gpt-oss-120b 65.8%, 24.3 points apart on a 0 to 100 scale.
- Output price
- Trinity Large Thinking $0.80 against gpt-oss-120b $0.50 per million output tokens, 1.6x apart.
- Context window
- Trinity Large Thinking takes 262K 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 | Trinity Large Thinking | Bars | gpt-oss-120b | Ties |
|---|---|---|---|---|
| Coding | 1 | 4 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 1 | 1 | ||
| Reasoning | 1 | 4 | ||
| Math | 0 | 2 | ||
| Knowledge | 1 | 5 | ||
| Web design | 6 | 0 | ||
| General | 6 | 14 | ||
| All shared boards | 16 | 31 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index14
| Benchmark | Trinity Large Thinking | 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 |
| LMArena WebDev | 1238 | Not measured | Only Trinity Large Thinking measured |
| AA Coding Index | 25.8 | 30.4 | gpt-oss-120b |
| LiveCodeBench (Vals AI) | Not measured | 83.2% | Only gpt-oss-120b measured |
| SWE-bench Verified (Vals AI) | Not measured | 33.6% | Only gpt-oss-120b measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 87.8% | Only gpt-oss-120b measured |
| SciCode | 40.6% | 34.0% | Trinity Large Thinking |
| Terminal-Bench Hard (Artificial Analysis run) | 22.7% | 23.5% | gpt-oss-120b |
| Terminal-Bench | Not measured | 18.7% | Only gpt-oss-120b measured |
| WeirdML | Not measured | 48.2% | Only gpt-oss-120b measured |
| ALE-Bench | Not measured | 575.6 | Only gpt-oss-120b measured |
| LMArena Text: coding | 1357 | 1379 | gpt-oss-120b |
| LMArena Text: software and IT | 1362 | 1387 | gpt-oss-120b |
| LMArena WebDev: React | 1231 | Not measured | Only Trinity Large Thinking measured |
| LMArena WebDev: HTML | 1240 | Not measured | Only Trinity Large Thinking measured |
| SciCode | Not measured | 7.7% | Only gpt-oss-120b measured |
| Neither model has a result on the other 22 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Trinity Large Thinking | gpt-oss-120b | Leader |
|---|---|---|---|
| AA Intelligence Index | 10.8 | 11.6 | gpt-oss-120b |
| Epoch Capabilities Index | Not measured | 139.8 | Only gpt-oss-120b measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | Trinity Large Thinking | gpt-oss-120b | Leader |
|---|---|---|---|
| AA Agentic Index | 1.0 | 3.7 | gpt-oss-120b |
| τ²-Bench Telecom | 90.1% | 65.8% | Trinity Large Thinking |
| APEX-Agents | Not measured | 4.4% | Only gpt-oss-120b measured |
| METR time horizons | Not measured | 56.6% | Only gpt-oss-120b measured |
| Vending-Bench 2 | Not measured | -$22 | Only gpt-oss-120b measured |
| BrowseComp-Plus | Not measured | 67.0% | Only gpt-oss-120b measured |
| Neither model has a result on the other 35 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index14
| Benchmark | Trinity Large Thinking | gpt-oss-120b | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 78.5% | Only gpt-oss-120b measured |
| GPQA Diamond (Artificial Analysis run) | 75.2% | 78.2% | gpt-oss-120b |
| Humanity's Last Exam (Artificial Analysis run) | 15.8% | 19.6% | gpt-oss-120b |
| GPQA Diamond (Epoch AI run) | Not measured | 75.8% | Only gpt-oss-120b measured |
| SimpleBench | Not measured | 22.1% | Only gpt-oss-120b measured |
| Mystery game puzzles | Not measured | 2.0% | Only gpt-oss-120b measured |
| LMArena Text: hard prompts | 1342 | 1364 | gpt-oss-120b |
| LMArena Text: expert | 1359 | 1356 | Trinity Large Thinking |
| LMArena Text: hard prompts in English | 1356 | 1368 | gpt-oss-120b |
| Kagi LLM Benchmark | Not measured | 58.6% | Only gpt-oss-120b measured |
| Neither model has a result on the other 12 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Trinity Large Thinking | 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 | Not measured | 88.9% | Only gpt-oss-120b measured |
| LMArena Text: math | 1365 | 1389 | gpt-oss-120b |
| LMArena Text: mathematical work | 1350 | 1386 | gpt-oss-120b |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index15
| Benchmark | Trinity Large Thinking | gpt-oss-120b | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 79.2% | Only gpt-oss-120b measured |
| LegalBench (Vals AI) | Not measured | 75.9% | Only gpt-oss-120b measured |
| CaseLaw (Vals AI) | 57.9% | 48.8% | Trinity Large Thinking |
| CorpFin (Vals AI) | 54.7% | 58.2% | gpt-oss-120b |
| TaxEval (Vals AI) | Not measured | 71.6% | Only gpt-oss-120b measured |
