Updated November 13, 2026. Rank says GPT-5.4 mini is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead. 0 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-5.4 mini: $0.75 input / $4.50 output
Trinity-Large-Thinking: $0.25 input / $0.90 output
GPT-5.4 mini: 400K
Trinity-Large-Thinking: 512K
GPT-5.4 mini: March 17, 2026
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-5.4 mini
GPT-5.4 mini has the higher public coding point estimate, 34.1 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. 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-5.4 mini 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-5.4 mini | Trinity-Large-Thinking | Basis | Reading |
|---|---|---|---|---|
| Coding | 34.1Supported · #91/162 | 18.6Supported · #138/162 | Like-for-likeBenchAlign v5.8 lane · 5 vs 1 public rows | GPT-5.4 mini leads · intervals overlap |
| Agentic | 35.4Supported · #81/135 | 22.7Estimated · #117/135 | Directional onlyBenchAlign v5.8 lane · 6 vs 2 public rows | Directional only |
| Reasoning | 30.6Supported · #113/160 | 30.2Estimated · #121/160 | Directional onlyBenchAlign v5.8 lane · 2 vs 0 public rows | Directional only |
| Knowledge | 51.5Supported · #71/180 | 36.9Estimated · #129/180 | Directional onlyBenchAlign v5.8 lane · 5 vs 2 public rows | Directional only |
| Instruction following | 89.9#30/157 | 28.9#140/157 | Directional onlyProvisional lane · 0 vs 1 weighted rows | Directional only |
| Multimodal | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 2 vs 0 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 51.9Unranked · 2 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
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-5.4 mini
Trinity-Large-Thinking
512K
GPT-5.4 mini
gpt-5.4-mini
OpenAI GPT-5.4 mini model documentationTrinity-Large-Thinking
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.4 mini
$0.075 per 1M cached input tokens
OpenAI pricingTrinity-Large-Thinking
Not published
GPT-5.4 mini
text, image
OpenAI model catalogTrinity-Large-Thinking
GPT-5.4 mini
Trinity-Large-Thinking
GPT-5.4 mini
Generally Available · OpenAI Responses API
OpenAI model catalogTrinity-Large-Thinking
Not sourced
GPT-5.4 mini
Reasoning
Trinity-Large-Thinking
Reasoning
GPT-5.4 mini
Proprietary
Trinity-Large-Thinking
Open Weight
GPT-5.4 mini
Proprietary
Trinity-Large-Thinking
Open Weight
GPT-5.4 mini
2026-03-17
Trinity-Large-Thinking
2026-03-10
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
GPT-5.4 mini has the higher public coding point estimate, 34.1 to 18.6, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
GPT-5.4 mini scores higher for agentic tasks on the public lane, 35.4 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 $0.003 on GPT-5.4 mini and $0.0007 on Trinity-Large-Thinking; repository review costs $0.051 and $0.0152; the cache-heavy agent loop costs $0.075 and $0.064. 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.
Terminal-Bench 2.0
Not directly comparable
OSWorld-Verified
Not directly comparable
MCP Atlas
Not directly comparable
Toolathlon
Not directly comparable
τ²-bench results
Not directly comparable
Terminal-Bench 2.1 (Vals)
Not directly comparable
τ²-bench Airline
Not directly comparable
Gert Labs
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
Vibe Code Bench
Not directly comparable
FrontierCode 1.1 Main
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
SWE-bench Verified*
Not directly comparable
GPQA
Not directly comparable
HLE
Not directly comparable
HLE w/o tools
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
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Last updated November 13, 2026
