Updated November 13, 2026. Rank says GPT-5.2 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
GPT-5.2 has the higher public point estimate, 58.88 versus 43.24. Their conditional score ranges overlap. These ranges do not establish rank confidence. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Arcee AI
43.24/100
Estimated · Public rank #133
Conditional range 28.9–57.6
GPT-5.2: $1.75 input / $14 output
Trinity-Large-Thinking: $0.25 input / $0.90 output
GPT-5.2: 400K
Trinity-Large-Thinking: 512K
GPT-5.2: December 11, 2025
Trinity-Large-Thinking: March 10, 2026
Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.
Code generation, repair, and software-engineering tasks
GPT-5.2
GPT-5.2 has the higher public coding point estimate, 38 to 18.6, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
Trinity-Large-Thinking
Trinity-Large-Thinking has the larger documented context window.
1K fresh input + 500 output tokens
Trinity-Large-Thinking
Trinity-Large-Thinking has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Trinity-Large-Thinking
Trinity-Large-Thinking has the lower estimated token cost for this stated workload. GPT-5.2 has no published cached-input rate, so cached tokens use its listed input rate. Trinity-Large-Thinking has no published cached-input rate, so cached tokens use its listed input rate.
50K fresh input + 3K output tokens
Trinity-Large-Thinking
Trinity-Large-Thinking has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Trinity-Large-Thinking is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
Like-for-like · BenchAlign v5.8
GPT-5.2 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.
We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
4 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, reasoning, multimodal, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
| Category | GPT-5.2 | Trinity-Large-Thinking | Basis | Reading |
|---|---|---|---|---|
| Coding | 38.0Supported · #75/162 | 18.6Supported · #138/162 | Like-for-likeBenchAlign v5.8 lane · 6 vs 1 public rows | GPT-5.2 leads |
| Agentic | 43.8Supported · #59/135 | 22.7Estimated · #117/135 | Directional onlyBenchAlign v5.8 lane · 5 vs 2 public rows | Directional only |
| Reasoning | 50.6Supported · #50/160 | 30.2Estimated · #121/160 | Directional onlyBenchAlign v5.8 lane · 3 vs 0 public rows | Directional only |
| Knowledge | 59.0Supported · #49/180 | 36.9Estimated · #129/180 | Directional onlyBenchAlign v5.8 lane · 3 vs 2 public rows | Directional only |
| Instruction following | 92.6#20/157 | 28.9#140/157 | Directional onlyProvisional lane · 0 vs 1 weighted rows | Directional only |
| Multimodal | 59.4Estimated · #39/67 | Not ranked | Not comparableBenchAlign v5.8 lane · 18 vs 0 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 66.5Unranked · 3 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, reasoning, multimodal, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.
1K fresh input + 500 output tokens
Trinity-Large-Thinking has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Trinity-Large-Thinking has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Trinity-Large-Thinking has the lower modeled cost
GPT-5.2 has no published cached-input rate, so cached tokens use its listed input rate. Trinity-Large-Thinking has no published cached-input rate, so cached tokens use its listed input rate.
Enter a month of tokens to price it at both models’ listed API rates.
Standard listed rates, no batch or volume discounts.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
GPT-5.2
400K
Trinity-Large-Thinking
512K
GPT-5.2
Not sourced
Trinity-Large-Thinking
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.2
Not published
Trinity-Large-Thinking
Not published
GPT-5.2
text, image
OpenAI model page for gpt-5.2Trinity-Large-Thinking
GPT-5.2
Trinity-Large-Thinking
GPT-5.2
Not sourced
Trinity-Large-Thinking
Not sourced
GPT-5.2
Reasoning
Trinity-Large-Thinking
Reasoning
GPT-5.2
Proprietary
Trinity-Large-Thinking
Open Weight
GPT-5.2
Proprietary
Trinity-Large-Thinking
Open Weight
GPT-5.2
2025-12-11
Trinity-Large-Thinking
2026-03-10
GPT-5.2 has the higher public point estimate, 58.88 versus 43.24. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
GPT-5.2 has the higher public coding point estimate, 38 to 18.6, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
GPT-5.2 scores higher for agentic tasks on the public lane, 43.8 to 22.7. Trinity-Large-Thinking is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
For the stated presets, chat costs $0.00875 on GPT-5.2 and $0.0007 on Trinity-Large-Thinking; repository review costs $0.1295 and $0.0152; the cache-heavy agent loop costs $0.525 and $0.064. GPT-5.2 has no published cached-input rate, so cached tokens use its listed input rate. Trinity-Large-Thinking has no published cached-input rate, so cached tokens use its listed input rate.
Trinity-Large-Thinking has the larger documented context window: 512K, compared with 400K.
Estimates at 50,000 req/day · 1000 tokens/req average.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
LifeVal
Not directly comparable
BrowseComp
Not directly comparable
OSWorld-Verified
Not directly comparable
Gert Labs
Shared sourceGPT-5.2 leads this result
JobBench
Not directly comparable
τ²-bench Airline
Not directly comparable
SWE-bench Verified
Not directly comparable
SWE-bench Pro
Not directly comparable
Vibe Code Bench
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
PostTrainBench v1
Not directly comparable
SWE-bench Verified*
Not directly comparable
MMMU
Not directly comparable
MMMU-Pro
Not directly comparable
RealWorldQA
Not directly comparable
OmniDocBench 1.5
Not directly comparable
Video-MME (with subtitle)
Not directly comparable
Video-MME (w/o subtitle)
Not directly comparable
MathVision
Not directly comparable
We-Math
Not directly comparable
DynaMath
Not directly comparable
SimpleVQA
Not directly comparable
CC-OCR
Not directly comparable
AI2D_TEST
Not directly comparable
CountBench
Not directly comparable
ERQA
Not directly comparable
VideoMMMU
Not directly comparable
MLVU (M-Avg)
Not directly comparable
CharXiv
Not directly comparable
V*
Not directly comparable
GPQA
Not directly comparable
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
GPQA-D
Not directly comparable
MMLU-Pro (Arcee)
Not directly comparable
IFBench
Not directly comparable
AIME 2025
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Not directly comparable
FrontierMath v2 (Tier 4)
Not directly comparable
AIME25 (Arcee)
Not directly comparable
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Last updated November 13, 2026