| MedQA (Vals AI) | Not measured | 91.4% | Only gpt-oss-120b measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 80.8% | Only gpt-oss-120b measured |
| LMArena Text: science | 1355 | 1382 | gpt-oss-120b |
| LMArena Text: medicine and healthcare | 1349 | 1388 | gpt-oss-120b |
| LMArena Text: legal and government | 1345 | 1355 | gpt-oss-120b |
| LMArena Text: business and finance | 1329 | 1359 | gpt-oss-120b |
| Neither model has a result on the other 7 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | Trinity Large Thinking | gpt-oss-120b | Leader |
|---|---|---|---|
| Design Arena: websites | 1137 | 967 | Trinity Large Thinking |
| Design Arena: UI components | 1042 | 924 | Trinity Large Thinking |
| Design Arena: game dev | 1078 | 992 | Trinity Large Thinking |
| Design Arena: data viz | 1098 | 987 | Trinity Large Thinking |
| Design Arena: 3D | 1081 | 901 | Trinity Large Thinking |
| Design Arena: all code categories | 1117 | 962 | Trinity Large Thinking |
| Design Arena: SVG | 1016 | Not measured | Only Trinity Large Thinking measured |
| Design Arena: ASCII art | 1041 | Not measured | Only Trinity Large Thinking measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index614
| Benchmark | Trinity Large Thinking | gpt-oss-120b | Leader |
|---|---|---|---|
| IFBench | 56.3% | 69.0% | gpt-oss-120b |
| Long context reasoning (AA-LCR) | 38.0% | 52.0% | gpt-oss-120b |
| DTBench | Not measured | 76.3% | Only gpt-oss-120b measured |
| LMCA | Not measured | 22.1% | Only gpt-oss-120b measured |
| 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 | 15.6% | 25.0% | gpt-oss-120b |
| LMArena Text | 1340 | 1366 | gpt-oss-120b |
| LMArena Text: instruction following | 1326 | 1318 | Trinity Large Thinking |
| LMArena Text: creative writing | 1308 | 1274 | Trinity Large Thinking |
| LMArena Text: English | 1356 | 1375 | gpt-oss-120b |
| LMArena Text: non-English | 1325 | 1351 | gpt-oss-120b |
| LMArena Text: Chinese | 1371 | 1385 | gpt-oss-120b |
| LMArena Text: German | 1356 | 1353 | Trinity Large Thinking |
| LMArena Text: Japanese | 1312 | 1331 | gpt-oss-120b |
| LMArena Text: Korean | 1306 | 1282 | Trinity Large Thinking |
| LMArena Text: French | 1366 | 1370 | gpt-oss-120b |
| LMArena Text: Spanish | 1327 | 1389 | gpt-oss-120b |
| LMArena Text: Russian | 1338 | 1343 | gpt-oss-120b |
| LMArena Text: Polish | 1312 | 1353 | gpt-oss-120b |
| LMArena Text: multi-turn | 1320 | 1340 | gpt-oss-120b |
| LMArena Text: longer queries | 1318 | 1319 | gpt-oss-120b |
| LMArena Text: writing and language | 1317 | 1306 | Trinity Large Thinking |
| LMArena Text: entertainment and media | 1296 | 1292 | Trinity Large Thinking |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Trinity Large Thinking | 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
gpt-oss-120b costs less
Repository review
50K input + 3K output tokens
gpt-oss-120b costs less
Agent loop
200K input + 10K output tokens
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 | Arcee AI Trinity Large Thinking | OpenAI gpt-oss-120b |
|---|---|---|
| Maker | Arcee AI | OpenAI |
| Weights | Open weights | Open weights |
| Released | April 1, 2026 | August 5, 2025 |
| Context window | 262K | 131K |
| Input $/Mtok | $0.25 | $0.10 |
| Output $/Mtok | $0.80 | $0.50 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | Not computable | $0.51 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Trinity Large Thinking better than gpt-oss-120b for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Trinity Large Thinking for a larger context window (262K against 131K tokens) and work measured by AA Coding Index or Terminal-Bench Hard (Artificial Analysis run), where it leads.
Which is cheaper, Trinity Large Thinking or gpt-oss-120b?
Trinity Large Thinking costs $0.25 per million input tokens and $0.80 per million output tokens; gpt-oss-120b costs $0.10 and $0.50. Cost per solved task is known for gpt-oss-120b only.
Which has the bigger context window, Trinity Large Thinking or gpt-oss-120b?
Trinity Large Thinking, with 262K tokens against 131K.
How many benchmarks is the Trinity Large Thinking vs gpt-oss-120b comparison based on?
47 benchmarks have a result for both models as of October 22, 2026. Trinity Large Thinking's coding index rests on 1 independent benchmark and gpt-oss-120b's on 1. Where only one model has a result, the page says so instead of naming a winner.