Trinity Large Thinking vs GPT-5.2-Codex
GPT-5.2-Codex leads Trinity Large Thinking on the Artificial Analysis Intelligence Index, 28.5 to 10.8, and Trinity Large Thinking costs 17.5x less per output token ($0.80 vs $14.00 per million). Updated October 22, 2026; 10 benchmarks have a result for both.
Trinity Large Thinking or GPT-5.2-Codex: which to pick
Only GPT-5.2-Codex has a coding score, 45.4 on the Vibe Coding Index; Trinity Large Thinking has none, so the two cannot be ranked on coding.
Pick Trinity Large Thinking for output tokens 17.5x cheaper ($0.80 vs $14.00 per million).
- Shared results
- 10
- Trinity Large Thinking only
- 42
- GPT-5.2-Codex only
- 12
- Like-for-like categories
- 5 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
Only GPT-5.2-Codex has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GPT-5.2-Codex
GPT-5.2-Codex leads, 28.5 against 10.8.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Trinity Large Thinking has a AA Agentic Index result, 1.0.
Directional only
Web design
Design Arena: websites
No call
Only Trinity Large Thinking has a Design Arena: websites result, 1137.
Directional only
Long documents
Listed context window
GPT-5.2-Codex
GPT-5.2-Codex takes 400K tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Trinity Large Thinking
Trinity Large Thinking $0.0006, GPT-5.2-Codex $0.0088 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.2-Codex has the number, $0.43.
Directional only
At a glance
- Coding index
- Trinity Large Thinking not benchmarked, GPT-5.2-Codex 45.4 from 3 benchmarks.
- Widest category
- General, 32.8 points apart on average over 2 shared benchmarks.
- Widest single gap
- Long context reasoning (AA-LCR): Trinity Large Thinking 38.0% against GPT-5.2-Codex 82.3%, 44.3 points apart on a 0 to 100 scale.
- Output price
- Trinity Large Thinking $0.80 against GPT-5.2-Codex $14.00 per million output tokens, 17.5x apart.
- Context window
- Trinity Large Thinking takes 262K tokens, GPT-5.2-Codex 400K.
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-5.2-Codex | Ties |
|---|---|---|---|---|
| Coding | 0 | 4 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| General | 0 | 2 | ||
| All shared boards | 0 | 10 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
| Benchmark | Trinity Large Thinking | GPT-5.2-Codex | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 72.8% | Only GPT-5.2-Codex measured |
| SWE-bench Multilingual | Not measured | 66.3% | Only GPT-5.2-Codex measured |
| LMArena WebDev | 1238 | 1339 | GPT-5.2-Codex |
| AA Coding Index | 25.8 | Not measured | Only Trinity Large Thinking measured |
| LiveCodeBench (Vals AI) | Not measured | 88.0% | Only GPT-5.2-Codex measured |
| SWE-bench Verified (Vals AI) | Not measured | 72.4% | Only GPT-5.2-Codex measured |
| Vibe Code Bench (Vals AI) | Not measured | 37.9% | Only GPT-5.2-Codex measured |
| SciCode | 40.6% | Not measured | Only Trinity Large Thinking measured |
| Terminal-Bench Hard (Artificial Analysis run) | 22.7% | 37.1% | GPT-5.2-Codex |
| Terminal-Bench | Not measured | 66.5% | Only GPT-5.2-Codex measured |
| ALE-Bench | Not measured | 1299.9 | Only GPT-5.2-Codex measured |
| LMArena Text: coding | 1357 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: software and IT | 1362 | Not measured | Only Trinity Large Thinking measured |
| LMArena WebDev: React | 1231 | 1329 | GPT-5.2-Codex |
| LMArena WebDev: HTML | 1240 | 1348 | GPT-5.2-Codex |
| Neither model has a result on the other 24 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Trinity Large Thinking | GPT-5.2-Codex | Leader |
|---|---|---|---|
| AA Intelligence Index | 10.8 | 28.5 | GPT-5.2-Codex |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Trinity Large Thinking | GPT-5.2-Codex | Leader |
|---|---|---|---|
| AA Agentic Index | 1.0 | Not measured | Only Trinity Large Thinking measured |
| Design Arena: agents building web apps | Not measured | 1051 | Only GPT-5.2-Codex measured |
| Design Arena: agents building full-stack apps | Not measured | 993 | Only GPT-5.2-Codex measured |
| Design Arena: agents building mobile apps | Not measured | 1090 | Only GPT-5.2-Codex measured |
| Design Arena: agents building Android apps | Not measured | 1176 | Only GPT-5.2-Codex measured |
| Design Arena: agents building Godot games | Not measured | 1142 | Only GPT-5.2-Codex measured |
| τ²-Bench Telecom | 90.1% | 92.1% | GPT-5.2-Codex |
| Neither model has a result on the other 34 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Trinity Large Thinking | GPT-5.2-Codex | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 75.2% | 89.9% | GPT-5.2-Codex |
| Humanity's Last Exam (Artificial Analysis run) | 15.8% | 35.7% | GPT-5.2-Codex |
| LMArena Text: hard prompts | 1342 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: expert | 1359 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: hard prompts in English | 1356 | Not measured | Only Trinity Large Thinking measured |
| Neither model has a result on the other 17 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Trinity Large Thinking | GPT-5.2-Codex | Leader |
|---|---|---|---|
| LMArena Text: math | 1365 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: mathematical work | 1350 | Not measured | Only Trinity Large Thinking measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Trinity Large Thinking | GPT-5.2-Codex | Leader |
|---|---|---|---|
| CaseLaw (Vals AI) | 57.9% | Not measured | Only Trinity Large Thinking measured |
| CorpFin (Vals AI) | 54.7% | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: science | 1355 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: medicine and healthcare | 1349 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: legal and government | 1345 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: business and finance | 1329 | Not measured | Only Trinity Large Thinking measured |
| Neither model has a result on the other 12 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Trinity Large Thinking | GPT-5.2-Codex | Leader |
|---|---|---|---|
| Design Arena: websites | 1137 | Not measured | Only Trinity Large Thinking measured |
| Design Arena: UI components | 1042 | Not measured | Only Trinity Large Thinking measured |
| Design Arena: game dev | 1078 | Not measured | Only Trinity Large Thinking measured |
| Design Arena: data viz | 1098 | Not measured | Only Trinity Large Thinking measured |
| Design Arena: 3D | 1081 | Not measured | Only Trinity Large Thinking measured |
| Design Arena: all code categories | 1117 | Not measured | Only Trinity Large Thinking measured |
| 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 index02
| Benchmark | Trinity Large Thinking | GPT-5.2-Codex | Leader |
|---|---|---|---|
| IFBench | 56.3% | 77.6% | GPT-5.2-Codex |
| Long context reasoning (AA-LCR) | 38.0% | 82.3% | GPT-5.2-Codex |
| Surface Evolver Bench | 15.6% | Not measured | Only Trinity Large Thinking measured |
| LMArena Text | 1340 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: instruction following | 1326 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: creative writing | 1308 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: English | 1356 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: non-English | 1325 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: Chinese | 1371 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: German | 1356 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: Japanese | 1312 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: Korean | 1306 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: French | 1366 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: Spanish | 1327 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: Russian | 1338 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: Polish | 1312 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: multi-turn | 1320 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: longer queries | 1318 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: writing and language | 1317 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: entertainment and media | 1296 | Not measured | Only Trinity Large Thinking measured |
| Neither model has a result on the other 26 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Trinity Large Thinking | GPT-5.2-Codex | 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
Trinity Large Thinking costs less
Repository review
50K input + 3K output tokens
Trinity Large Thinking costs less
Agent loop
200K input + 10K output tokens
Trinity Large Thinking 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-5.2-Codex |
|---|---|---|
| Maker | Arcee AI | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | April 1, 2026 | January 14, 2026 |
| Context window | 262K | 400K |
| Input $/Mtok | $0.25 | $1.75 |
| Output $/Mtok | $0.80 | $14.00 |
| Coding index | Not benchmarked | 45.4 |
| Independent measurements behind it | 1 | 3 |
| Cost per solved task | Not computable | $0.43 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Trinity Large Thinking better than GPT-5.2-Codex for coding?
Only GPT-5.2-Codex has a coding score, 45.4 on the Vibe Coding Index; Trinity Large Thinking has none, so the two cannot be ranked on coding. Pick Trinity Large Thinking for output tokens 17.5x cheaper ($0.80 vs $14.00 per million).
Which is cheaper, Trinity Large Thinking or GPT-5.2-Codex?
Trinity Large Thinking costs $0.25 per million input tokens and $0.80 per million output tokens; GPT-5.2-Codex costs $1.75 and $14.00. Cost per solved task is known for GPT-5.2-Codex only.
Which has the bigger context window, Trinity Large Thinking or GPT-5.2-Codex?
GPT-5.2-Codex, with 400K tokens against 262K.
How many benchmarks is the Trinity Large Thinking vs GPT-5.2-Codex comparison based on?
10 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-5.2-Codex's on 3. Where only one model has a result, the page says so instead of naming a winner.